commit
001a46260a
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cmake_minimum_required(VERSION 3.6)
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project(Lab1)
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set(SFML_COMPONENTS graphics window system network audio)
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set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR})
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find_package(SFML 2.1 COMPONENTS ${SFML_COMPONENTS} REQUIRED)
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message(STATUS "SFML found: ${SFML_FOUND}")
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include_directories(${SFML_INCLUDE_DIR})
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
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set(SOURCE_FILES main.cpp mtrand.cpp mtrand.h)
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add_executable(Lab1 ${SOURCE_FILES})
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target_link_libraries (Lab1 ${SFML_LIBRARIES} ${SFML_DEPENDENCIES})
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# This script locates the SFML library
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# ------------------------------------
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#
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# Usage
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# -----
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#
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# When you try to locate the SFML libraries, you must specify which modules you want to use (system, window, graphics, network, audio, main).
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# If none is given, the SFML_LIBRARIES variable will be empty and you'll end up linking to nothing.
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# example:
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# find_package(SFML COMPONENTS graphics window system) # find the graphics, window and system modules
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#
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# You can enforce a specific version, either MAJOR.MINOR or only MAJOR.
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# If nothing is specified, the version won't be checked (i.e. any version will be accepted).
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# example:
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# find_package(SFML COMPONENTS ...) # no specific version required
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# find_package(SFML 2 COMPONENTS ...) # any 2.x version
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# find_package(SFML 2.4 COMPONENTS ...) # version 2.4 or greater
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#
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# By default, the dynamic libraries of SFML will be found. To find the static ones instead,
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# you must set the SFML_STATIC_LIBRARIES variable to TRUE before calling find_package(SFML ...).
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# Since you have to link yourself all the SFML dependencies when you link it statically, the following
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# additional variables are defined: SFML_XXX_DEPENDENCIES and SFML_DEPENDENCIES (see their detailed
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# description below).
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# In case of static linking, the SFML_STATIC macro will also be defined by this script.
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# example:
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# set(SFML_STATIC_LIBRARIES TRUE)
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# find_package(SFML 2 COMPONENTS network system)
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#
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# On Mac OS X if SFML_STATIC_LIBRARIES is not set to TRUE then by default CMake will search for frameworks unless
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# CMAKE_FIND_FRAMEWORK is set to "NEVER" for example. Please refer to CMake documentation for more details.
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# Moreover, keep in mind that SFML frameworks are only available as release libraries unlike dylibs which
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# are available for both release and debug modes.
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#
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# If SFML is not installed in a standard path, you can use the SFML_ROOT CMake (or environment) variable
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# to tell CMake where SFML is.
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#
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# Output
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# ------
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#
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# This script defines the following variables:
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# - For each specified module XXX (system, window, graphics, network, audio, main):
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# - SFML_XXX_LIBRARY_DEBUG: the name of the debug library of the xxx module (set to SFML_XXX_LIBRARY_RELEASE is no debug version is found)
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# - SFML_XXX_LIBRARY_RELEASE: the name of the release library of the xxx module (set to SFML_XXX_LIBRARY_DEBUG is no release version is found)
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# - SFML_XXX_LIBRARY: the name of the library to link to for the xxx module (includes both debug and optimized names if necessary)
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# - SFML_XXX_FOUND: true if either the debug or release library of the xxx module is found
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# - SFML_XXX_DEPENDENCIES: the list of libraries the module depends on, in case of static linking
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# - SFML_LIBRARIES: the list of all libraries corresponding to the required modules
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# - SFML_FOUND: true if all the required modules are found
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# - SFML_INCLUDE_DIR: the path where SFML headers are located (the directory containing the SFML/Config.hpp file)
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# - SFML_DEPENDENCIES: the list of libraries SFML depends on, in case of static linking
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#
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# example:
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# find_package(SFML 2 COMPONENTS system window graphics audio REQUIRED)
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# include_directories(${SFML_INCLUDE_DIR})
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# add_executable(myapp ...)
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# target_link_libraries(myapp ${SFML_LIBRARIES})
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# define the SFML_STATIC macro if static build was chosen
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if(SFML_STATIC_LIBRARIES)
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add_definitions(-DSFML_STATIC)
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endif()
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# define the list of search paths for headers and libraries
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set(FIND_SFML_PATHS
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${SFML_ROOT}
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$ENV{SFML_ROOT}
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~/Library/Frameworks
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/Library/Frameworks
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/usr/local
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/usr
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/sw
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/opt/local
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/opt/csw
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/opt)
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# find the SFML include directory
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find_path(SFML_INCLUDE_DIR SFML/Config.hpp
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PATH_SUFFIXES include
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PATHS ${FIND_SFML_PATHS})
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# check the version number
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set(SFML_VERSION_OK TRUE)
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if(SFML_FIND_VERSION AND SFML_INCLUDE_DIR)
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# extract the major and minor version numbers from SFML/Config.hpp
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# we have to handle framework a little bit differently:
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if("${SFML_INCLUDE_DIR}" MATCHES "SFML.framework")
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set(SFML_CONFIG_HPP_INPUT "${SFML_INCLUDE_DIR}/Headers/Config.hpp")
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else()
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set(SFML_CONFIG_HPP_INPUT "${SFML_INCLUDE_DIR}/SFML/Config.hpp")
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endif()
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FILE(READ "${SFML_CONFIG_HPP_INPUT}" SFML_CONFIG_HPP_CONTENTS)
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STRING(REGEX REPLACE ".*#define SFML_VERSION_MAJOR ([0-9]+).*" "\\1" SFML_VERSION_MAJOR "${SFML_CONFIG_HPP_CONTENTS}")
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STRING(REGEX REPLACE ".*#define SFML_VERSION_MINOR ([0-9]+).*" "\\1" SFML_VERSION_MINOR "${SFML_CONFIG_HPP_CONTENTS}")
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STRING(REGEX REPLACE ".*#define SFML_VERSION_PATCH ([0-9]+).*" "\\1" SFML_VERSION_PATCH "${SFML_CONFIG_HPP_CONTENTS}")
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if (NOT "${SFML_VERSION_PATCH}" MATCHES "^[0-9]+$")
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set(SFML_VERSION_PATCH 0)
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endif()
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math(EXPR SFML_REQUESTED_VERSION "${SFML_FIND_VERSION_MAJOR} * 10000 + ${SFML_FIND_VERSION_MINOR} * 100 + ${SFML_FIND_VERSION_PATCH}")
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# if we could extract them, compare with the requested version number
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if (SFML_VERSION_MAJOR)
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# transform version numbers to an integer
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math(EXPR SFML_VERSION "${SFML_VERSION_MAJOR} * 10000 + ${SFML_VERSION_MINOR} * 100 + ${SFML_VERSION_PATCH}")
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# compare them
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if(SFML_VERSION LESS SFML_REQUESTED_VERSION)
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set(SFML_VERSION_OK FALSE)
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endif()
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else()
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# SFML version is < 2.0
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if (SFML_REQUESTED_VERSION GREATER 10900)
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set(SFML_VERSION_OK FALSE)
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set(SFML_VERSION_MAJOR 1)
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set(SFML_VERSION_MINOR x)
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set(SFML_VERSION_PATCH x)
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endif()
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endif()
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endif()
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# find the requested modules
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set(SFML_FOUND TRUE) # will be set to false if one of the required modules is not found
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foreach(FIND_SFML_COMPONENT ${SFML_FIND_COMPONENTS})
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string(TOLOWER ${FIND_SFML_COMPONENT} FIND_SFML_COMPONENT_LOWER)
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string(TOUPPER ${FIND_SFML_COMPONENT} FIND_SFML_COMPONENT_UPPER)
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set(FIND_SFML_COMPONENT_NAME sfml-${FIND_SFML_COMPONENT_LOWER})
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# no suffix for sfml-main, it is always a static library
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if(FIND_SFML_COMPONENT_LOWER STREQUAL "main")
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# release library
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find_library(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE
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NAMES ${FIND_SFML_COMPONENT_NAME}
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PATH_SUFFIXES lib64 lib
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PATHS ${FIND_SFML_PATHS})
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# debug library
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find_library(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG
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NAMES ${FIND_SFML_COMPONENT_NAME}-d
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PATH_SUFFIXES lib64 lib
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PATHS ${FIND_SFML_PATHS})
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else()
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# static release library
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find_library(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_STATIC_RELEASE
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NAMES ${FIND_SFML_COMPONENT_NAME}-s
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PATH_SUFFIXES lib64 lib
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PATHS ${FIND_SFML_PATHS})
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# static debug library
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find_library(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_STATIC_DEBUG
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NAMES ${FIND_SFML_COMPONENT_NAME}-s-d
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PATH_SUFFIXES lib64 lib
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PATHS ${FIND_SFML_PATHS})
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# dynamic release library
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find_library(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DYNAMIC_RELEASE
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NAMES ${FIND_SFML_COMPONENT_NAME}
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PATH_SUFFIXES lib64 lib
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PATHS ${FIND_SFML_PATHS})
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||||||
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# dynamic debug library
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find_library(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DYNAMIC_DEBUG
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NAMES ${FIND_SFML_COMPONENT_NAME}-d
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PATH_SUFFIXES lib64 lib
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PATHS ${FIND_SFML_PATHS})
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||||||
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# choose the entries that fit the requested link type
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if(SFML_STATIC_LIBRARIES)
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if(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_STATIC_RELEASE)
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set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_STATIC_RELEASE})
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|
endif()
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|
if(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_STATIC_DEBUG)
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set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_STATIC_DEBUG})
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|
endif()
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|
else()
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||||||
|
if(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DYNAMIC_RELEASE)
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set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DYNAMIC_RELEASE})
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||||||
|
endif()
|
||||||
|
if(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DYNAMIC_DEBUG)
|
||||||
|
set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DYNAMIC_DEBUG})
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||||||
|
endif()
|
||||||
|
endif()
|
||||||
|
endif()
|
||||||
|
|
||||||
|
if (SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG OR SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE)
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||||||
|
# library found
|
||||||
|
set(SFML_${FIND_SFML_COMPONENT_UPPER}_FOUND TRUE)
|
||||||
|
|
||||||
|
# if both are found, set SFML_XXX_LIBRARY to contain both
|
||||||
|
if (SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG AND SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE)
|
||||||
|
set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY debug ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG}
|
||||||
|
optimized ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE})
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# if only one debug/release variant is found, set the other to be equal to the found one
|
||||||
|
if (SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG AND NOT SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE)
|
||||||
|
# debug and not release
|
||||||
|
set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG})
|
||||||
|
set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG})
|
||||||
|
endif()
|
||||||
|
if (SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE AND NOT SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG)
|
||||||
|
# release and not debug
|
||||||
|
set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE})
|
||||||
|
set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY ${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE})
|
||||||
|
endif()
|
||||||
|
else()
|
||||||
|
# library not found
|
||||||
|
set(SFML_FOUND FALSE)
|
||||||
|
set(SFML_${FIND_SFML_COMPONENT_UPPER}_FOUND FALSE)
|
||||||
|
set(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY "")
|
||||||
|
set(FIND_SFML_MISSING "${FIND_SFML_MISSING} SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY")
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# mark as advanced
|
||||||
|
MARK_AS_ADVANCED(SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY
|
||||||
|
SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_RELEASE
|
||||||
|
SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DEBUG
|
||||||
|
SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_STATIC_RELEASE
|
||||||
|
SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_STATIC_DEBUG
|
||||||
|
SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DYNAMIC_RELEASE
|
||||||
|
SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY_DYNAMIC_DEBUG)
|
||||||
|
|
||||||
|
# add to the global list of libraries
|
||||||
|
set(SFML_LIBRARIES ${SFML_LIBRARIES} "${SFML_${FIND_SFML_COMPONENT_UPPER}_LIBRARY}")
|
||||||
|
endforeach()
|
||||||
|
|
||||||
|
# in case of static linking, we must also define the list of all the dependencies of SFML libraries
|
||||||
|
if(SFML_STATIC_LIBRARIES)
|
||||||
|
|
||||||
|
# detect the OS
|
||||||
|
if(${CMAKE_SYSTEM_NAME} MATCHES "Windows")
|
||||||
|
set(FIND_SFML_OS_WINDOWS 1)
|
||||||
|
elseif(${CMAKE_SYSTEM_NAME} MATCHES "Linux")
|
||||||
|
set(FIND_SFML_OS_LINUX 1)
|
||||||
|
elseif(${CMAKE_SYSTEM_NAME} MATCHES "FreeBSD")
|
||||||
|
set(FIND_SFML_OS_FREEBSD 1)
|
||||||
|
elseif(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
|
||||||
|
set(FIND_SFML_OS_MACOSX 1)
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# start with an empty list
|
||||||
|
set(SFML_DEPENDENCIES)
|
||||||
|
set(FIND_SFML_DEPENDENCIES_NOTFOUND)
|
||||||
|
|
||||||
|
# macro that searches for a 3rd-party library
|
||||||
|
macro(find_sfml_dependency output friendlyname)
|
||||||
|
# No lookup in environment variables (PATH on Windows), as they may contain wrong library versions
|
||||||
|
find_library(${output} NAMES ${ARGN} PATHS ${FIND_SFML_PATHS} PATH_SUFFIXES lib NO_SYSTEM_ENVIRONMENT_PATH)
|
||||||
|
if(${${output}} STREQUAL "${output}-NOTFOUND")
|
||||||
|
unset(output)
|
||||||
|
set(FIND_SFML_DEPENDENCIES_NOTFOUND "${FIND_SFML_DEPENDENCIES_NOTFOUND} ${friendlyname}")
|
||||||
|
endif()
|
||||||
|
endmacro()
|
||||||
|
|
||||||
|
# sfml-system
|
||||||
|
list(FIND SFML_FIND_COMPONENTS "system" FIND_SFML_SYSTEM_COMPONENT)
|
||||||
|
if(NOT ${FIND_SFML_SYSTEM_COMPONENT} EQUAL -1)
|
||||||
|
|
||||||
|
# update the list -- these are only system libraries, no need to find them
|
||||||
|
if(FIND_SFML_OS_LINUX OR FIND_SFML_OS_FREEBSD OR FIND_SFML_OS_MACOSX)
|
||||||
|
set(SFML_SYSTEM_DEPENDENCIES "pthread")
|
||||||
|
endif()
|
||||||
|
if(FIND_SFML_OS_LINUX)
|
||||||
|
set(SFML_SYSTEM_DEPENDENCIES ${SFML_SYSTEM_DEPENDENCIES} "rt")
|
||||||
|
endif()
|
||||||
|
if(FIND_SFML_OS_WINDOWS)
|
||||||
|
set(SFML_SYSTEM_DEPENDENCIES "winmm")
|
||||||
|
endif()
|
||||||
|
set(SFML_DEPENDENCIES ${SFML_SYSTEM_DEPENDENCIES} ${SFML_DEPENDENCIES})
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# sfml-network
|
||||||
|
list(FIND SFML_FIND_COMPONENTS "network" FIND_SFML_NETWORK_COMPONENT)
|
||||||
|
if(NOT ${FIND_SFML_NETWORK_COMPONENT} EQUAL -1)
|
||||||
|
|
||||||
|
# update the list -- these are only system libraries, no need to find them
|
||||||
|
if(FIND_SFML_OS_WINDOWS)
|
||||||
|
set(SFML_NETWORK_DEPENDENCIES "ws2_32")
|
||||||
|
endif()
|
||||||
|
set(SFML_DEPENDENCIES ${SFML_NETWORK_DEPENDENCIES} ${SFML_DEPENDENCIES})
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# sfml-window
|
||||||
|
list(FIND SFML_FIND_COMPONENTS "window" FIND_SFML_WINDOW_COMPONENT)
|
||||||
|
if(NOT ${FIND_SFML_WINDOW_COMPONENT} EQUAL -1)
|
||||||
|
|
||||||
|
# find libraries
|
||||||
|
if(FIND_SFML_OS_LINUX OR FIND_SFML_OS_FREEBSD)
|
||||||
|
find_sfml_dependency(X11_LIBRARY "X11" X11)
|
||||||
|
find_sfml_dependency(LIBXCB_LIBRARIES "XCB" xcb libxcb)
|
||||||
|
find_sfml_dependency(X11_XCB_LIBRARY "X11-xcb" X11-xcb libX11-xcb)
|
||||||
|
find_sfml_dependency(XCB_RANDR_LIBRARY "xcb-randr" xcb-randr libxcb-randr)
|
||||||
|
find_sfml_dependency(XCB_IMAGE_LIBRARY "xcb-image" xcb-image libxcb-image)
|
||||||
|
endif()
|
||||||
|
|
||||||
|
if(FIND_SFML_OS_LINUX)
|
||||||
|
find_sfml_dependency(UDEV_LIBRARIES "UDev" udev libudev)
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# update the list
|
||||||
|
if(FIND_SFML_OS_WINDOWS)
|
||||||
|
set(SFML_WINDOW_DEPENDENCIES ${SFML_WINDOW_DEPENDENCIES} "opengl32" "winmm" "gdi32")
|
||||||
|
elseif(FIND_SFML_OS_LINUX)
|
||||||
|
set(SFML_WINDOW_DEPENDENCIES ${SFML_WINDOW_DEPENDENCIES} "GL" ${X11_LIBRARY} ${LIBXCB_LIBRARIES} ${X11_XCB_LIBRARY} ${XCB_RANDR_LIBRARY} ${XCB_IMAGE_LIBRARY} ${UDEV_LIBRARIES})
|
||||||
|
elseif(FIND_SFML_OS_FREEBSD)
|
||||||
|
set(SFML_WINDOW_DEPENDENCIES ${SFML_WINDOW_DEPENDENCIES} "GL" ${X11_LIBRARY} ${LIBXCB_LIBRARIES} ${X11_XCB_LIBRARY} ${XCB_RANDR_LIBRARY} ${XCB_IMAGE_LIBRARY} "usbhid")
|
||||||
|
elseif(FIND_SFML_OS_MACOSX)
|
||||||
|
set(SFML_WINDOW_DEPENDENCIES ${SFML_WINDOW_DEPENDENCIES} "-framework OpenGL -framework Foundation -framework AppKit -framework IOKit -framework Carbon")
|
||||||
|
endif()
|
||||||
|
set(SFML_DEPENDENCIES ${SFML_WINDOW_DEPENDENCIES} ${SFML_DEPENDENCIES})
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# sfml-graphics
|
||||||
|
list(FIND SFML_FIND_COMPONENTS "graphics" FIND_SFML_GRAPHICS_COMPONENT)
|
||||||
|
if(NOT ${FIND_SFML_GRAPHICS_COMPONENT} EQUAL -1)
|
||||||
|
|
||||||
|
# find libraries
|
||||||
|
find_sfml_dependency(FREETYPE_LIBRARY "FreeType" freetype)
|
||||||
|
find_sfml_dependency(JPEG_LIBRARY "libjpeg" jpeg)
|
||||||
|
|
||||||
|
# update the list
|
||||||
|
set(SFML_GRAPHICS_DEPENDENCIES ${FREETYPE_LIBRARY} ${JPEG_LIBRARY})
|
||||||
|
set(SFML_DEPENDENCIES ${SFML_GRAPHICS_DEPENDENCIES} ${SFML_DEPENDENCIES})
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# sfml-audio
|
||||||
|
list(FIND SFML_FIND_COMPONENTS "audio" FIND_SFML_AUDIO_COMPONENT)
|
||||||
|
if(NOT ${FIND_SFML_AUDIO_COMPONENT} EQUAL -1)
|
||||||
|
|
||||||
|
# find libraries
|
||||||
|
find_sfml_dependency(OPENAL_LIBRARY "OpenAL" openal openal32)
|
||||||
|
find_sfml_dependency(OGG_LIBRARY "Ogg" ogg)
|
||||||
|
find_sfml_dependency(VORBIS_LIBRARY "Vorbis" vorbis)
|
||||||
|
find_sfml_dependency(VORBISFILE_LIBRARY "VorbisFile" vorbisfile)
|
||||||
|
find_sfml_dependency(VORBISENC_LIBRARY "VorbisEnc" vorbisenc)
|
||||||
|
find_sfml_dependency(FLAC_LIBRARY "FLAC" FLAC)
|
||||||
|
|
||||||
|
# update the list
|
||||||
|
set(SFML_AUDIO_DEPENDENCIES ${OPENAL_LIBRARY} ${FLAC_LIBRARY} ${VORBISENC_LIBRARY} ${VORBISFILE_LIBRARY} ${VORBIS_LIBRARY} ${OGG_LIBRARY})
|
||||||
|
set(SFML_DEPENDENCIES ${SFML_DEPENDENCIES} ${SFML_AUDIO_DEPENDENCIES})
|
||||||
|
endif()
|
||||||
|
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# handle errors
|
||||||
|
if(NOT SFML_VERSION_OK)
|
||||||
|
# SFML version not ok
|
||||||
|
set(FIND_SFML_ERROR "SFML found but version too low (requested: ${SFML_FIND_VERSION}, found: ${SFML_VERSION_MAJOR}.${SFML_VERSION_MINOR}.${SFML_VERSION_PATCH})")
|
||||||
|
set(SFML_FOUND FALSE)
|
||||||
|
elseif(SFML_STATIC_LIBRARIES AND FIND_SFML_DEPENDENCIES_NOTFOUND)
|
||||||
|
set(FIND_SFML_ERROR "SFML found but some of its dependencies are missing (${FIND_SFML_DEPENDENCIES_NOTFOUND})")
|
||||||
|
set(SFML_FOUND FALSE)
|
||||||
|
elseif(NOT SFML_FOUND)
|
||||||
|
# include directory or library not found
|
||||||
|
set(FIND_SFML_ERROR "Could NOT find SFML (missing: ${FIND_SFML_MISSING})")
|
||||||
|
endif()
|
||||||
|
if (NOT SFML_FOUND)
|
||||||
|
if(SFML_FIND_REQUIRED)
|
||||||
|
# fatal error
|
||||||
|
message(FATAL_ERROR ${FIND_SFML_ERROR})
|
||||||
|
elseif(NOT SFML_FIND_QUIETLY)
|
||||||
|
# error but continue
|
||||||
|
message("${FIND_SFML_ERROR}")
|
||||||
|
endif()
|
||||||
|
endif()
|
||||||
|
|
||||||
|
# handle success
|
||||||
|
if(SFML_FOUND AND NOT SFML_FIND_QUIETLY)
|
||||||
|
message(STATUS "Found SFML ${SFML_VERSION_MAJOR}.${SFML_VERSION_MINOR}.${SFML_VERSION_PATCH} in ${SFML_INCLUDE_DIR}")
|
||||||
|
endif()
|
||||||
|
|
||||||
@ -0,0 +1,133 @@
|
|||||||
|
|
||||||
|
%%% Preamble
|
||||||
|
\documentclass[paper=a4, fontsize=11pt]{scrartcl}
|
||||||
|
\usepackage[T1]{fontenc}
|
||||||
|
\usepackage{fourier}
|
||||||
|
|
||||||
|
\usepackage[english]{babel} % English language/hyphenation
|
||||||
|
\usepackage[protrusion=true,expansion=true]{microtype}
|
||||||
|
\usepackage{amsmath,amsfonts,amsthm} % Math packages
|
||||||
|
\usepackage[pdftex]{graphicx}
|
||||||
|
\usepackage{url}
|
||||||
|
|
||||||
|
|
||||||
|
%%% Custom sectioning
|
||||||
|
\usepackage{sectsty}
|
||||||
|
\allsectionsfont{\centering \normalfont\scshape}
|
||||||
|
|
||||||
|
|
||||||
|
%%% Custom headers/footers (fancyhdr package)
|
||||||
|
\usepackage{fancyhdr}
|
||||||
|
\pagestyle{fancyplain}
|
||||||
|
\fancyhead{} % No page header
|
||||||
|
\fancyfoot[L]{} % Empty
|
||||||
|
\fancyfoot[C]{} % Empty
|
||||||
|
\fancyfoot[R]{\thepage} % Pagenumbering
|
||||||
|
\renewcommand{\headrulewidth}{0pt} % Remove header underlines
|
||||||
|
\renewcommand{\footrulewidth}{0pt} % Remove footer underlines
|
||||||
|
\setlength{\headheight}{13.6pt}
|
||||||
|
|
||||||
|
|
||||||
|
%%% Equation and float numbering
|
||||||
|
\numberwithin{equation}{section} % Equationnumbering: section.eq#
|
||||||
|
\numberwithin{figure}{section} % Figurenumbering: section.fig#
|
||||||
|
\numberwithin{table}{section} % Tablenumbering: section.tab#
|
||||||
|
|
||||||
|
|
||||||
|
%%% Maketitle metadata
|
||||||
|
\newcommand{\horrule}[1]{\rule{\linewidth}{#1}} % Horizontal rule
|
||||||
|
|
||||||
|
\title{
|
||||||
|
%\vspace{-1in}
|
||||||
|
\usefont{OT1}{bch}{b}{n}
|
||||||
|
\normalfont \normalsize \textsc{Central Washington University} \\ [1pt]
|
||||||
|
\normalfont \normalsize \textsc{CS 427} \\ [15pt]
|
||||||
|
\horrule{0.5pt} \\[0.4cm]
|
||||||
|
\huge Lab 1 \\
|
||||||
|
\horrule{2pt} \\[0.5cm]
|
||||||
|
}
|
||||||
|
\author{
|
||||||
|
\normalfont \normalsize
|
||||||
|
Mitchell Hansen\\[-3pt] \normalsize
|
||||||
|
\today
|
||||||
|
}
|
||||||
|
\date{}
|
||||||
|
|
||||||
|
|
||||||
|
%%% Begin document
|
||||||
|
\begin{document}
|
||||||
|
\maketitle
|
||||||
|
\section{Introduction}
|
||||||
|
For our first lab, we took 15 functions from various optimization test suites and ran them for varying dimensionality and input. The goal being to determine the range of the functions output, it's standard deviation from input to input, average time to run the function, and other such tests.
|
||||||
|
|
||||||
|
\section {Methods}
|
||||||
|
For each test, I generated an array of random integers using the C rand() function, seeded at the begginning of the application with srand() and the time. In future tests I will be utilizing the Mersenne Twister method of generating random numbers, which is far more random. After generating the random numbers, I passed each set into the functions at 10, 20, and 30 dimensionality intervals, logging what they return.
|
||||||
|
|
||||||
|
\section{Experimentation Results}
|
||||||
|
\begin{center}
|
||||||
|
\begin{tabular}{ | l | l | l | l | l | l |}
|
||||||
|
\hline
|
||||||
|
Function & Dimensionality & Mean & Median & Deviation & Avg. Time \\ \hline
|
||||||
|
|
||||||
|
schwefel & 10 & -1.36058 & -103.102 & 618.227 & 1.7575 \\ \hline
|
||||||
|
& 20 & -5.22963 & 1366.82 & 863.447 & 2.7682 \\ \hline
|
||||||
|
& 30 & -3.35496 & 441.352 & 1071.53 & 3.8081 \\ \hline
|
||||||
|
de jong & 10 & 33264 & 40362 & 9402.19 & 0.7467 \\ \hline
|
||||||
|
& 20 & 66737.3 & 51890 & 13442.9 & 0.7351 \\ \hline
|
||||||
|
& 30 & 100044 & 95177 & 16432.6 & 0.3675 \\ \hline
|
||||||
|
rosenbrok & 10 & 1.79827e+10 & 1.67874e+10 & 7.98429e+09 & 0.0835 \\ \hline
|
||||||
|
& 20 & 3.78563e+10 & 2.30065e+10 & 1.16052e+10 & 1.76 \\ \hline
|
||||||
|
& 30 & 5.81661e+10 & 5.26908e+10 & 1.43576e+10 & 1.0109 \\ \hline
|
||||||
|
rastrigin & 10 & 58068.8 & 37720 & 17104 & 1.0163 \\ \hline
|
||||||
|
& 20 & 231345 & 264520 & 47672.9 & 2.5908 \\ \hline
|
||||||
|
& 30 & 521790 & 523500 & 89713.1 & 3.1789 \\ \hline
|
||||||
|
griegwangk & 10 & 208.696 & 201.88 & 58.559 & 1.0334 \\ \hline
|
||||||
|
& 20 & 417.247 & 502.07 & 82.0775 & 3.7124 \\ \hline
|
||||||
|
& 30 & 626.906 & 633.49 & 101.588 & 4.0333 \\ \hline
|
||||||
|
sine envelope sine wave & 10 & 7.484 & 8.42901 & 0.776108 & 1.3989 \\ \hline
|
||||||
|
& 20 & 15.7985 & 14.7606 & 1.13035 & 3.3113 \\ \hline
|
||||||
|
& 30 & 24.1052 & 26.4525 & 1.40737 & 5.025 \\ \hline
|
||||||
|
stretched v sine wave & 10 & 30.0648 & 37.1503 & 5.56455 & 3.8074 \\ \hline
|
||||||
|
& 20 & 63.5978 & 61.1979 & 8.1662 & 6.1452 \\ \hline
|
||||||
|
& 30 & 96.8599 & 98.3275 & 9.95237 & 8.8907 \\ \hline
|
||||||
|
ackleys one & 10 & 180.243 & 149.991 & 31.6898 & 2.0144 \\ \hline
|
||||||
|
& 20 & 380.786 & 442.58 & 45.7357 & 5.0182 \\ \hline
|
||||||
|
& 30 & 583.005 & 681.962 & 57.6042 & 7.0651 \\ \hline
|
||||||
|
ackleys two & 10 & 152.968 & 152.204 & 4.22097 & 3.3884 \\ \hline
|
||||||
|
& 20 & 322.851 & 326.451 & 6.31728 & 6.0586 \\ \hline
|
||||||
|
& 30 & 492.805 & 498.597 & 7.74626 & 8.9194 \\ \hline
|
||||||
|
egg holder & 10 & -28.1673 & -1334.07 & 872.855 & 2.2148 \\ \hline
|
||||||
|
& 20 & -46.6518 & 718.965 & 1265.43 & 5.0378 \\ \hline
|
||||||
|
& 30 & -120.776 & -2238.62 & 1563.34 & 7.0771 \\ \hline
|
||||||
|
rana & 10 & 5.96939 & 33.1935 & 602.187 & 4.1041 \\ \hline
|
||||||
|
& 20 & 17.3048 & -816.859 & 874.721 & 9.1242 \\ \hline
|
||||||
|
& 30 & 3.58453 & -1667.75 & 1077.64 & 13.6967 \\ \hline
|
||||||
|
pathological & 10 & 4.50048 & 4.50316 & 0.226241 & 2.0145 \\ \hline
|
||||||
|
& 20 & 9.50768 & 9.77562 & 0.326493 & 3.9414 \\ \hline
|
||||||
|
& 30 & 14.5061 & 14.8958 & 0.409931 & 5.4952 \\ \hline
|
||||||
|
michalewicz & 10 & -0.0472368 & 0 & 0.535466 & 1.5615 \\ \hline
|
||||||
|
& 20 & -0.0359855 & 0 & 0.773229 & 3.0854 \\ \hline
|
||||||
|
& 30 & -0.038839 & 0 & 0.969944 & 4.5795 \\ \hline
|
||||||
|
masters cosine wave & 10 & 0.0242817 & -2.82524 & 2.07645 & 2.4656 \\ \hline
|
||||||
|
& 20 & 0.000543481 & 1.27202 & 3.02895 & 3.886 \\ \hline
|
||||||
|
& 30 & -0.073613 & 2.43497 & 3.70944 & 5.1847 \\ \hline
|
||||||
|
shekels foxholes & 10 & -3.18527 & 0 & 4.95254 & 4.8601 \\ \hline
|
||||||
|
& 20 & -3.06529 & 0 & 4.60784 & 7.5344 \\ \hline
|
||||||
|
& 30 & -3.39603 & -5.22145 & 5.05984 & 11.7375 \\ \hline
|
||||||
|
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
\end{center}
|
||||||
|
|
||||||
|
|
||||||
|
\section{Analysis}
|
||||||
|
Viewing the results of the experiments yields expected and unexpected results. First off, and probably the most easily predicted is the steady increase in computation time required for each function as measured in microseconds. Where steady increases in dimensionality yielded steady increases in computation time. Everything further from here was unexpected for me as I had no idea what to expect from these computations.
|
||||||
|
|
||||||
|
|
||||||
|
For most of the functions, each steady increase in dimensionality was met with an equally steady increasing Mean, Median, and Standard Deviation. Notable exceptions to this are the Schwefel, Michalewicz, and Shekels Foxholes functions. Each either showed little to no change in values, or the values increased and decreased in a random manner.
|
||||||
|
|
||||||
|
I also graphed each of the 15 functions in 2D to view their behavior and found that the Michalewicz and Shekels Foxholes functions had a behavior very different than the other 13. The Michalewicz was similar to how it looks when viewing 3rd party sources, but the Shekels Foxholes function does not match 3rd party sources, which leads me to believe that the input data I had is weird, or I have the function wrong.
|
||||||
|
|
||||||
|
|
||||||
|
%%% End document
|
||||||
|
\end{document}
|
||||||
@ -0,0 +1,611 @@
|
|||||||
|
#include <iostream>
|
||||||
|
#include <vector>
|
||||||
|
#include <cmath>
|
||||||
|
#include <map>
|
||||||
|
#include <chrono>
|
||||||
|
#include "SFML/Graphics.hpp"
|
||||||
|
#include "mtrand.h"
|
||||||
|
|
||||||
|
|
||||||
|
// In order to compile and run this project you're going to either comment out the SFML
|
||||||
|
// code or install SFML which is trivial on *NIX.
|
||||||
|
|
||||||
|
// Just apt-get install libsfml-dev
|
||||||
|
// or brew install sfml
|
||||||
|
|
||||||
|
|
||||||
|
std::vector<double> c = {0.806,0.517,0.1,0.908,0.965,0.669,0.524,0.902,0.351,0.876,0.462,
|
||||||
|
0.491,0.463,0.741,0.352,0.869,0.813,0.811,0.0828,0.964,0.789,0.360,0.369,
|
||||||
|
0.992,0.332,0.817,0.632,0.883,0.608,0.326};
|
||||||
|
|
||||||
|
double a[][10] =
|
||||||
|
{
|
||||||
|
{9.681,0.667,4.783,9.095,3.517,9.325,6.544,0.211,5.122,2.02},
|
||||||
|
{9.4,2.041,3.788,7.931,2.882,2.672,3.568,1.284,7.033,7.374},
|
||||||
|
{8.025,9.152,5.114,7.621,4.564,4.711,2.996,6.126,0.734,4.982},
|
||||||
|
{2.196,0.415,5.649,6.979,9.510,9.166,6.304,6.054,9.377,1.426},
|
||||||
|
{8.074,8.777,3.467,1.863,6.708,6.349,4.534,0.276,7.633,1.567},
|
||||||
|
{7.650,5.658,0.720,2.764,3.278,5.283,7.474,6.274,1.409,8.208},
|
||||||
|
{1.256,3.605,8.623,6.905,4.584,8.133,6.071,6.888,4.187,5.448},
|
||||||
|
{8.314,2.261,4.24,1.781,4.124,0.932,8.129,8.658,1.208,5.762},
|
||||||
|
{0.226,8.858,1.42,0.954,1.622,4.698,6.228,9.096,0.972,7.637},
|
||||||
|
{7.305,2.228,1.242,5.928,9.133,1.826,4.06,5.204,8.713,8.247},
|
||||||
|
{0.652,7.027,0.508,4.876,8.807,4.632,5.808,6.937,3.291,7.016},
|
||||||
|
{2.699,3.516,5.847,4.119,4.461,7.496,8.817,0.69,6.593,9.789},
|
||||||
|
{8.327,3.897,2.017,9.57,9.825,1.15,1.395,3.885,6.354,0.109},
|
||||||
|
{2.132,7.006,7.136,2.641,1.882,5.943,7.273,7.691,2.88,0.564},
|
||||||
|
{4.707,5.579,4.08,0.581,9.698,8.542,8.077,8.515,9.231,4.67},
|
||||||
|
{8.304,7.559,8.567,0.322,7.128,8.392,1.472,8.524,2.277,7.826},
|
||||||
|
{8.632,4.409,4.832,5.768,7.05,6.715,1.711,4.323,4.405,4.591},
|
||||||
|
{4.887,9.112,0.17,8.967,9.693,9.867,7.508,7.77,8.382,6.74},
|
||||||
|
{2.44,6.686,4.299,1.007,7.008,1.427,9.398,8.48,9.95,1.675},
|
||||||
|
{6.306,8.583,6.084,1.138,4.350,3.134,7.853,6.061,7.457,2.258},
|
||||||
|
{0.652,2.343,1.37,0.821,1.31,1.063,0.689,8.819,8.833,9.07},
|
||||||
|
{5.558,1.272,5.756,9.857,2.279,2.764,1.284,1.677,1.244,1.234},
|
||||||
|
{3.352,7.549,9.817,9.437,8.687,4.167,2.57,6.54,0.228,0.027},
|
||||||
|
{8.798,0.88,2.37,0.168,1.701,3.68,1.231,2.39,2.499,0.064},
|
||||||
|
{1.46,8.057,1.337,7.217,7.914,3.615,9.981,9.198,5.292,1.224},
|
||||||
|
{0.432,8.645,8.774,0.249,8.081,7.461,4.416,0.652,4.002,4.644},
|
||||||
|
{0.679,2.8,5.523,3.049,2.968,7.225,6.73,4.199,9.614,9.229},
|
||||||
|
{4.263,1.074,7.286,5.599,8.291,5.2,9.214,8.272,4.398,4.506},
|
||||||
|
{9.496,4.83,3.15,8.27,5.079,1.231,5.731,9.494,1.883,9.732},
|
||||||
|
{4.138,2.562,2.532,9.661,5.611,5.5,6.886,2.341,9.699,6.5}
|
||||||
|
};
|
||||||
|
|
||||||
|
double schwefel(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 512;
|
||||||
|
int lower_bound = -512;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += (-input[i]) * std::sin(std::sqrt(std::abs(input[i])));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
double first_de_jong(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double rosenbrock(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 100 * std::pow((std::pow(input[i], 2) - input[i + 1]), 2) + std::pow((1 - input[i]), 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double rastrigin(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2) - 10 * std::cos(2 * M_PI * input[i]);
|
||||||
|
}
|
||||||
|
|
||||||
|
sum *= 2 * input.size();
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double griewangk(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2) / 4000;
|
||||||
|
}
|
||||||
|
|
||||||
|
double product = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
product *= std::cos(input[i] / sqrt(i + 1));
|
||||||
|
}
|
||||||
|
|
||||||
|
return 1 + sum - product;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
double sine_envelope_sine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 0.5 + (std::pow(std::sin(std::pow(input[i], 2) + std::pow(input[i + 1], 2) - 0.5), 2)) /
|
||||||
|
(1 + 0.001 * (std::pow(input[i], 2) + std::pow(input[i + 1], 2)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double stretched_v_sine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += std::pow(std::pow(input[i], 2) + std::pow(input[i + 1], 2), 1.0 / 4) *
|
||||||
|
std::pow(std::sin(50 * std::pow(std::pow(input[i], 2) + std::pow(input[i + 1], 2), 1.0 / 10)), 2) + 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
double ackleys_one(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 32;
|
||||||
|
int lower_bound = -32;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += (1.0 / pow(M_E, 0.2)) *
|
||||||
|
std::sqrt(std::pow(input[i], 2) + std::pow(input[i + 1], 2)) +
|
||||||
|
3 * std::cos(2 * input[i]) +
|
||||||
|
std::sin(2 * input[i + 1]);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
double ackleys_two(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 32;
|
||||||
|
int lower_bound = -32;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 20 + M_E -
|
||||||
|
(20 / (std::pow(M_E, 0.2) * std::sqrt(((std::pow(input[i], 2) + std::pow(input[i+1], 2) + 1) / 2)))) -
|
||||||
|
std::pow(M_E, 0.5 * std::cos(2 * M_PI * input[i]) + cos(2 * M_PI * input[i + 1]));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
double egg_holder(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += -input[i] * std::sin(std::sqrt(abs(input[i] - input[i + 1] - 47))) -
|
||||||
|
(input[i + 1] + 47) * std::sin(std::sqrt(std::abs(input[i + 1] + 47 + input[i] / 2)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double rana(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += input[i] * std::sin(std::sqrt(std::abs(input[i + 1] - input[i] + 1))) *
|
||||||
|
std::cos(std::sqrt(std::abs(input[i + 1] + input[i] + 1))) +
|
||||||
|
(input[i + 1] + 1) *
|
||||||
|
std::cos(std::sqrt(std::abs(input[i + 1] - input[i] + 1))) *
|
||||||
|
std::sin(std::sqrt(std::abs(input[i + 1] + input[i] + 1)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
double pathological(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += 0.5 +
|
||||||
|
(std::pow(std::sin(std::sqrt(100 * std::pow(input[i], 2) + std::pow(input[i + 1], 2))), 2) - 0.5) /
|
||||||
|
(1 + 0.001 * std::pow(std::pow(input[i], 2) - 2 * input[i] * input[i + 1] + std::pow(input[i + 1], 2), 2));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double michalewicz(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = M_PI;
|
||||||
|
int lower_bound = 0;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += std::sin(input[i]) * std::pow(std::sin(i * std::pow(input[i], 2) / M_PI), 20);
|
||||||
|
}
|
||||||
|
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
double masters_cosine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += std::pow(M_E, -(1/8) * (std::pow(input[i], 2) + std::pow(input[i + 1], 2) + 0.5 * input[i + 1] * input[i])) *
|
||||||
|
std::cos(4 * std::sqrt(std::pow(input[i], 2) + std::pow(input[i + 1], 2) + 0.5 * input[i] * input[i + 1]));
|
||||||
|
}
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double shekels_foxholes(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 10;
|
||||||
|
int lower_bound = 0;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < c.size() - 1; i++) {
|
||||||
|
|
||||||
|
double bottom_sum = 0;
|
||||||
|
|
||||||
|
for (int q = 0; q < input.size(); q++){
|
||||||
|
bottom_sum = std::pow(input.at(q) - a[i][q], 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
sum += 1 / (bottom_sum + c[i]);
|
||||||
|
}
|
||||||
|
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
struct timer{
|
||||||
|
std::chrono::high_resolution_clock::time_point t1;
|
||||||
|
std::chrono::high_resolution_clock::time_point t2;
|
||||||
|
void start(){t1 = std::chrono::high_resolution_clock::now();}
|
||||||
|
void end(){t2 = std::chrono::high_resolution_clock::now();}
|
||||||
|
double duration(){ return std::chrono::duration_cast<std::chrono::microseconds>( t2 - t1 ).count();}
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
double set_within(double val, double prior_upper, double prior_lower, double after_upper, double after_lower){
|
||||||
|
return ((after_upper - after_lower) * (val - prior_lower) / (prior_upper - prior_lower)) + after_lower;
|
||||||
|
}
|
||||||
|
|
||||||
|
struct function{
|
||||||
|
|
||||||
|
double (*function_pointer)(std::vector<double>);
|
||||||
|
double range = 0;
|
||||||
|
double upper_bound = 0;
|
||||||
|
double lower_bound = 0;
|
||||||
|
|
||||||
|
timer t;
|
||||||
|
|
||||||
|
function(double (*func)(std::vector<double>), double upper_bound, double lower_bound){
|
||||||
|
function_pointer = func;
|
||||||
|
this->upper_bound = upper_bound;
|
||||||
|
this->lower_bound = lower_bound;
|
||||||
|
}
|
||||||
|
|
||||||
|
double compute_defined(std::vector<double> input){
|
||||||
|
|
||||||
|
for (auto v: input) {
|
||||||
|
if (v >= lower_bound && v <= upper_bound) {
|
||||||
|
return function_pointer(input);
|
||||||
|
} else {
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
enum Test_Data { MEAN, MEDIAN, DEVIATION, AVG_TIME, DIMENSIONALIY, UPPER_RANGE, LOWER_RANGE};
|
||||||
|
|
||||||
|
std::map<Test_Data, double> run_tests_random(int dimensionality, int permutations){
|
||||||
|
|
||||||
|
std::vector<double> times;
|
||||||
|
std::vector<double> vals;
|
||||||
|
|
||||||
|
for (int i = 0; i < permutations; i++) {
|
||||||
|
|
||||||
|
std::vector<double> dimension_vals;
|
||||||
|
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
dimension_vals.push_back(fmod(std::rand() , (upper_bound * 2)) + lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
t.start();
|
||||||
|
|
||||||
|
vals.push_back(compute_defined(dimension_vals));
|
||||||
|
|
||||||
|
t.end();
|
||||||
|
|
||||||
|
times.push_back(t.duration());
|
||||||
|
}
|
||||||
|
|
||||||
|
// Mean
|
||||||
|
double mean = 0;
|
||||||
|
for (double v: vals){
|
||||||
|
mean += v;
|
||||||
|
}
|
||||||
|
mean /= vals.size();
|
||||||
|
|
||||||
|
// Median
|
||||||
|
double median = vals[std::floor(vals.size() / 2)];
|
||||||
|
|
||||||
|
// Standard Deviation
|
||||||
|
double sum = 0;
|
||||||
|
for (double v: vals){
|
||||||
|
sum += std::pow(v - mean, 2);
|
||||||
|
}
|
||||||
|
double deviation = std::sqrt(sum / vals.size());
|
||||||
|
|
||||||
|
// Time Mean
|
||||||
|
double time_mean = 0;
|
||||||
|
for (double v: times){
|
||||||
|
time_mean += v;
|
||||||
|
}
|
||||||
|
time_mean /= times.size();
|
||||||
|
|
||||||
|
// Range
|
||||||
|
std::sort(vals.begin(), vals.end());
|
||||||
|
|
||||||
|
double lower_range = vals.front();
|
||||||
|
double upper_range = vals.back();
|
||||||
|
|
||||||
|
std::map<Test_Data, double> ret;
|
||||||
|
|
||||||
|
ret[Test_Data::DIMENSIONALIY] = dimensionality;
|
||||||
|
ret[Test_Data::AVG_TIME] = time_mean;
|
||||||
|
ret[Test_Data::DEVIATION] = deviation;
|
||||||
|
ret[Test_Data::MEAN] = mean;
|
||||||
|
ret[Test_Data::MEDIAN] = median;
|
||||||
|
ret[Test_Data::UPPER_RANGE] = upper_range;
|
||||||
|
ret[Test_Data::LOWER_RANGE] = lower_range;
|
||||||
|
|
||||||
|
std::cout << " & " << dimensionality << " & " << mean << " & " << median << " & " << deviation << " & " << time_mean << " \\\\ \\hline" << std::endl;
|
||||||
|
|
||||||
|
return ret;
|
||||||
|
};
|
||||||
|
|
||||||
|
std::map<Test_Data, double> run_tests_defined(std::vector<double> input, int permutations){
|
||||||
|
|
||||||
|
std::vector<double> times;
|
||||||
|
std::vector<double> vals;
|
||||||
|
|
||||||
|
for (int i = 0; i < permutations; i++) {
|
||||||
|
|
||||||
|
t.start();
|
||||||
|
|
||||||
|
vals.push_back(function_pointer(input));
|
||||||
|
|
||||||
|
t.end();
|
||||||
|
|
||||||
|
times.push_back(t.duration());
|
||||||
|
}
|
||||||
|
|
||||||
|
// Mean
|
||||||
|
double mean = 0;
|
||||||
|
for (double v: vals){
|
||||||
|
mean += v;
|
||||||
|
}
|
||||||
|
mean /= vals.size();
|
||||||
|
|
||||||
|
// Median
|
||||||
|
double median = vals[std::floor(vals.size() / 2)];
|
||||||
|
|
||||||
|
// Standard Deviation
|
||||||
|
double sum = 0;
|
||||||
|
for (double v: vals){
|
||||||
|
sum += std::pow(v - mean, 2);
|
||||||
|
}
|
||||||
|
double deviation = std::sqrt(sum / vals.size());
|
||||||
|
|
||||||
|
// Time Mean
|
||||||
|
double time_mean = 0;
|
||||||
|
for (double v: times){
|
||||||
|
time_mean += v;
|
||||||
|
}
|
||||||
|
time_mean /= times.size();
|
||||||
|
|
||||||
|
// Range
|
||||||
|
std::sort(vals.begin(), vals.end());
|
||||||
|
|
||||||
|
double lower_range = vals.front();
|
||||||
|
double upper_range = vals.back();
|
||||||
|
|
||||||
|
std::map<Test_Data, double> ret;
|
||||||
|
|
||||||
|
ret[Test_Data::DIMENSIONALIY] = input.size();
|
||||||
|
ret[Test_Data::AVG_TIME] = time_mean;
|
||||||
|
ret[Test_Data::DEVIATION] = deviation;
|
||||||
|
ret[Test_Data::MEAN] = mean;
|
||||||
|
ret[Test_Data::MEDIAN] = median;
|
||||||
|
ret[Test_Data::UPPER_RANGE] = upper_range;
|
||||||
|
ret[Test_Data::LOWER_RANGE] = lower_range;
|
||||||
|
|
||||||
|
return ret;
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
void draw(){
|
||||||
|
|
||||||
|
int bounds = fabs(upper_bound) + fabs(lower_bound);
|
||||||
|
int window_xy = 1024;
|
||||||
|
sf::RenderWindow window(sf::VideoMode(window_xy, window_xy), "Functions");
|
||||||
|
sf::Uint8* pixel_array = new sf::Uint8[window_xy * window_xy * 4];
|
||||||
|
sf::Texture texture;
|
||||||
|
texture.create(window_xy, window_xy);
|
||||||
|
sf::Sprite sprite(texture);
|
||||||
|
|
||||||
|
|
||||||
|
double min = 9999999;
|
||||||
|
double max = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < window_xy * window_xy * 4; i += 4) {
|
||||||
|
|
||||||
|
std::vector<double> position =
|
||||||
|
{static_cast<double>(set_within(((i / 4) % window_xy) - window_xy/2, 512, -512, upper_bound, lower_bound)),
|
||||||
|
static_cast<double>(set_within(((i / 4) / window_xy) - window_xy/2, 512, -512, upper_bound, lower_bound))};
|
||||||
|
|
||||||
|
auto res = static_cast<double>(compute_defined(position));
|
||||||
|
if (res > max)
|
||||||
|
max = res;
|
||||||
|
if (res < min)
|
||||||
|
min = res;
|
||||||
|
}
|
||||||
|
|
||||||
|
for (int i = 0; i < window_xy * window_xy * 4; i += 4){
|
||||||
|
|
||||||
|
std::vector<double> position =
|
||||||
|
{static_cast<double>(set_within(((i / 4) % window_xy) - window_xy/2, 512, -512, upper_bound, lower_bound)),
|
||||||
|
static_cast<double>(set_within(((i / 4) / window_xy) - window_xy/2, 512, -512, upper_bound, lower_bound))};
|
||||||
|
|
||||||
|
auto res = static_cast<int>((((compute_defined(position) - min) * (16581375 - 0)) / (max - min)) + 0);
|
||||||
|
|
||||||
|
pixel_array[i + 0] = res & 0xff;
|
||||||
|
pixel_array[i + 1] = (res>>8) & 0xff;
|
||||||
|
pixel_array[i + 2] = (res>>16) & 0xff;
|
||||||
|
pixel_array[i + 3] = 255;
|
||||||
|
|
||||||
|
//pixel_array[i + 0] = res;
|
||||||
|
//pixel_array[i + 1] = 255;
|
||||||
|
//pixel_array[i + 2] = 255;
|
||||||
|
//pixel_array[i + 3] = 255;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
texture.update(pixel_array);
|
||||||
|
|
||||||
|
while (window.isOpen()) {
|
||||||
|
|
||||||
|
sf::Event event;
|
||||||
|
while (window.pollEvent(event)) {
|
||||||
|
if (event.type == sf::Event::Closed)
|
||||||
|
window.close();
|
||||||
|
}
|
||||||
|
|
||||||
|
window.clear();
|
||||||
|
window.draw(sprite);
|
||||||
|
window.display();
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
delete pixel_array;
|
||||||
|
};
|
||||||
|
};
|
||||||
|
|
||||||
|
int main() {
|
||||||
|
|
||||||
|
MTRand r(time(NULL));
|
||||||
|
std::cout << r();
|
||||||
|
|
||||||
|
|
||||||
|
srand(time(NULL));
|
||||||
|
|
||||||
|
function schwefel_f(&schwefel, 512, -512);
|
||||||
|
function de_jong_f(&first_de_jong, 100, -100);
|
||||||
|
function rosenbrock_f(&rosenbrock, 100, -100);
|
||||||
|
function rastrigin_f(&rastrigin, 30, -30);
|
||||||
|
function griegwangk_f(&griewangk, 500, -500);
|
||||||
|
function sine_envelope_sine_wave_f(&sine_envelope_sine_wave, 30, -30);
|
||||||
|
function stretched_v_sine_wave_f(&stretched_v_sine_wave, 30, -30);
|
||||||
|
function ackleys_one_f(&ackleys_one, 32, -32);
|
||||||
|
function ackleys_two_f(&ackleys_two, 32, -32);
|
||||||
|
function egg_holder_f(&egg_holder, 500, -500);
|
||||||
|
function rana_f(&rana, 500, -500);
|
||||||
|
function pathological_f(&pathological, 100, -100);
|
||||||
|
function michalewicz_f(&michalewicz, M_PI, 0);
|
||||||
|
function masters_cosine_wave_f(&masters_cosine_wave, 30, -30);
|
||||||
|
function shekels_foxholes_f(&shekels_foxholes, 10, 0);
|
||||||
|
|
||||||
|
// shekels_foxholes_f.draw();
|
||||||
|
// schwefel_f.draw();
|
||||||
|
// de_jong_f.draw();
|
||||||
|
// rosenbrock_f.draw();
|
||||||
|
// rastrigin_f.draw();
|
||||||
|
// griegwangk_f.draw();
|
||||||
|
// sine_envelope_sine_wave_f.draw();
|
||||||
|
// stretched_v_sine_wave_f.draw();
|
||||||
|
// ackleys_one_f.draw();
|
||||||
|
// ackleys_two_f.draw();
|
||||||
|
// egg_holder_f.draw();
|
||||||
|
// rana_f.draw();
|
||||||
|
// pathological_f.draw();
|
||||||
|
// michalewicz_f.draw();
|
||||||
|
// masters_cosine_wave_f.draw();
|
||||||
|
|
||||||
|
std::string vals[] {
|
||||||
|
"MEAN", "MEDIAN", "DEVIATION", "AVG_TIME", "DIMENSIONALIY", "UPPER_RANGE", "LOWER_RANGE"
|
||||||
|
};
|
||||||
|
|
||||||
|
int permutations = 10000;
|
||||||
|
|
||||||
|
for (int i = 10; i < 40; i += 10) {
|
||||||
|
|
||||||
|
auto ret = schwefel_f.run_tests_random(i, permutations);
|
||||||
|
ret = de_jong_f.run_tests_random(i, permutations);
|
||||||
|
ret = rosenbrock_f.run_tests_random(i, permutations);
|
||||||
|
ret = rastrigin_f.run_tests_random(i, permutations);
|
||||||
|
ret = griegwangk_f.run_tests_random(i, permutations);
|
||||||
|
ret = sine_envelope_sine_wave_f.run_tests_random(i, permutations);
|
||||||
|
ret = stretched_v_sine_wave_f.run_tests_random(i, permutations);
|
||||||
|
ret = ackleys_one_f.run_tests_random(i, permutations);
|
||||||
|
ret = ackleys_two_f.run_tests_random(i, permutations);
|
||||||
|
ret = egg_holder_f.run_tests_random(i, permutations);
|
||||||
|
ret = rana_f.run_tests_random(i, permutations);
|
||||||
|
ret = pathological_f.run_tests_random(i, permutations);
|
||||||
|
ret = michalewicz_f.run_tests_random(i, permutations);
|
||||||
|
ret = masters_cosine_wave_f.run_tests_random(i, permutations);
|
||||||
|
ret = shekels_foxholes_f.run_tests_random(i, permutations);
|
||||||
|
}
|
||||||
|
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
@ -0,0 +1,51 @@
|
|||||||
|
// mtrand.cpp, see include file mtrand.h for information
|
||||||
|
|
||||||
|
#include "mtrand.h"
|
||||||
|
// non-inline function definitions and static member definitions cannot
|
||||||
|
// reside in header file because of the risk of multiple declarations
|
||||||
|
|
||||||
|
// initialization of static private members
|
||||||
|
unsigned long MTRand_int32::state[n] = {0x0UL};
|
||||||
|
int MTRand_int32::p = 0;
|
||||||
|
bool MTRand_int32::init = false;
|
||||||
|
|
||||||
|
void MTRand_int32::gen_state() { // generate new state vector
|
||||||
|
for (int i = 0; i < (n - m); ++i)
|
||||||
|
state[i] = state[i + m] ^ twiddle(state[i], state[i + 1]);
|
||||||
|
for (int i = n - m; i < (n - 1); ++i)
|
||||||
|
state[i] = state[i + m - n] ^ twiddle(state[i], state[i + 1]);
|
||||||
|
state[n - 1] = state[m - 1] ^ twiddle(state[n - 1], state[0]);
|
||||||
|
p = 0; // reset position
|
||||||
|
}
|
||||||
|
|
||||||
|
void MTRand_int32::seed(unsigned long s) { // init by 32 bit seed
|
||||||
|
state[0] = s & 0xFFFFFFFFUL; // for > 32 bit machines
|
||||||
|
for (int i = 1; i < n; ++i) {
|
||||||
|
state[i] = 1812433253UL * (state[i - 1] ^ (state[i - 1] >> 30)) + i;
|
||||||
|
// see Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier
|
||||||
|
// in the previous versions, MSBs of the seed affect only MSBs of the array state
|
||||||
|
// 2002/01/09 modified by Makoto Matsumoto
|
||||||
|
state[i] &= 0xFFFFFFFFUL; // for > 32 bit machines
|
||||||
|
}
|
||||||
|
p = n; // force gen_state() to be called for next random number
|
||||||
|
}
|
||||||
|
|
||||||
|
void MTRand_int32::seed(const unsigned long* array, int size) { // init by array
|
||||||
|
seed(19650218UL);
|
||||||
|
int i = 1, j = 0;
|
||||||
|
for (int k = ((n > size) ? n : size); k; --k) {
|
||||||
|
state[i] = (state[i] ^ ((state[i - 1] ^ (state[i - 1] >> 30)) * 1664525UL))
|
||||||
|
+ array[j] + j; // non linear
|
||||||
|
state[i] &= 0xFFFFFFFFUL; // for > 32 bit machines
|
||||||
|
++j; j %= size;
|
||||||
|
if ((++i) == n) { state[0] = state[n - 1]; i = 1; }
|
||||||
|
}
|
||||||
|
for (int k = n - 1; k; --k) {
|
||||||
|
state[i] = (state[i] ^ ((state[i - 1] ^ (state[i - 1] >> 30)) * 1566083941UL)) - i;
|
||||||
|
state[i] &= 0xFFFFFFFFUL; // for > 32 bit machines
|
||||||
|
if ((++i) == n) { state[0] = state[n - 1]; i = 1; }
|
||||||
|
}
|
||||||
|
state[0] = 0x80000000UL; // MSB is 1; assuring non-zero initial array
|
||||||
|
p = n; // force gen_state() to be called for next random number
|
||||||
|
}
|
||||||
|
|
||||||
@ -0,0 +1,156 @@
|
|||||||
|
// mtrand.h
|
||||||
|
// C++ include file for MT19937, with initialization improved 2002/1/26.
|
||||||
|
// Coded by Takuji Nishimura and Makoto Matsumoto.
|
||||||
|
// Ported to C++ by Jasper Bedaux 2003/1/1 (see http://www.bedaux.net/mtrand/).
|
||||||
|
// The generators returning floating point numbers are based on
|
||||||
|
// a version by Isaku Wada, 2002/01/09
|
||||||
|
//
|
||||||
|
// Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
|
||||||
|
// All rights reserved.
|
||||||
|
//
|
||||||
|
// Redistribution and use in source and binary forms, with or without
|
||||||
|
// modification, are permitted provided that the following conditions
|
||||||
|
// are met:
|
||||||
|
//
|
||||||
|
// 1. Redistributions of source code must retain the above copyright
|
||||||
|
// notice, this list of conditions and the following disclaimer.
|
||||||
|
//
|
||||||
|
// 2. Redistributions in binary form must reproduce the above copyright
|
||||||
|
// notice, this list of conditions and the following disclaimer in the
|
||||||
|
// documentation and/or other materials provided with the distribution.
|
||||||
|
//
|
||||||
|
// 3. The names of its contributors may not be used to endorse or promote
|
||||||
|
// products derived from this software without specific prior written
|
||||||
|
// permission.
|
||||||
|
//
|
||||||
|
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||||
|
// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||||
|
// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||||
|
// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||||
|
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||||
|
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||||
|
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||||
|
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
||||||
|
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||||
|
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||||
|
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||||
|
//
|
||||||
|
// Any feedback is very welcome.
|
||||||
|
// http://www.math.keio.ac.jp/matumoto/emt.html
|
||||||
|
// email: matumoto@math.keio.ac.jp
|
||||||
|
//
|
||||||
|
// Feedback about the C++ port should be sent to Jasper Bedaux,
|
||||||
|
// see http://www.bedaux.net/mtrand/ for e-mail address and info.
|
||||||
|
|
||||||
|
#ifndef MTRAND_H
|
||||||
|
#define MTRAND_H
|
||||||
|
|
||||||
|
class MTRand_int32 { // Mersenne Twister random number generator
|
||||||
|
public:
|
||||||
|
// default constructor: uses default seed only if this is the first instance
|
||||||
|
MTRand_int32() { if (!init) seed(5489UL); init = true; }
|
||||||
|
// constructor with 32 bit int as seed
|
||||||
|
MTRand_int32(unsigned long s) { seed(s); init = true; }
|
||||||
|
// constructor with array of size 32 bit ints as seed
|
||||||
|
MTRand_int32(const unsigned long* array, int size) { seed(array, size); init = true; }
|
||||||
|
// the two seed functions
|
||||||
|
void seed(unsigned long); // seed with 32 bit integer
|
||||||
|
void seed(const unsigned long*, int size); // seed with array
|
||||||
|
// overload operator() to make this a generator (functor)
|
||||||
|
unsigned long operator()() { return rand_int32(); }
|
||||||
|
// 2007-02-11: made the destructor virtual; thanks "double more" for pointing this out
|
||||||
|
virtual ~MTRand_int32() {} // destructor
|
||||||
|
protected: // used by derived classes, otherwise not accessible; use the ()-operator
|
||||||
|
unsigned long rand_int32(); // generate 32 bit random integer
|
||||||
|
private:
|
||||||
|
static const int n = 624, m = 397; // compile time constants
|
||||||
|
// the variables below are static (no duplicates can exist)
|
||||||
|
static unsigned long state[n]; // state vector array
|
||||||
|
static int p; // position in state array
|
||||||
|
static bool init; // true if init function is called
|
||||||
|
// private functions used to generate the pseudo random numbers
|
||||||
|
unsigned long twiddle(unsigned long, unsigned long); // used by gen_state()
|
||||||
|
void gen_state(); // generate new state
|
||||||
|
// make copy constructor and assignment operator unavailable, they don't make sense
|
||||||
|
MTRand_int32(const MTRand_int32&); // copy constructor not defined
|
||||||
|
void operator=(const MTRand_int32&); // assignment operator not defined
|
||||||
|
};
|
||||||
|
|
||||||
|
// inline for speed, must therefore reside in header file
|
||||||
|
inline unsigned long MTRand_int32::twiddle(unsigned long u, unsigned long v) {
|
||||||
|
return (((u & 0x80000000UL) | (v & 0x7FFFFFFFUL)) >> 1)
|
||||||
|
^ ((v & 1UL) * 0x9908B0DFUL);
|
||||||
|
// 2013-07-22: line above modified for performance according to http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/Ierymenko.html
|
||||||
|
// thanks Vitaliy FEOKTISTOV for pointing this out
|
||||||
|
}
|
||||||
|
|
||||||
|
inline unsigned long MTRand_int32::rand_int32() { // generate 32 bit random int
|
||||||
|
if (p == n) gen_state(); // new state vector needed
|
||||||
|
// gen_state() is split off to be non-inline, because it is only called once
|
||||||
|
// in every 624 calls and otherwise irand() would become too big to get inlined
|
||||||
|
unsigned long x = state[p++];
|
||||||
|
x ^= (x >> 11);
|
||||||
|
x ^= (x << 7) & 0x9D2C5680UL;
|
||||||
|
x ^= (x << 15) & 0xEFC60000UL;
|
||||||
|
return x ^ (x >> 18);
|
||||||
|
}
|
||||||
|
|
||||||
|
// generates double floating point numbers in the half-open interval [0, 1)
|
||||||
|
class MTRand : public MTRand_int32 {
|
||||||
|
public:
|
||||||
|
MTRand() : MTRand_int32() {}
|
||||||
|
MTRand(unsigned long seed) : MTRand_int32(seed) {}
|
||||||
|
MTRand(const unsigned long* seed, int size) : MTRand_int32(seed, size) {}
|
||||||
|
~MTRand() {}
|
||||||
|
double operator()() {
|
||||||
|
return static_cast<double>(rand_int32()) * (1. / 4294967296.); } // divided by 2^32
|
||||||
|
private:
|
||||||
|
MTRand(const MTRand&); // copy constructor not defined
|
||||||
|
void operator=(const MTRand&); // assignment operator not defined
|
||||||
|
};
|
||||||
|
|
||||||
|
// generates double floating point numbers in the closed interval [0, 1]
|
||||||
|
class MTRand_closed : public MTRand_int32 {
|
||||||
|
public:
|
||||||
|
MTRand_closed() : MTRand_int32() {}
|
||||||
|
MTRand_closed(unsigned long seed) : MTRand_int32(seed) {}
|
||||||
|
MTRand_closed(const unsigned long* seed, int size) : MTRand_int32(seed, size) {}
|
||||||
|
~MTRand_closed() {}
|
||||||
|
double operator()() {
|
||||||
|
return static_cast<double>(rand_int32()) * (1. / 4294967295.); } // divided by 2^32 - 1
|
||||||
|
private:
|
||||||
|
MTRand_closed(const MTRand_closed&); // copy constructor not defined
|
||||||
|
void operator=(const MTRand_closed&); // assignment operator not defined
|
||||||
|
};
|
||||||
|
|
||||||
|
// generates double floating point numbers in the open interval (0, 1)
|
||||||
|
class MTRand_open : public MTRand_int32 {
|
||||||
|
public:
|
||||||
|
MTRand_open() : MTRand_int32() {}
|
||||||
|
MTRand_open(unsigned long seed) : MTRand_int32(seed) {}
|
||||||
|
MTRand_open(const unsigned long* seed, int size) : MTRand_int32(seed, size) {}
|
||||||
|
~MTRand_open() {}
|
||||||
|
double operator()() {
|
||||||
|
return (static_cast<double>(rand_int32()) + .5) * (1. / 4294967296.); } // divided by 2^32
|
||||||
|
private:
|
||||||
|
MTRand_open(const MTRand_open&); // copy constructor not defined
|
||||||
|
void operator=(const MTRand_open&); // assignment operator not defined
|
||||||
|
};
|
||||||
|
|
||||||
|
// generates 53 bit resolution doubles in the half-open interval [0, 1)
|
||||||
|
class MTRand53 : public MTRand_int32 {
|
||||||
|
public:
|
||||||
|
MTRand53() : MTRand_int32() {}
|
||||||
|
MTRand53(unsigned long seed) : MTRand_int32(seed) {}
|
||||||
|
MTRand53(const unsigned long* seed, int size) : MTRand_int32(seed, size) {}
|
||||||
|
~MTRand53() {}
|
||||||
|
double operator()() {
|
||||||
|
return (static_cast<double>(rand_int32() >> 5) * 67108864. +
|
||||||
|
static_cast<double>(rand_int32() >> 6)) * (1. / 9007199254740992.); }
|
||||||
|
private:
|
||||||
|
MTRand53(const MTRand53&); // copy constructor not defined
|
||||||
|
void operator=(const MTRand53&); // assignment operator not defined
|
||||||
|
};
|
||||||
|
|
||||||
|
#endif // MTRAND_H
|
||||||
|
|
||||||
@ -0,0 +1,10 @@
|
|||||||
|
cmake_minimum_required(VERSION 3.5.1)
|
||||||
|
project(Lab2)
|
||||||
|
|
||||||
|
set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR})
|
||||||
|
|
||||||
|
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
|
||||||
|
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
|
||||||
|
|
||||||
|
set(SOURCE_FILES main.cpp)
|
||||||
|
add_executable(Lab2 ${SOURCE_FILES})
|
||||||
Binary file not shown.
@ -0,0 +1,402 @@
|
|||||||
|
|
||||||
|
std::vector<double> c = {0.806,0.517,0.1,0.908,0.965,0.669,0.524,0.902,0.351,0.876,0.462,
|
||||||
|
0.491,0.463,0.741,0.352,0.869,0.813,0.811,0.0828,0.964,0.789,0.360,0.369,
|
||||||
|
0.992,0.332,0.817,0.632,0.883,0.608,0.326};
|
||||||
|
|
||||||
|
double a[][10] =
|
||||||
|
{
|
||||||
|
{9.681,0.667,4.783,9.095,3.517,9.325,6.544,0.211,5.122,2.02},
|
||||||
|
{9.4,2.041,3.788,7.931,2.882,2.672,3.568,1.284,7.033,7.374},
|
||||||
|
{8.025,9.152,5.114,7.621,4.564,4.711,2.996,6.126,0.734,4.982},
|
||||||
|
{2.196,0.415,5.649,6.979,9.510,9.166,6.304,6.054,9.377,1.426},
|
||||||
|
{8.074,8.777,3.467,1.863,6.708,6.349,4.534,0.276,7.633,1.567},
|
||||||
|
{7.650,5.658,0.720,2.764,3.278,5.283,7.474,6.274,1.409,8.208},
|
||||||
|
{1.256,3.605,8.623,6.905,4.584,8.133,6.071,6.888,4.187,5.448},
|
||||||
|
{8.314,2.261,4.24,1.781,4.124,0.932,8.129,8.658,1.208,5.762},
|
||||||
|
{0.226,8.858,1.42,0.954,1.622,4.698,6.228,9.096,0.972,7.637},
|
||||||
|
{7.305,2.228,1.242,5.928,9.133,1.826,4.06,5.204,8.713,8.247},
|
||||||
|
{0.652,7.027,0.508,4.876,8.807,4.632,5.808,6.937,3.291,7.016},
|
||||||
|
{2.699,3.516,5.847,4.119,4.461,7.496,8.817,0.69,6.593,9.789},
|
||||||
|
{8.327,3.897,2.017,9.57,9.825,1.15,1.395,3.885,6.354,0.109},
|
||||||
|
{2.132,7.006,7.136,2.641,1.882,5.943,7.273,7.691,2.88,0.564},
|
||||||
|
{4.707,5.579,4.08,0.581,9.698,8.542,8.077,8.515,9.231,4.67},
|
||||||
|
{8.304,7.559,8.567,0.322,7.128,8.392,1.472,8.524,2.277,7.826},
|
||||||
|
{8.632,4.409,4.832,5.768,7.05,6.715,1.711,4.323,4.405,4.591},
|
||||||
|
{4.887,9.112,0.17,8.967,9.693,9.867,7.508,7.77,8.382,6.74},
|
||||||
|
{2.44,6.686,4.299,1.007,7.008,1.427,9.398,8.48,9.95,1.675},
|
||||||
|
{6.306,8.583,6.084,1.138,4.350,3.134,7.853,6.061,7.457,2.258},
|
||||||
|
{0.652,2.343,1.37,0.821,1.31,1.063,0.689,8.819,8.833,9.07},
|
||||||
|
{5.558,1.272,5.756,9.857,2.279,2.764,1.284,1.677,1.244,1.234},
|
||||||
|
{3.352,7.549,9.817,9.437,8.687,4.167,2.57,6.54,0.228,0.027},
|
||||||
|
{8.798,0.88,2.37,0.168,1.701,3.68,1.231,2.39,2.499,0.064},
|
||||||
|
{1.46,8.057,1.337,7.217,7.914,3.615,9.981,9.198,5.292,1.224},
|
||||||
|
{0.432,8.645,8.774,0.249,8.081,7.461,4.416,0.652,4.002,4.644},
|
||||||
|
{0.679,2.8,5.523,3.049,2.968,7.225,6.73,4.199,9.614,9.229},
|
||||||
|
{4.263,1.074,7.286,5.599,8.291,5.2,9.214,8.272,4.398,4.506},
|
||||||
|
{9.496,4.83,3.15,8.27,5.079,1.231,5.731,9.494,1.883,9.732},
|
||||||
|
{4.138,2.562,2.532,9.661,5.611,5.5,6.886,2.341,9.699,6.5}
|
||||||
|
};
|
||||||
|
|
||||||
|
double schwefel(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 512;
|
||||||
|
int lower_bound = -512;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += (-input[i]) * std::sin(std::sqrt(std::abs(input[i])));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double first_de_jong(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double rosenbrock(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 100 * std::pow((std::pow(input[i], 2) - input[i + 1]), 2) + std::pow((1 - input[i]), 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double rastrigin(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2) - 10 * std::cos(2 * M_PI * input[i]);
|
||||||
|
}
|
||||||
|
|
||||||
|
sum *= 2 * input.size();
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double griewangk(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2) / 4000;
|
||||||
|
}
|
||||||
|
|
||||||
|
double product = 1;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
product *= std::cos(input[i] / sqrt(i + 1));
|
||||||
|
}
|
||||||
|
|
||||||
|
return 1 + sum - product;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double sine_envelope_sine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 0.5 + (std::pow(std::sin(std::pow(input[i], 2) + std::pow(input[i + 1], 2) - 0.5), 2)) /
|
||||||
|
(1 + 0.001 * (std::pow(input[i], 2) + std::pow(input[i + 1], 2)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double stretched_v_sine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += std::pow(std::pow(input[i], 2) + std::pow(input[i + 1], 2), 1.0 / 4) *
|
||||||
|
std::pow(std::sin(50 * std::pow(std::pow(input[i], 2) + std::pow(input[i + 1], 2), 1.0 / 10)), 2) + 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double ackleys_one(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 32;
|
||||||
|
int lower_bound = -32;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += (1.0 / pow(M_E, 0.2)) *
|
||||||
|
std::sqrt(std::pow(input[i], 2) + std::pow(input[i + 1], 2)) +
|
||||||
|
3 * std::cos(2 * input[i]) +
|
||||||
|
std::sin(2 * input[i + 1]);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double ackleys_two(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 32;
|
||||||
|
int lower_bound = -32;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 20 + M_E -
|
||||||
|
(20 / (std::pow(M_E, 0.2) * std::sqrt(((std::pow(input[i], 2) + std::pow(input[i+1], 2) + 1) / 2)))) -
|
||||||
|
std::pow(M_E, 0.5 * std::cos(2 * M_PI * input[i]) + cos(2 * M_PI * input[i + 1]));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double egg_holder(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += -input[i] * std::sin(std::sqrt(abs(input[i] - input[i + 1] - 47))) -
|
||||||
|
(input[i + 1] + 47) * std::sin(std::sqrt(std::abs(input[i + 1] + 47 + input[i] / 2)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double rana(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += input[i] * std::sin(std::sqrt(std::abs(input[i + 1] - input[i] + 1))) *
|
||||||
|
std::cos(std::sqrt(std::abs(input[i + 1] + input[i] + 1))) +
|
||||||
|
(input[i + 1] + 1) *
|
||||||
|
std::cos(std::sqrt(std::abs(input[i + 1] - input[i] + 1))) *
|
||||||
|
std::sin(std::sqrt(std::abs(input[i + 1] + input[i] + 1)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double pathological(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += 0.5 +
|
||||||
|
(std::pow(std::sin(std::sqrt(100 * std::pow(input[i], 2) + std::pow(input[i + 1], 2))), 2) - 0.5) /
|
||||||
|
(1 + 0.001 * std::pow(std::pow(input[i], 2) - 2 * input[i] * input[i + 1] + std::pow(input[i + 1], 2), 2));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double michalewicz(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = M_PI;
|
||||||
|
int lower_bound = 0;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += std::sin(input[i]) * std::pow(std::sin(i * std::pow(input[i], 2) / M_PI), 20);
|
||||||
|
}
|
||||||
|
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double masters_cosine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += std::pow(M_E, -(1/8) * (std::pow(input[i], 2) + std::pow(input[i + 1], 2) + 0.5 * input[i + 1] * input[i])) *
|
||||||
|
std::cos(4 * std::sqrt(std::pow(input[i], 2) + std::pow(input[i + 1], 2) + 0.5 * input[i] * input[i + 1]));
|
||||||
|
}
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double shekels_foxholes(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 10;
|
||||||
|
int lower_bound = 0;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < c.size() - 1; i++) {
|
||||||
|
|
||||||
|
double bottom_sum = 0;
|
||||||
|
|
||||||
|
for (int q = 0; q < input.size(); q++){
|
||||||
|
bottom_sum = std::pow(input.at(q) - a[i][q], 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
sum += 1 / (bottom_sum + c[i]);
|
||||||
|
}
|
||||||
|
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double set_within(double val, double prior_upper, double prior_lower, double after_upper, double after_lower){
|
||||||
|
return ((after_upper - after_lower) * (val - prior_lower) / (prior_upper - prior_lower)) + after_lower;
|
||||||
|
}
|
||||||
|
|
||||||
|
struct function {
|
||||||
|
|
||||||
|
double (*function_pointer)(std::vector<double>);
|
||||||
|
|
||||||
|
double range = 0;
|
||||||
|
double upper_bound = 0;
|
||||||
|
double lower_bound = 0;
|
||||||
|
|
||||||
|
timer t;
|
||||||
|
|
||||||
|
function(){};
|
||||||
|
|
||||||
|
function(double (*func)(std::vector<double>), double upper_bound, double lower_bound) {
|
||||||
|
function_pointer = func;
|
||||||
|
this->upper_bound = upper_bound;
|
||||||
|
this->lower_bound = lower_bound;
|
||||||
|
}
|
||||||
|
|
||||||
|
double compute(std::vector<double> input) {
|
||||||
|
|
||||||
|
for (auto v: input) {
|
||||||
|
if (v <= lower_bound && v >= upper_bound) {
|
||||||
|
std::cout << "Function exceeded bounds";
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
double res = function_pointer(input);
|
||||||
|
return res;
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
// enum Test_Data {
|
||||||
|
// MEAN, MEDIAN, DEVIATION, AVG_TIME, DIMENSIONALIY, UPPER_RANGE, LOWER_RANGE
|
||||||
|
// };
|
||||||
|
//
|
||||||
|
// std::vector<double> random_walk(int dimensionality, int permutations){
|
||||||
|
//
|
||||||
|
// MTRand rng();
|
||||||
|
//
|
||||||
|
// std::vector<double> times;
|
||||||
|
// std::vector<double> vals;
|
||||||
|
//
|
||||||
|
// double best_value = 99999999999;
|
||||||
|
//
|
||||||
|
// t.start();
|
||||||
|
//
|
||||||
|
// for (int i = 0; i < permutations; i++) {
|
||||||
|
//
|
||||||
|
// std::vector<double> dimension_vals;
|
||||||
|
//
|
||||||
|
// for (int i = 0; i < dimensionality; i++) {
|
||||||
|
// dimension_vals.push_back(fmod(rand(), (upper_bound * 2)) + lower_bound);
|
||||||
|
// }
|
||||||
|
//
|
||||||
|
//
|
||||||
|
// double res = compute(dimension_vals);
|
||||||
|
// if (res < best_value){
|
||||||
|
// best_value = res;
|
||||||
|
// }
|
||||||
|
// }
|
||||||
|
//
|
||||||
|
// vals.push_back(best_value);
|
||||||
|
//
|
||||||
|
// t.end();
|
||||||
|
//
|
||||||
|
// times.push_back(t.duration());
|
||||||
|
//
|
||||||
|
// // Mean
|
||||||
|
// double mean = 0;
|
||||||
|
// for (double v: vals) {
|
||||||
|
// mean += v;
|
||||||
|
// }
|
||||||
|
// mean /= vals.size();
|
||||||
|
//
|
||||||
|
// // Median
|
||||||
|
// double median = vals[std::floor(vals.size() / 2)];
|
||||||
|
//
|
||||||
|
// // Standard Deviation
|
||||||
|
// double sum = 0;
|
||||||
|
// for (double v: vals) {
|
||||||
|
// sum += std::pow(v - mean, 2);
|
||||||
|
// }
|
||||||
|
// double deviation = std::sqrt(sum / vals.size());
|
||||||
|
//
|
||||||
|
// // Time Mean
|
||||||
|
// double time_mean = 0;
|
||||||
|
// for (double v: times) {
|
||||||
|
// time_mean += v;
|
||||||
|
// }
|
||||||
|
// time_mean /= times.size();
|
||||||
|
//
|
||||||
|
// // Range
|
||||||
|
// std::sort(vals.begin(), vals.end());
|
||||||
|
//
|
||||||
|
// double lower_range = vals.front();
|
||||||
|
// double upper_range = vals.back();
|
||||||
|
//
|
||||||
|
// std::map<Test_Data, double> ret;
|
||||||
|
//
|
||||||
|
// ret[Test_Data::DIMENSIONALIY] = dimensionality;
|
||||||
|
// ret[Test_Data::AVG_TIME] = time_mean;
|
||||||
|
// ret[Test_Data::DEVIATION] = deviation;
|
||||||
|
// ret[Test_Data::MEAN] = mean;
|
||||||
|
// ret[Test_Data::MEDIAN] = median;
|
||||||
|
// ret[Test_Data::UPPER_RANGE] = upper_range;
|
||||||
|
// ret[Test_Data::LOWER_RANGE] = lower_range;
|
||||||
|
//
|
||||||
|
// // std::cout << " & " << dimensionality << " & " << mean << " & " << median << " & " << deviation << " & "
|
||||||
|
// // << time_mean << " \\\\ \\hline" << std::endl;
|
||||||
|
//
|
||||||
|
// return ret;
|
||||||
|
// };
|
||||||
|
|
||||||
|
};
|
||||||
@ -0,0 +1,65 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class iterative_local_search : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
iterative_local_search(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
// Set up random start
|
||||||
|
std::vector<double> global_best_solution;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
global_best_solution.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
// 30 iteration max
|
||||||
|
int iteration_max = 30;
|
||||||
|
for (int i = 0; i < iteration_max; i++){
|
||||||
|
|
||||||
|
// Random new solution
|
||||||
|
std::vector<double> best_solution;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
best_solution.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<double> temp_solution = best_solution;
|
||||||
|
|
||||||
|
// While a better solution is still being found
|
||||||
|
bool better_solution_found = true;
|
||||||
|
while (better_solution_found) {
|
||||||
|
|
||||||
|
better_solution_found = false;
|
||||||
|
double delta = 0.11;
|
||||||
|
|
||||||
|
temp_solution = best_solution;
|
||||||
|
std::vector<double> new_solution(dimensionality);
|
||||||
|
|
||||||
|
// Set up the new solution
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
|
||||||
|
temp_solution[i] += delta;
|
||||||
|
new_solution[i] = best_solution[i] - delta * (func.compute(temp_solution) - func.compute(best_solution));
|
||||||
|
temp_solution[i] = best_solution[i];
|
||||||
|
// temp[i] - delta * new with delta, and the old without
|
||||||
|
}
|
||||||
|
|
||||||
|
// test it
|
||||||
|
if (func.compute(new_solution) < func.compute(best_solution)) {
|
||||||
|
best_solution = new_solution;
|
||||||
|
better_solution_found = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Check to see if we found a better global solution
|
||||||
|
if (func.compute(best_solution) < func.compute(global_best_solution)){
|
||||||
|
global_best_solution = best_solution;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,53 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class local_search : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
local_search(function f) : search_function(f) {
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
// Set up the initial soution
|
||||||
|
std::vector<double> best_solution;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
best_solution.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<double> temp_solution = best_solution;
|
||||||
|
|
||||||
|
// While a better solution is being found
|
||||||
|
bool better_solution_found = true;
|
||||||
|
while (better_solution_found) {
|
||||||
|
|
||||||
|
better_solution_found = false;
|
||||||
|
double delta = 0.11;
|
||||||
|
|
||||||
|
temp_solution = best_solution;
|
||||||
|
std::vector<double> new_solution(dimensionality);
|
||||||
|
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
|
||||||
|
temp_solution[i] += delta;
|
||||||
|
new_solution[i] = best_solution[i] - delta * (func.compute(temp_solution) - func.compute(best_solution));
|
||||||
|
temp_solution[i] = best_solution[i];
|
||||||
|
// temp[i] - delta * new with delta, and the old without
|
||||||
|
}
|
||||||
|
|
||||||
|
// Check to see if we found a better solution
|
||||||
|
if (func.compute(new_solution) < func.compute(best_solution)) {
|
||||||
|
best_solution = new_solution;
|
||||||
|
better_solution_found = true;
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
return func.compute(best_solution);
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,79 @@
|
|||||||
|
#include <iostream>
|
||||||
|
#include <vector>
|
||||||
|
#include <cmath>
|
||||||
|
#include <map>
|
||||||
|
#include <chrono>
|
||||||
|
#include <cstring>
|
||||||
|
#include "twister.c"
|
||||||
|
#include "util.hpp"
|
||||||
|
#include "functions.hpp"
|
||||||
|
#include "random_walk.hpp"
|
||||||
|
#include "local_search.hpp"
|
||||||
|
#include "iterative_local_search.hpp"
|
||||||
|
|
||||||
|
|
||||||
|
// for each i in demension vals:
|
||||||
|
// new solution[i] = best[i] - delta *(f(new) - f(old))
|
||||||
|
//
|
||||||
|
// if new solution better, set it as solution
|
||||||
|
|
||||||
|
int main(int argc, char* args[]) {
|
||||||
|
|
||||||
|
std::map<int, function> function_lookup;
|
||||||
|
|
||||||
|
function_lookup.emplace(std::make_pair(0, function(&schwefel, 512, -512)));
|
||||||
|
function_lookup.emplace(std::make_pair(1, function(&first_de_jong, 100, -100)));
|
||||||
|
function_lookup.emplace(std::make_pair(2, function(&rosenbrock, 100, -100)));
|
||||||
|
function_lookup.emplace(std::make_pair(3, function(&rastrigin, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(4, function(&griewangk, 500, -500)));
|
||||||
|
function_lookup.emplace(std::make_pair(5, function(&sine_envelope_sine_wave, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(6, function(&stretched_v_sine_wave, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(7, function(&ackleys_one, 32, -32)));
|
||||||
|
function_lookup.emplace(std::make_pair(8, function(&ackleys_two, 32, -32)));
|
||||||
|
function_lookup.emplace(std::make_pair(9, function(&egg_holder, 500, -500)));
|
||||||
|
function_lookup.emplace(std::make_pair(10, function(&rana, 500, -500)));
|
||||||
|
function_lookup.emplace(std::make_pair(11, function(&pathological, 100, -100)));
|
||||||
|
function_lookup.emplace(std::make_pair(12, function(&michalewicz, M_PI, 0)));
|
||||||
|
function_lookup.emplace(std::make_pair(13, function(&masters_cosine_wave, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(14, function(&shekels_foxholes, 10, 0)));
|
||||||
|
|
||||||
|
function f;
|
||||||
|
int dimensionality = 0;
|
||||||
|
double seed = 0;
|
||||||
|
int search_function = 0;
|
||||||
|
|
||||||
|
// Get the command line args
|
||||||
|
if (argc == 1){
|
||||||
|
char arg_str[200];
|
||||||
|
std::cin.get(arg_str, 200);
|
||||||
|
char t = ' ';
|
||||||
|
|
||||||
|
f = function_lookup[atoi(strtok(arg_str, &t))];
|
||||||
|
dimensionality = atoi(strtok(NULL, &t));
|
||||||
|
seed = atoi(strtok(NULL, &t));
|
||||||
|
search_function = atoi(strtok(NULL, &t));
|
||||||
|
|
||||||
|
} else {
|
||||||
|
|
||||||
|
f = function_lookup[atoi(args[1])];
|
||||||
|
dimensionality = atoi(args[2]);
|
||||||
|
seed = atoi(args[3]);
|
||||||
|
search_function = atoi(args[4]);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Set up the search functions
|
||||||
|
seedMT(seed);
|
||||||
|
random_walk r_w(f);
|
||||||
|
local_search l_s(f);
|
||||||
|
iterative_local_search it_s(f);
|
||||||
|
|
||||||
|
// return the results of the search
|
||||||
|
if (search_function == 0)
|
||||||
|
std::cout << r_w.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 1)
|
||||||
|
std::cout << l_s.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 2)
|
||||||
|
std::cout << it_s.search(1, dimensionality) << std::endl;
|
||||||
|
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
@ -0,0 +1,41 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
#include <algorithm>
|
||||||
|
|
||||||
|
class random_walk : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
random_walk(function f) : search_function(f) {
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
timer t;
|
||||||
|
t.start();
|
||||||
|
|
||||||
|
std::vector<double> r;
|
||||||
|
for (int i = 0; i < permutations; i++){
|
||||||
|
|
||||||
|
std::vector<double> dimension_vals;
|
||||||
|
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
auto val = fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound;
|
||||||
|
dimension_vals.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
r.push_back(func.compute(dimension_vals));
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
t.end();
|
||||||
|
|
||||||
|
std::sort(r.begin(), r.end(), std::less<double>());
|
||||||
|
|
||||||
|
return r[0];
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 39 KiB |
@ -0,0 +1,71 @@
|
|||||||
|
import matplotlib.pyplot as plt
|
||||||
|
|
||||||
|
plt.plot([
|
||||||
|
7.08019,
|
||||||
|
7.76125,
|
||||||
|
7.78901,
|
||||||
|
6.86725,
|
||||||
|
6.89327,
|
||||||
|
6.66617,
|
||||||
|
6.62795,
|
||||||
|
6.89146,
|
||||||
|
7.93945,
|
||||||
|
6.32002,
|
||||||
|
6.37838,
|
||||||
|
6.39102,
|
||||||
|
6.52912,
|
||||||
|
6.13898,
|
||||||
|
6.46143,
|
||||||
|
6.20752,
|
||||||
|
6.24721,
|
||||||
|
7.4823,
|
||||||
|
7.44849,
|
||||||
|
7.1168,
|
||||||
|
6.44656,
|
||||||
|
7.13644,
|
||||||
|
7.83785,
|
||||||
|
6.90917,
|
||||||
|
6.41901,
|
||||||
|
6.83155,
|
||||||
|
6.60604,
|
||||||
|
7.09917,
|
||||||
|
6.27536,
|
||||||
|
6.07117
|
||||||
|
])
|
||||||
|
|
||||||
|
plt.plot([
|
||||||
|
7.08019,
|
||||||
|
7.08019,
|
||||||
|
7.08019,
|
||||||
|
6.86725,
|
||||||
|
6.86725,
|
||||||
|
6.66617,
|
||||||
|
6.62795,
|
||||||
|
6.62795,
|
||||||
|
6.62795,
|
||||||
|
6.32002,
|
||||||
|
6.32002,
|
||||||
|
6.32002,
|
||||||
|
6.32002,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.13898,
|
||||||
|
6.07117
|
||||||
|
])
|
||||||
|
|
||||||
|
|
||||||
|
plt.title("Sine Envelope Sine Wave")
|
||||||
|
plt.show()
|
||||||
|
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|
|||||||
|
import matplotlib.pyplot as plt
|
||||||
|
|
||||||
|
plt.plot([
|
||||||
|
-8.69983,
|
||||||
|
-8.69546,
|
||||||
|
-7.02473,
|
||||||
|
-8.70105,
|
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|
-8.74989,
|
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|
-8.83741,
|
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|
-8.26651,
|
||||||
|
-8.83748,
|
||||||
|
-8.65732,
|
||||||
|
-8.85348,
|
||||||
|
-8.6054,
|
||||||
|
-8.8361,
|
||||||
|
-8.53024,
|
||||||
|
-8.33546,
|
||||||
|
-8.64118,
|
||||||
|
-8.82631,
|
||||||
|
-8.7925,
|
||||||
|
-8.7356,
|
||||||
|
-8.81997,
|
||||||
|
-8.72815,
|
||||||
|
-8.80891,
|
||||||
|
-8.55486,
|
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|
-8.83265,
|
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|
-8.75692,
|
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|
-8.82736,
|
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|
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|
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|
-8.76973,
|
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|
-8.60752,
|
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|
-8.8201,
|
||||||
|
-8.84716
|
||||||
|
])
|
||||||
|
|
||||||
|
plt.plot([
|
||||||
|
-8.69983,
|
||||||
|
-8.69546,
|
||||||
|
-8.69546,
|
||||||
|
-8.70105,
|
||||||
|
-8.74989,
|
||||||
|
-8.83741,
|
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|
-8.83741,
|
||||||
|
-8.83748,
|
||||||
|
-8.83748,
|
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|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348,
|
||||||
|
-8.85348
|
||||||
|
|
||||||
|
])
|
||||||
|
|
||||||
|
plt.title("Pathological Function")
|
||||||
|
plt.show()
|
||||||
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|
|||||||
|
dim=10
|
||||||
@ -0,0 +1,113 @@
|
|||||||
|
from subprocess import check_output
|
||||||
|
import subprocess
|
||||||
|
import random
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import time
|
||||||
|
|
||||||
|
random.seed()
|
||||||
|
|
||||||
|
loc = "../build/Lab2"
|
||||||
|
|
||||||
|
f = open('out','w')
|
||||||
|
|
||||||
|
function = "1"
|
||||||
|
|
||||||
|
print("dim=10")
|
||||||
|
f.write("dim=10\n")
|
||||||
|
for q in range(0, 30):
|
||||||
|
|
||||||
|
subprocess.call([
|
||||||
|
loc,
|
||||||
|
str(13),
|
||||||
|
str(10),
|
||||||
|
str(random.randint(0, 2147483646)),
|
||||||
|
function
|
||||||
|
])
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#print("dim=10")
|
||||||
|
#f.write("dim=10\n")
|
||||||
|
#for q in range(0, 15):
|
||||||
|
# if q == 4 or q == 12:
|
||||||
|
# continue
|
||||||
|
# print(q)
|
||||||
|
# start = time.time()
|
||||||
|
#
|
||||||
|
# subprocess.call([
|
||||||
|
# loc,
|
||||||
|
# str(q),
|
||||||
|
# str(10),
|
||||||
|
# str(random.randint(0, 2147483646)),
|
||||||
|
# function
|
||||||
|
# ])
|
||||||
|
#
|
||||||
|
# end = time.time()
|
||||||
|
# f.write(str(end-start) + "\n")
|
||||||
|
# print(end - start)
|
||||||
|
#
|
||||||
|
#print("dim=20")
|
||||||
|
#f.write("dim=20\n")
|
||||||
|
#for q in range(0, 15):
|
||||||
|
# if q == 4 or q == 12:
|
||||||
|
# continue
|
||||||
|
# print(q)
|
||||||
|
# start = time.time()
|
||||||
|
#
|
||||||
|
# subprocess.call([
|
||||||
|
# loc,
|
||||||
|
# str(q),
|
||||||
|
# str(20),
|
||||||
|
# str(random.randint(0, 2147483646)),
|
||||||
|
# function
|
||||||
|
# ])
|
||||||
|
#
|
||||||
|
# end = time.time()
|
||||||
|
# f.write(str(end-start) + "\n")
|
||||||
|
# print(end - start)
|
||||||
|
|
||||||
|
#print("dim=30")
|
||||||
|
#f.write("dim=30\n")
|
||||||
|
#for q in range(0, 15):
|
||||||
|
# if q == 4:
|
||||||
|
# continue
|
||||||
|
# print(q)
|
||||||
|
# start = time.time()
|
||||||
|
#
|
||||||
|
# subprocess.call([
|
||||||
|
# loc,
|
||||||
|
# str(q),
|
||||||
|
# str(30),
|
||||||
|
# str(random.randint(0, 2147483646)),
|
||||||
|
# function
|
||||||
|
# ])
|
||||||
|
#
|
||||||
|
# end = time.time()
|
||||||
|
# f.write(str(end-start) + "\n")
|
||||||
|
# print(end - start)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
f.close()
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
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|
|||||||
|
\relax
|
||||||
|
\providecommand*\new@tpo@label[2]{}
|
||||||
|
\select@language{english}
|
||||||
|
\@writefile{toc}{\select@language{english}}
|
||||||
|
\@writefile{lof}{\select@language{english}}
|
||||||
|
\@writefile{lot}{\select@language{english}}
|
||||||
|
\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}}
|
||||||
|
\@writefile{toc}{\contentsline {section}{\numberline {2}Methods}{1}}
|
||||||
|
\@writefile{toc}{\contentsline {section}{\numberline {3}Analysis}{1}}
|
||||||
|
\@writefile{toc}{\contentsline {section}{\numberline {4}Conclusion}{2}}
|
||||||
|
\@writefile{toc}{\contentsline {section}{\numberline {5}Results}{4}}
|
||||||
|
\@writefile{toc}{\contentsline {section}{\numberline {6}Previous Results}{14}}
|
||||||
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|
|||||||
|
This is pdfTeX, Version 3.14159265-2.6-1.40.17 (TeX Live 2016) (preloaded format=pdflatex 2016.10.20) 24 OCT 2016 16:15
|
||||||
|
entering extended mode
|
||||||
|
restricted \write18 enabled.
|
||||||
|
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|
||||||
|
**writeup.tex
|
||||||
|
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|
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|
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|
||||||
|
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|
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|
||||||
|
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|
||||||
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|
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|
Package: scrkbase 2016/06/14 v3.21 KOMA-Script package (KOMA-Script-dependent b
|
||||||
|
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|
||||||
|
|
||||||
|
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|
||||||
|
Package: scrbase 2016/06/14 v3.21 KOMA-Script package (KOMA-Script-independent
|
||||||
|
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|
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|
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|
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|
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|
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|
||||||
|
|
||||||
|
Package scrlfile, 2016/06/14 v3.21 KOMA-Script package (loading files)
|
||||||
|
Copyright (C) Markus Kohm
|
||||||
|
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
\scr@dte@tocline@numbox=\box26
|
||||||
|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
|
||||||
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|
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|
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|
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|
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|
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|
Package typearea, 2016/06/14 v3.21 KOMA-Script package (type area)
|
||||||
|
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|
||||||
|
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|
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|
|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
||||||
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
||||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
(grfext) [.png,.pdf,.jpg,.mps,.jpeg,.jbig2,.jb2,.PNG,.PDF,.JPG,.JPE
|
||||||
|
G,.JBIG2,.JB2,.eps]
|
||||||
|
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|
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|
|
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|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
)
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
)
|
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|
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|
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|
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|
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|
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.
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|
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|
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|
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|
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|
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|
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|
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\title{
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%\vspace{-1in}
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\normalfont \normalsize \textsc{Central Washington University of the Computer Science Department} \\ [25pt]
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\horrule{0.5pt} \\[0.4cm]
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\huge Project 2 \\
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\horrule{2pt} \\[0.5cm]
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}
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\author{\normalsize Mitchell Hansen \\[-6pt]}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{document}
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\maketitle
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|
||||||
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\section{Introduction}
|
||||||
|
For this lab we took the 15 functions that we programmed in the previous
|
||||||
|
lab and ran them through 3 different optimization functions, each more
|
||||||
|
accurate than the previous. We have random search, which blindly tests
|
||||||
|
randomized solutions looking for an optimum. Secondly we have local search,
|
||||||
|
which takes an initial randomized solution and then attempts to optimize it
|
||||||
|
until it's at its minimum. Thirdly we have iterative local search, which
|
||||||
|
combines the two previous functions.
|
||||||
|
|
||||||
|
\section{Methods}
|
||||||
|
A significant portion of the code from the previous lab was rewritten to
|
||||||
|
allow the functions and search methods to be run from the command line.
|
||||||
|
Arguments specifying the dimensionality of the solution, id of the function,
|
||||||
|
id of the search method, and a seed are all handled by the program. Using
|
||||||
|
this backbone, we wrote a trivial python script that executes each search
|
||||||
|
method on all of the 15 functions being tested for each dimensionality.
|
||||||
|
There currently is an issue with run times being significant for a select
|
||||||
|
few functions on high dimensionalties. As a result some data points have
|
||||||
|
been omitted.
|
||||||
|
|
||||||
|
\section{Analysis}
|
||||||
|
There were various interesting results both in the new data, what the new
|
||||||
|
functions were able to find in terms of minimums, and how close some data
|
||||||
|
points got to the last lab where the search was purely random.
|
||||||
|
|
||||||
|
Comparing the new data from the Iterative Local Search (ILS) and the Local
|
||||||
|
Search (LS) with the previous results, we see that the purely naive method
|
||||||
|
that we used previously is actually quite sufficient for a few select functions,
|
||||||
|
namely: Sine Envelope Sine Wave, Pathological, Rosenbrok, and Ackleys
|
||||||
|
Two functions. Each of these functions evaluated to very similar solutions
|
||||||
|
in all three methods, naive, ILS and LS. Often being within 10\% of each other.
|
||||||
|
|
||||||
|
The differences between the two new methods used in this lab, ILS and LS
|
||||||
|
are mainly negligible in their cumulative accuracy. There are
|
||||||
|
some examples where the search methods differ more than others. Griegwangk
|
||||||
|
and Egg Holder differ the most between the two methods, with a 100 - 200 \%
|
||||||
|
difference seen between the methods. For single runs of the functions though,
|
||||||
|
ILS is superior to LS as can be seen in the graphics below for two separate
|
||||||
|
runs of ILS on differing functions. The top line being the single run results,
|
||||||
|
and the bottom being the running best solution.
|
||||||
|
|
||||||
|
|
||||||
|
\scalebox{0.4}{\includegraphics{figure_1}}
|
||||||
|
\scalebox{0.4}{\includegraphics{figure_2}}
|
||||||
|
|
||||||
|
|
||||||
|
There were also a few problems with the experimentation, one being the fact that
|
||||||
|
we neglected the fact that the delta value within the LS and ILS functions could
|
||||||
|
throw the function outside of its specified bounds. The implementation checked
|
||||||
|
each of these bounds each function call, but only returned 0 if it exceeded them.
|
||||||
|
Thus some results have erroneous values of either 0 or some other integer value.
|
||||||
|
|
||||||
|
Another problem, as mentioned again in the conclusion, is the run time of these
|
||||||
|
search methods. In particular, function 5 and 13 ran extremely slow. Slow enough
|
||||||
|
for the results having to be omitted as it would take longer to obtain the results
|
||||||
|
than we have time for the lab. We hope to speed up the implementation prior to the
|
||||||
|
next lab and include those results then.
|
||||||
|
|
||||||
|
All testing was done on an i7-3630QM with 16GB ram using a single thread.
|
||||||
|
Complete results can be viewed in the sections below.
|
||||||
|
|
||||||
|
\section{Conclusion}
|
||||||
|
The search functions in this lab seem to behave much more accurately than the
|
||||||
|
random search in the previous lab. Not only are the new functions seemingly accurate,
|
||||||
|
but they also appear to repeat their values consistently giving the appearance that
|
||||||
|
they are coming up with a somewhat correct answer. Unfortunately, it seems where these
|
||||||
|
new functions fall down is in their performance. In the 30 dimension trials for this lab,
|
||||||
|
there were multiple functions where results had to be omitted because of running time issues.
|
||||||
|
Some taking up to multiple hours to run for their full permutation count. We hope to see
|
||||||
|
functions which take care of these issues in the upcoming labs.
|
||||||
|
|
||||||
|
\section{Results}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Random Search, 10 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
& -1223.19 & 13286 & 3.18E+09 & 24260 & 114.244 & 5.52567 & 18.2586 & 154.502 & 145.101 & -1714.21 & -1147.36 & 3.95561 & -1.64259 & -3.31459 & -19.1981 \\
|
||||||
|
& -1399.06 & 19906 & 1.96E+09 & 16700 & 90.6946 & 6.07905 & 20.4112 & 102.177 & 142.077 & -1648.81 & -1560.96 & 4.09041 & -1.39102 & -4.9016 & -19.1981 \\
|
||||||
|
& -1010.48 & 14917 & 6.76E+09 & 24520 & 51.8071 & 6.28399 & 21.7189 & 125.452 & 138.122 & -1661.62 & -1122.59 & 4.06708 & -1.34332 & -5.57971 & -19.1981 \\
|
||||||
|
& -1934.21 & 13942 & 4.96E+09 & 29540 & 122.229 & 5.67277 & 19.682 & 123.082 & 139.72 & -2343.48 & -1408.66 & 4.03919 & -1.86534 & -4.43512 & -19.1981 \\
|
||||||
|
& -794.372 & 14642 & 3.77E+09 & 19260 & 51.4992 & 5.64587 & 17.3606 & 130.231 & 146.019 & -1927.46 & -1392.74 & 4.02665 & -0.919067 & -2.9242 & -19.1981 \\
|
||||||
|
& -1431.49 & 10213 & 5.51E+09 & 15600 & 99.3078 & 6.37517 & 19.3915 & 106.105 & 147.534 & -1693.06 & -1590.86 & 3.81288 & -1.94752 & -4.41244 & -19.1981 \\
|
||||||
|
& -1222.58 & 14061 & 6.24E+09 & 27600 & 61.8407 & 5.95916 & 21.8219 & 134.971 & 138.811 & -1120.32 & -1274.41 & 3.90444 & -0.949296 & -5.21336 & -19.1981 \\
|
||||||
|
& -1659.45 & 14265 & 3.86E+09 & 12040 & 77.3118 & 6.59486 & 19.6264 & 107.374 & 137.152 & -2115.97 & -1151.79 & 3.9272 & -2.01492 & -4.14032 & -10.3861 \\
|
||||||
|
& -1495.15 & 16757 & 7.43E+09 & 29540 & 90.1975 & 5.82742 & 17.0893 & 118.252 & 133.507 & -1385.32 & -1145.69 & 4.03879 & -1.59431 & -4.89982 & -19.1981 \\
|
||||||
|
& -869.356 & 16426 & 4.32E+09 & 32060 & 34.4248 & 5.79818 & 19.4186 & 111.549 & 133.483 & -1677.77 & -859.394 & 4.00669 & -0.948425 & -5.22481 & -13.4604 \\
|
||||||
|
& -1039.51 & 18099 & 3.32E+09 & 34560 & 91.9329 & 6.26357 & 21.1327 & 114.807 & 140.81 & -1668.54 & -1128.23 & 4.07449 & -1.05552 & -3.01325 & -19.1981 \\
|
||||||
|
& -919.612 & 12176 & 6.94E+09 & 25180 & 90.0024 & 6.26924 & 16.2619 & 113.944 & 128.653 & -2016.45 & -873.684 & 4.06288 & -1.4206 & -4.45688 & -19.1981 \\
|
||||||
|
& -1506.41 & 14320 & 5.21E+08 & 31120 & 108.815 & 6.47541 & 18.8506 & 92.2691 & 142.909 & -2022.87 & -1605.81 & 4.00233 & -1.76635 & -2.84643 & -19.1981 \\
|
||||||
|
& -1418.55 & 20724 & 2.01E+09 & 26780 & 82.2471 & 6.03665 & 19.3898 & 106.227 & 133.068 & -2409.09 & -1688.21 & 4.16777 & -2.26334 & -4.32598 & -19.1981 \\
|
||||||
|
& -1011.89 & 18565 & 7.75E+09 & 23560 & 95.8855 & 6.32915 & 17.2525 & 114.955 & 133.399 & -3479.56 & -988.721 & 4.03036 & -1.32162 & -4.01327 & -19.1981 \\
|
||||||
|
& -1408.31 & 15157 & 5.16E+09 & 23300 & 85.4808 & 5.70288 & 18.6271 & 150.783 & 138.84 & -2023.31 & -1078.79 & 4.00918 & -1.95085 & -4.44009 & -19.1981 \\
|
||||||
|
& -748.024 & 23811 & 2.54E+09 & 18940 & 147.794 & 5.43586 & 20.034 & 137.888 & 138.61 & -2389.56 & -1342.49 & 3.70141 & -1.36667 & -4.68997 & -19.1981 \\
|
||||||
|
& -1638.56 & 18165 & 5.13E+08 & 15660 & 116.448 & 5.86329 & 18.6616 & 114.843 & 144.891 & -2692.09 & -1348.9 & 4.1051 & -2.70373 & -4.78687 & -19.1981 \\
|
||||||
|
& -840.918 & 20571 & 5.90E+09 & 29040 & 88.0594 & 6.07913 & 21.4554 & 108.89 & 148.508 & -1373.66 & -709.318 & 4.16242 & -1.27512 & -3.5624 & -19.1981 \\
|
||||||
|
& -918.388 & 19467 & 5.67E+09 & 18540 & 86.3758 & 5.50249 & 16.6631 & 117.034 & 138.953 & -1841.87 & -1118.65 & 4.05446 & -1.43019 & -4.92021 & -19.1981 \\
|
||||||
|
& -1914.4 & 14579 & 4.51E+09 & 35380 & 112.945 & 6.45106 & 17.4196 & 113.865 & 137.99 & -1295.97 & -1449.41 & 4.04415 & -1.21765 & -3.82571 & -19.1981 \\
|
||||||
|
& -1152.74 & 16613 & 8.29E+09 & 27140 & 93.3062 & 6.2383 & 18.8128 & 120.968 & 143.59 & -1384.85 & -1405.02 & 4.21014 & -0.963691 & -5.42752 & -13.344 \\
|
||||||
|
& -1484.78 & 18381 & 5.35E+09 & 36160 & 58.6201 & 6.30713 & 17.9042 & 130.23 & 135.182 & -1930.82 & -1426.85 & 4.00492 & -1.37802 & -4.93164 & -19.1981 \\
|
||||||
|
& -1049.66 & 8486 & 3.92E+09 & 19580 & 112.332 & 6.55806 & 19.9514 & 141.112 & 135.308 & -1799.74 & -884.562 & 4.23598 & -2.511 & -3.99085 & -13.4604 \\
|
||||||
|
& -1093.92 & 16661 & 6.44E+09 & 24500 & 92.3324 & 6.30898 & 20.232 & 121.163 & 130.104 & -1710.14 & -967.321 & 4.06013 & -2.29801 & -6.0234 & -19.1981 \\
|
||||||
|
& -1412.25 & 18188 & 5.81E+09 & 26880 & 109.978 & 5.70144 & 13.2134 & 90.171 & 134.353 & -1785.93 & -819.994 & 3.77693 & -1.0307 & -5.92079 & -19.1981 \\
|
||||||
|
& -1186.11 & 13189 & 3.77E+09 & 19320 & 106.128 & 6.27975 & 18.8899 & 136.097 & 146.582 & -1703.69 & -1298.35 & 3.73847 & -0.944601 & -3.52666 & -19.1981 \\
|
||||||
|
& -1356.38 & 18343 & 2.37E+09 & 13500 & 65.7106 & 5.97634 & 21.5128 & 115.854 & 140.762 & -2070.81 & -1319.27 & 4.18465 & -1.24715 & -5.7363 & -19.1981 \\
|
||||||
|
& -1528.85 & 17689 & 1.03E+10 & 18840 & 107.888 & 6.00707 & 15.2945 & 94.1472 & 144.7 & -3449.27 & -1375.83 & 4.14164 & -1.64993 & -3.02796 & -19.1981 \\
|
||||||
|
& -1063.76 & 15783 & 2.62E+09 & 32600 & 97.6585 & 6.46626 & 22.6138 & 123.843 & 131.62 & -1700.03 & -1291.51 & 4.01155 & -1.00427 & -4.24119 & -19.1981 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -1257.7453333333 & 16246.0666666667 & 4.72E+09 & 2.44E+04 & 9.14E+01 & 6.07E+00 & 1.90E+01 & 1.19E+02 & 1.39E+02 & -1.93E+03 & -1.23E+03 & 4.02E+00 & -1.51E+00 & -4.43E+00 & -1.83E+01 \\
|
||||||
|
Med. & -1222.885 & 16519.5 & 4735540000 & 24510 & 92.13265 & 6.07909 & 19.13985 & 116.444 & 138.8255 & -1792.835 & -1282.96 & 4.03899 & -1.38452 & -4.437605 & -19.1981 \\
|
||||||
|
Std. Dev. & 315.9494375281 & 3243.999978032 & 2288019739.0926 & 6682.1279905999 & 24.08152579 & 0.3362814731 & 2.0863159585 & 15.7480637114 & 5.4144432384 & 542.1878355628 & 253.2605529849 & 0.1313044821 & 0.4955181247 & 0.901789508 & 2.3117761941 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Random Search, 20 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
& -1910.87 & 40054 & 1.87E+10 & 122800 & 316.532 & 13.3092 & 54.0294 & 272.443 & 302.424 & -4041.92 & -1817.15 & 8.84883 & -2.46942 & -5.56505 & -12.4756 \\
|
||||||
|
& -1696.99 & 45772 & 7.63E+09 & 147520 & 297.122 & 14.295 & 50.705 & 272.861 & 303.698 & -4286.27 & -2370.93 & 8.81079 & -1.86537 & -5.17117 & -14.6404 \\
|
||||||
|
& -3381.59 & 40944 & 1.07E+10 & 111960 & 261.007 & 14.2375 & 48.5996 & 293.562 & 312.574 & -2033.17 & -1370.09 & 7.86544 & -1.92465 & -5.47614 & -14.6404 \\
|
||||||
|
& -2309.24 & 43289 & 2.03E+10 & 114080 & 249.051 & 13.181 & 46.7492 & 254.37 & 312.802 & -2243.62 & -1637.31 & 8.75968 & -3.27363 & -7.05667 & -14.6404 \\
|
||||||
|
& -3308.55 & 45667 & 1.77E+10 & 98840 & 258.288 & 13.4705 & 48.7221 & 303.815 & 317.885 & -2736.5 & -1716.73 & 8.69769 & -2.13134 & -8.56678 & -14.6404 \\
|
||||||
|
& -1776.1 & 35152 & 1.97E+10 & 128360 & 282.098 & 14.0992 & 49.7351 & 295.107 & 293.715 & -3083.96 & -1337.39 & 8.904 & -2.72989 & -4.97279 & -14.6404 \\
|
||||||
|
& -2495.52 & 42321 & 1.55E+10 & 149040 & 314.038 & 12.2785 & 48.4086 & 311.61 & 312.538 & -2241.98 & -1953.24 & 8.84628 & -2.22338 & -5.40834 & -14.6404 \\
|
||||||
|
& -1753.65 & 54835 & 2.34E+10 & 144560 & 253.313 & 14.0274 & 52.4705 & 265.832 & 306.999 & -2809.58 & -1617.48 & 9.03187 & -2.36168 & -7.64268 & -14.6404 \\
|
||||||
|
& -1574.77 & 42698 & 1.66E+10 & 105920 & 253.962 & 13.7941 & 47.9362 & 205.073 & 314.335 & -3135.48 & -1120.02 & 8.97631 & -1.76927 & -7.50096 & -12.4756 \\
|
||||||
|
& -2206.58 & 47544 & 1.38E+10 & 92400 & 267.474 & 12.5201 & 50.5015 & 300.654 & 299.266 & -2352.88 & -1563.85 & 8.57557 & -1.90659 & -4.65802 & -14.6404 \\
|
||||||
|
& -2159.17 & 27230 & 1.29E+10 & 135280 & 252.351 & 13.261 & 45.9535 & 202.094 & 304.092 & -3073.25 & -2085.45 & 8.53621 & -1.87303 & -8.01815 & -14.6404 \\
|
||||||
|
& -2138.87 & 42123 & 1.63E+10 & 172680 & 271.911 & 13.2188 & 44.2571 & 238.99 & 310.573 & -2373.5 & -1490.61 & 8.82391 & -1.49354 & -5.60778 & -14.6404 \\
|
||||||
|
& -1491.32 & 41559 & 1.57E+10 & 120560 & 264.467 & 14.7017 & 49.9226 & 270.661 & 314.715 & -4606.54 & -1594.99 & 8.57499 & -2.34868 & -6.03643 & -12.4756 \\
|
||||||
|
& -1716.67 & 43371 & 1.96E+10 & 146160 & 256.312 & 13.2972 & 47.8418 & 296.121 & 299.162 & -2949.32 & -1714.88 & 8.94451 & -1.42826 & -7.75141 & -14.6404 \\
|
||||||
|
& -1353.62 & 30573 & 2.00E+10 & 160960 & 304.457 & 13.6341 & 44.8636 & 281.098 & 302.466 & -2253.05 & -1586.38 & 8.79012 & -3.8665 & -4.02897 & -14.6404 \\
|
||||||
|
& -1876.11 & 37188 & 1.04E+10 & 89680 & 268.29 & 12.9539 & 43.9137 & 251.274 & 294.581 & -3173.41 & -1092.65 & 8.51772 & -2.85364 & -4.28 & -14.6404 \\
|
||||||
|
& -1933.32 & 34504 & 2.32E+10 & 145600 & 255.038 & 14.1056 & 52.7792 & 271.035 & 300.68 & -3193.08 & -1565.67 & 8.89243 & -1.22167 & -6.49108 & -14.6404 \\
|
||||||
|
& -1778.81 & 32208 & 1.21E+10 & 164080 & 250.116 & 13.5012 & 48.5518 & 321.977 & 304.95 & -2310.76 & -1192.91 & 8.99516 & -2.24735 & -3.84925 & -14.6404 \\
|
||||||
|
& -1429.94 & 42230 & 1.93E+10 & 139160 & 279.049 & 14.2125 & 47.4455 & 275.462 & 308.209 & -2107.31 & -2552.48 & 8.58813 & -1.87343 & -8.05159 & -14.6404 \\
|
||||||
|
& -1987.26 & 40385 & 2.04E+10 & 151400 & 276.766 & 12.8164 & 53.0487 & 282.964 & 315.214 & -2678.56 & -2387 & 8.6457 & -3.03476 & -6.09081 & -14.6404 \\
|
||||||
|
& -1213.51 & 37332 & 1.51E+10 & 158560 & 261.681 & 13.675 & 47.6498 & 259.175 & 303.699 & -2531.58 & -1766.16 & 8.84001 & -2.59238 & -3.25015 & -12.4756 \\
|
||||||
|
& -1695.59 & 32437 & 1.90E+10 & 128640 & 275.131 & 13.0249 & 46.3462 & 248.083 & 306.287 & -2644.79 & -1901.53 & 8.95061 & -2.73942 & -4.03195 & -14.6404 \\
|
||||||
|
& -1844.9 & 40572 & 2.06E+10 & 166080 & 249.066 & 14.0993 & 44.662 & 267.558 & 302.1 & -2844.67 & -2131.51 & 9.07241 & -1.88671 & -4.71827 & -14.6404 \\
|
||||||
|
& -2039.62 & 48632 & 1.87E+10 & 138960 & 245.71 & 13.6312 & 38.0255 & 275.48 & 312.316 & -1829.93 & -2004.13 & 8.91483 & -1.52482 & -8.56454 & -14.6404 \\
|
||||||
|
& -1689.48 & 44937 & 1.61E+10 & 124600 & 282.54 & 13.3536 & 45.8805 & 290.598 & 305.559 & -2133.36 & -1566.78 & 8.72377 & -3.27145 & -6.96928 & -14.6404 \\
|
||||||
|
& -1633.96 & 51091 & 1.63E+10 & 117000 & 229.145 & 13.471 & 45.3704 & 307.327 & 311.438 & -1961.85 & -2460.35 & 8.80427 & -2.06181 & -7.33068 & -14.6404 \\
|
||||||
|
& -2439.89 & 45380 & 7.91E+09 & 140880 & 310.028 & 13.7849 & 39.4548 & 280.565 & 301.484 & -3402.53 & -2003.01 & 8.76709 & -2.71809 & -5.60029 & -14.6404 \\
|
||||||
|
& -2364.89 & 38298 & 1.23E+10 & 145760 & 238.505 & 14.0388 & 46.8051 & 290.425 & 309.168 & -2827.15 & -1049.02 & 8.70034 & -2.46217 & -7.9223 & -14.6404 \\
|
||||||
|
& -1855.61 & 47187 & 1.06E+10 & 137680 & 248.9 & 13.9612 & 48.5063 & 302.025 & 305.866 & -2152.73 & -1918.95 & 8.4452 & -2.47409 & -6.79521 & -12.4756 \\
|
||||||
|
& -1260.76 & 46347 & 1.16E+10 & 130600 & 281.027 & 13.9532 & 49.979 & 252.528 & 301.864 & -1840.58 & -1860.37 & 8.04923 & -2.63026 & -7.00734 & -14.6404 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -1943.9053333333 & 41395.3333333333 & 1.61E+10 & 1.34E+05 & 2.68E+02 & 1.36E+01 & 4.76E+01 & 2.75E+02 & 3.06E+02 & -2.73E+03 & -1.75E+03 & 8.73E+00 & -2.31E+00 & -6.15E+00 & -1.43E+01 \\
|
||||||
|
Med. & -1850.255 & 42176.5 & 16269000000 & 138320 & 263.074 & 13.63265 & 47.889 & 275.471 & 305.7125 & -2661.675 & -1715.805 & 8.797195 & -2.298015 & -6.06362 & -14.6404 \\
|
||||||
|
Std. Dev. & 503.7270251575 & 6204.5677408321 & 4287970694.60419 & 21553.682353336 & 22.5100649673 & 0.5550063904 & 3.5839928835 & 28.0219424426 & 6.1634317364 & 693.8827057417 & 399.7555341973 & 0.2654236567 & 0.6045133055 & 1.5251158054 & 0.8205653224 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Random Search, 30 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -2360.73 & 76232 & 3.56E+10 & 354540 & 429.015 & 21.1792 & 78.9986 & 444.386 & 468.28 & -3685.03 & -2174.65 & 13.6966 & -3.39811 & -7.7131 & -18.886 \\
|
||||||
|
& -2223.96 & 77130 & 2.33E+10 & 427440 & 482.271 & 21.4306 & 78.5455 & 477.565 & 481.841 & -3105.05 & -1719.81 & 13.3772 & -2.28782 & -5.96241 & -18.886 \\
|
||||||
|
& -2310.43 & 79924 & 2.53E+10 & 381060 & 438.418 & 22.4267 & 85.9081 & 431.309 & 476.297 & -2717.31 & -3512.41 & 13.4609 & -2.95267 & -9.47434 & -18.886 \\
|
||||||
|
& -1233.77 & 74245 & 3.11E+10 & 405060 & 500.971 & 20.6669 & 77.8205 & 460.647 & 478.937 & -3095.92 & -2547.58 & 13.8835 & -3.62638 & -9.1978 & -18.886 \\
|
||||||
|
& -1528.18 & 72335 & 3.46E+10 & 407400 & 480.637 & 21.006 & 85.0255 & 492.562 & 464.278 & -5541.45 & -2344.66 & 13.9109 & -2.60874 & -9.66453 & -13.3197 \\
|
||||||
|
& -1924.52 & 62337 & 2.96E+10 & 350940 & 400.405 & 21.2461 & 72.6017 & 482.764 & 483.487 & -3317.2 & -2334.39 & 13.194 & -2.35764 & -10.1073 & -18.886 \\
|
||||||
|
& -2423.33 & 65161 & 2.41E+10 & 257700 & 455.803 & 21.3956 & 79.6323 & 453.521 & 483.905 & -2929.27 & -3939.27 & 13.9277 & -2.81222 & -11.1674 & -18.886 \\
|
||||||
|
& -3297.71 & 56282 & 2.22E+10 & 333720 & 423.001 & 21.1405 & 72.1825 & 455.991 & 472.822 & -3685.92 & -2244.92 & 13.5072 & -3.4393 & -8.00828 & -13.3197 \\
|
||||||
|
& -2605.99 & 55514 & 2.99E+10 & 258960 & 432.783 & 20.5493 & 77.5907 & 487.188 & 468.534 & -2270.3 & -1770.48 & 13.8159 & -2.20179 & -7.3187 & -18.886 \\
|
||||||
|
& -2324.67 & 56507 & 3.57E+10 & 321720 & 497.616 & 21.1693 & 81.6804 & 548.596 & 472.64 & -4354.96 & -2232.69 & 13.4288 & -3.11785 & -9.96651 & -18.886 \\
|
||||||
|
& -3097.93 & 68919 & 3.54E+10 & 281160 & 311.272 & 21.3644 & 76.7997 & 473.195 & 482.708 & -3184.8 & -1491.68 & 13.6581 & -4.08066 & -11.396 & -18.886 \\
|
||||||
|
& -1810.75 & 74399 & 2.33E+10 & 325980 & 488.695 & 20.1705 & 79.5259 & 425.339 & 467.469 & -3514.4 & -2784.74 & 13.3882 & -2.20352 & -5.85588 & -13.3197 \\
|
||||||
|
& -2695.32 & 74130 & 2.86E+10 & 286740 & 445.725 & 21.7638 & 78.5654 & 443.915 & 465.322 & -3661.35 & -2224.44 & 13.7479 & -2.58372 & -8.03162 & -18.886 \\
|
||||||
|
& -1394.7 & 45078 & 4.43E+10 & 366540 & 392.949 & 19.8834 & 78.3782 & 443.771 & 474.73 & -2292.49 & -2073.16 & 13.8387 & -1.60751 & -8.19351 & -18.886 \\
|
||||||
|
& -2988.33 & 76470 & 3.30E+10 & 356040 & 403.55 & 22.1061 & 66.4539 & 479.545 & 474.343 & -5213 & -2393.19 & 13.621 & -1.97457 & -7.41504 & -18.886 \\
|
||||||
|
& -2107.9 & 64868 & 2.55E+10 & 335760 & 315.955 & 21.3366 & 76.1817 & 456.456 & 475.502 & -3594.52 & -1990.68 & 13.2762 & -1.95789 & -9.20082 & -18.886 \\
|
||||||
|
& -2979.51 & 66791 & 3.77E+10 & 300600 & 476.424 & 22.0409 & 79.8235 & 481.37 & 480.499 & -3226.79 & -2044.33 & 13.4292 & -3.30836 & -8.31641 & -18.886 \\
|
||||||
|
& -2513.73 & 63196 & 2.56E+10 & 378900 & 308.324 & 22.2978 & 73.8612 & 430.727 & 475.581 & -3321.28 & -2602.19 & 13.2479 & -3.04897 & -7.98816 & -18.886 \\
|
||||||
|
& -1622.69 & 62073 & 2.68E+10 & 370080 & 453.052 & 21.0996 & 80.359 & 480.134 & 477.041 & -3560.86 & -2227.66 & 13.4122 & -2.65343 & -9.57417 & -13.3197 \\
|
||||||
|
& -898.919 & 71119 & 3.36E+10 & 275400 & 394.268 & 21.1665 & 76.3499 & 475.151 & 482.187 & -3499.9 & -2010.88 & 13.8071 & -1.77653 & -6.50943 & -18.886 \\
|
||||||
|
& -2228.64 & 59344 & 2.66E+10 & 365520 & 446.585 & 21.9857 & 81.2511 & 473.327 & 469.027 & -4350.96 & -2530.21 & 13.1101 & -2.27438 & -6.5663 & -18.886 \\
|
||||||
|
& -2524.28 & 70564 & 2.93E+10 & 375960 & 467.46 & 20.6626 & 76.0828 & 458.589 & 473.993 & -3010.55 & -2107.52 & 13.5724 & -2.93552 & -3.36125 & -13.3197 \\
|
||||||
|
& -2141.75 & 69544 & 3.38E+10 & 275040 & 410.104 & 20.1186 & 72.5838 & 471.886 & 472.222 & -2617.06 & -2105.35 & 13.348 & -2.82033 & -5.74818 & -18.886 \\
|
||||||
|
& -2203.34 & 72793 & 3.62E+10 & 380220 & 406.591 & 22.0699 & 71.4693 & 407.417 & 484.37 & -2650.34 & -2240.99 & 13.9337 & -1.66277 & -8.93489 & -18.886 \\
|
||||||
|
& -1836.75 & 73045 & 3.98E+10 & 379800 & 514.007 & 22.242 & 79.9503 & 478.007 & 471.384 & -4044.69 & -2652.98 & 13.6287 & -2.44844 & -7.85381 & -18.886 \\
|
||||||
|
& -1787.41 & 50713 & 3.85E+10 & 296100 & 445.226 & 20.0734 & 76.9451 & 459.307 & 448.98 & -3725.34 & -1302.85 & 13.9199 & -2.28859 & -7.12064 & -18.886 \\
|
||||||
|
& -3185.05 & 70905 & 2.69E+10 & 374640 & 424.633 & 22.0167 & 74.4666 & 459.963 & 469.897 & -4000.25 & -2647.59 & 13.8378 & -3.37299 & -10.0858 & -18.886 \\
|
||||||
|
& -2551.98 & 57495 & 3.29E+10 & 370380 & 352.025 & 21.2376 & 81.8073 & 458.902 & 480.163 & -2257.61 & -2125.84 & 13.3929 & -1.83939 & -6.25541 & -18.886 \\
|
||||||
|
& -2406.77 & 78787 & 2.80E+10 & 403560 & 472.017 & 20.9728 & 79.011 & 416.437 & 481.494 & -2723.46 & -1653.13 & 13.5631 & -1.96041 & -5.54987 & -13.3197 \\
|
||||||
|
& -2783.41 & 66765 & 2.70E+10 & 322920 & 330.427 & 21.3975 & 80.8424 & 466.09 & 467.905 & -3393.49 & -2011.66 & 13.5916 & -2.33285 & -5.45623 & -18.886 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -2266.4149666667 & 67088.8333333333 & 3.08E+10 & 3.44E+05 & 4.27E+02 & 2.13E+01 & 7.77E+01 & 4.62E+02 & 4.74E+02 & -3.42E+03 & -2.27E+03 & 1.36E+01 & -2.60E+00 & -7.93E+00 & -1.78E+01 \\
|
||||||
|
Med. & -2317.55 & 69231.5 & 29737350000 & 355290 & 435.6005 & 21.24185 & 78.46185 & 460.305 & 474.5365 & -3357.385 & -2226.05 & 13.582 & -2.51608 & -7.99822 & -18.886 \\
|
||||||
|
Std. Dev. & 584.4233609571 & 8739.8871218282 & 5587568894.7127 & 47357.674511872 & 57.409444418 & 0.6956118635 & 4.0959187591 & 26.9132515273 & 7.5943841166 & 775.2133686027 & 526.201488921 & 0.2412609758 & 0.6322303515 & 1.8774263418 & 2.2645829281 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Local Search, 10 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -1722.16 & 0.03025 & 1.31E+10 & 5978.56 & 69.3764 & 7.53377 & 9.04847 & 181.426 & 151.893 & -754.573 & -2406.31 & 4.19767 & -2.05904 & -8.82787 & -8.1772 \\
|
||||||
|
& -2788.25 & 0.03025 & 1.59E+10 & 29804.2 & 69.0028 & 6.62654 & 9.08272 & 121.985 & 157.316 & 482.297 & -2056.85 & 4.27003 & -0.232247 & -8.81589 & -8.1772 \\
|
||||||
|
& -1839.66 & 0.03025 & 2.15E+10 & 17368.8 & 63.9238 & 7.28417 & 9.19482 & 170.218 & 140.056 & -1188.15 & -2643.43 & 4.22798 & 0.00103445 & -8.76307 & -8.1772 \\
|
||||||
|
& -3498.88 & 0.0300057 & 1.25E+10 & 20604.7 & 68.4473 & 5.87015 & 10.7235 & 109.273 & 156.978 & 765.691 & -2240.2 & 4.50026 & 0.0612122 & -8.79559 & -19.1981 \\
|
||||||
|
& -2373.7 & 0.03025 & 1.93E+10 & 13430.3 & 34.2408 & 7.98904 & 9.18401 & 130.203 & 151.598 & 357.187 & -2195.2 & 4.49177 & 0.843228 & -8.72636 & -19.2635 \\
|
||||||
|
& -2669.32 & 0.03025 & 1.38E+10 & 13495.7 & 60.3948 & 7.3257 & 9.63616 & 126.236 & 150.787 & -701.815 & -2689.55 & 4.52112 & -0.563567 & -8.62028 & -19.2635 \\
|
||||||
|
& -2412.63 & 0.0288532 & 1.22E+10 & 30023.6 & 74.2869 & 7.63259 & 9.07038 & 153.164 & 157.549 & -394.844 & -2613.69 & 4.45488 & 0.147295 & -8.74889 & -19.2635 \\
|
||||||
|
& -2452.59 & 0.03025 & 2.45E+10 & 22446.9 & 42.4768 & 6.59059 & 9.49866 & 167.659 & 153.588 & -2790.05 & -884.232 & 4.49433 & -1.58924 & -8.68392 & -22.0872 \\
|
||||||
|
& -2827.23 & 0.03025 & 6.57E+09 & 18043.7 & 64.0325 & 6.60687 & 9.40728 & 137.864 & 155.09 & 167.839 & -3035.03 & 4.10601 & 2.38E-05 & -8.77553 & -8.1772 \\
|
||||||
|
& -3577.85 & 0.0248398 & 1.74E+10 & 25988.4 & 74.2439 & 6.73684 & 9.28461 & 132.581 & 144.729 & -91.2695 & -1509.7 & 4.46538 & -1.92424 & -8.45917 & -11.5852 \\
|
||||||
|
& -1326.85 & 0.03025 & 2.68E+10 & 7867.05 & 70.423 & 7.52619 & 9.06149 & 111.908 & 154.855 & -117.596 & -2566.91 & 4.49956 & -0.783772 & -8.59181 & -8.1772 \\
|
||||||
|
& -3735.8 & 0.03025 & 1.80E+10 & 22752.2 & 50.6174 & 6.33447 & 9.1547 & 139.641 & 152.514 & -1316.55 & -2518 & 4.47642 & -0.0463014 & -8.75746 & -19.27 \\
|
||||||
|
& -2393.27 & 0.03025 & 1.14E+10 & 14274.5 & 40.5886 & 6.41117 & 10.6114 & 224.812 & 151.689 & -1288.83 & -2630.38 & 3.96854 & -1.52409 & -8.57556 & -8.1772 \\
|
||||||
|
& -2136.79 & 0.0251012 & 1.16E+10 & 13427.8 & 74.9665 & 6.62278 & 9.18783 & 124.629 & 154.597 & -1036 & -2279.34 & 4.08355 & -0.337027 & -8.83124 & -19.27 \\
|
||||||
|
& -2551.26 & 0.03025 & 2.09E+10 & 14787 & 71.8753 & 6.1052 & 10.2295 & 144.82 & 149.299 & -2255.52 & -2667.36 & 4.48691 & -0.522676 & -8.6941 & -19.27 \\
|
||||||
|
& -2610.58 & 0.0275071 & 2.19E+10 & 23744.9 & 51.3633 & 6.37208 & 9.21428 & 170.428 & 154.633 & 499.723 & -1960.24 & 4.49717 & -0.225219 & -8.82633 & -22.0364 \\
|
||||||
|
& -3301.42 & 0.03025 & 1.42E+10 & 17620.1 & 58.3238 & 6.90171 & 9.09557 & 109.074 & 155.68 & -413.377 & -2868.24 & 4.28546 & -0.609139 & -8.78285 & -22.0364 \\
|
||||||
|
& -2610.41 & 0.0293121 & 1.09E+10 & 27801.8 & 95.0175 & 6.93148 & 9.15471 & 167.973 & 150.033 & 689.394 & -2833.55 & 4.28827 & 0.0360106 & -8.26686 & -8.1772 \\
|
||||||
|
& -3183.07 & 0.0290287 & 1.69E+10 & 20238.5 & 60.3234 & 6.74264 & 9.67354 & 102.391 & 156.435 & -1058.05 & -2757.67 & 4.00982 & -0.78522 & -8.08889 & -8.1772 \\
|
||||||
|
& -2432.85 & 0.0301624 & 3.56E+10 & 22842.4 & 35.3722 & 6.46636 & 11.4128 & 170.701 & 148.289 & 1105.59 & -2210.27 & 4.77573 & -1.19427 & -8.509 & -21.9278 \\
|
||||||
|
& -2630.3 & 0.0278163 & 2.73E+10 & 25658.9 & 83.3985 & 6.10535 & 9.14037 & 121.269 & 154.065 & -958.512 & -2575.3 & 4.48104 & -1.21108 & -7.78537 & -11.6076 \\
|
||||||
|
& -2095.89 & 0.03025 & 9.81E+09 & 20270.9 & 40.2026 & 7.20097 & 9.0472 & 104.663 & 154.173 & -505.146 & -1771.42 & 4.11262 & -0.613923 & -8.83158 & -8.1772 \\
|
||||||
|
& -3301.53 & 0.0302264 & 1.35E+10 & 13708.2 & 62.5321 & 8.05472 & 11.0529 & 165.973 & 155.957 & -2069.58 & -2830.66 & 4.2232 & 0.124692 & -8.83616 & -19.2635 \\
|
||||||
|
& -2314.48 & 0.0296459 & 1.98E+10 & 21595.5 & 66.9216 & 5.32096 & 9.18803 & 119.401 & 156.968 & -728.852 & -3393.19 & 4.37753 & -1.01123 & -8.81274 & -11.6076 \\
|
||||||
|
& -3380.47 & 0.0302365 & 9.73E+09 & 26948.7 & 96.7156 & 7.25999 & 9.21712 & 187.299 & 153.919 & -213.715 & -2813.35 & 3.95788 & 0.110539 & -8.7079 & -8.1772 \\
|
||||||
|
& -3143.51 & 0.0199678 & 2.03E+10 & 11383 & 68.7048 & 6.28303 & 9.25189 & 188.644 & 157.746 & 110.405 & -1688.99 & 4.49839 & -1.74384 & -8.7247 & -8.1772 \\
|
||||||
|
& -2847.49 & 0.029854 & 1.84E+10 & 23915.5 & 72.8991 & 6.63326 & 9.38279 & 105.037 & 150.94 & 862.177 & -2078.53 & 4.58513 & -1.08988 & -8.61938 & -11.5852 \\
|
||||||
|
& -2926.46 & 0.03025 & 1.77E+10 & 21728.5 & 81.07 & 6.60333 & 9.31926 & 150.771 & 152.901 & -1333.47 & -2068.27 & 4.05192 & -0.656969 & -8.55918 & -21.9278 \\
|
||||||
|
& -2195.99 & 0.03025 & 2.26E+10 & 19417.8 & 76.3799 & 6.66027 & 9.09237 & 125.473 & 153.158 & -1148.96 & -2269.14 & 4.4759 & -1.44312 & -8.40539 & -8.1772 \\
|
||||||
|
& -2610.57 & 0.03025 & 1.95E+10 & 10095 & 51.5363 & 6.21274 & 9.11763 & 159.311 & 154.965 & -304.918 & -2250.28 & 4.4864 & 0.365686 & -8.75928 & -8.1772 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -2663.042 & 0.02921857 & 1.75E+10 & 1.92E+04 & 6.43E+01 & 6.76E+00 & 9.49E+00 & 1.44E+02 & 1.53E+02 & -5.21E+02 & -2.38E+03 & 4.35E+00 & -6.16E-01 & -8.64E+00 & -1.39E+01 \\
|
||||||
|
Med. & -2610.575 & 0.03025 & 17541950000 & 20254.7 & 67.68445 & 6.6299 & 9.20455 & 138.7525 & 153.992 & -459.2615 & -2462.155 & 4.46013 & -0.586353 & -8.72553 & -11.5964 \\
|
||||||
|
Std. Dev. & 565.9521286104 & 0.0022586778 & 6227253551.6775 & 6364.105861632 & 15.8722794272 & 0.6200294914 & 0.6404997265 & 30.5973750771 & 3.8796949489 & 947.6193603132 & 502.5410994529 & 0.2088816019 & 0.7371789862 & 0.2404532 & 5.8867273045 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Local Search, 20 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -5793.64 & 0.0605 & 2.54E+10 & 84243.8 & 0.00544314 & 15.7534 & 22.794 & 388.743 & 318.54 & 872.22 & -4878.35 & 9.29745 & -0.868549 & -17.4433 & -18.4163 \\
|
||||||
|
& -6425.36 & 0.0605 & 3.40E+10 & 83330.5 & 2.59579 & 15.18 & 19.4765 & 282.217 & 326.136 & -890.302 & -5205.81 & 9.19442 & -1.71722 & -18.36 & -18.1189 \\
|
||||||
|
& -5773.94 & 0.0605 & 5.03E+10 & 81685.7 & 0.0325362 & 14.6438 & 19.2967 & 414.614 & 325.683 & -38.8792 & -4808.13 & 8.96731 & -1.28757 & -18.4541 & -18.1189 \\
|
||||||
|
& -4312.14 & 0.0605 & 4.46E+10 & 84028.2 & 0.012734 & 14.8892 & 20.1529 & 248.993 & 329.517 & -49.3419 & -5762.01 & 8.95851 & -0.769716 & -18.3303 & -11.5925 \\
|
||||||
|
& -5201.29 & 0.0528605 & 2.90E+10 & 85961.1 & 0.0175917 & 14.6958 & 20.7801 & 295.784 & 326.719 & 131.108 & -4284.49 & 9.19177 & -0.978247 & -17.4535 & -11.5925 \\
|
||||||
|
& -5437.26 & 0.0484009 & 4.27E+10 & 81010.4 & 0.0128073 & 14.7319 & 20.2851 & 308.26 & 321.815 & -3179.98 & -5910.02 & 8.83616 & -1.10339 & -18.3132 & -12.1791 \\
|
||||||
|
& -5911.51 & 0.0605 & 4.18E+10 & 82890.4 & 0.0127846 & 15.3506 & 24.845 & 297.244 & 320.763 & 124.661 & 359.29 & 9.15435 & -0.821465 & -18.5011 & -11.5925 \\
|
||||||
|
& -5852.3 & 0.0605 & 3.89E+10 & 80465.7 & 0.50121 & 13.8042 & 24.586 & 358.358 & 328.323 & -1240.58 & -5701.28 & 9.48494 & -2.10437 & -17.7341 & -11.5925 \\
|
||||||
|
& -4549.95 & 0.0554514 & 2.43E+10 & 82872.7 & 0.0150484 & 14.0756 & 21.9814 & 378.772 & 320.122 & 866.459 & -4804.78 & 9.32682 & 0.177762 & -18.4128 & -11.5924 \\
|
||||||
|
& -5615.93 & 0.0594314 & 3.87E+10 & 85395.4 & 0.0177641 & 14.9938 & 21.737 & 348.39 & 330.393 & -40.7054 & -803.14 & 9.46576 & -0.757524 & -17.8996 & -12.1455 \\
|
||||||
|
& -6128.66 & 0.0599989 & 5.86E+09 & 82482.7 & 0.022684 & 15.7937 & 23.5337 & 318.578 & 313.61 & -1594.82 & -5561.16 & 9.47295 & -0.0502173 & -18.3411 & -12.1791 \\
|
||||||
|
& -5911.12 & 0.06046 & 3.82E+10 & 79597.1 & 0.00540948 & 14.6048 & 19.8786 & 343.956 & 316.997 & -1549.64 & -4889 & 9.12806 & 0.593797 & -17.5708 & -11.5925 \\
|
||||||
|
& -5378.96 & 0.0546049 & 3.55E+10 & 79844.9 & 0.513188 & 13.4111 & 20.819 & 237.504 & 323.974 & -236.878 & -2783.49 & 9.40669 & -1.5814 & -17.8182 & -11.5925 \\
|
||||||
|
& -6208.09 & 0.0605 & 4.11E+10 & 82806.8 & 0.0423564 & 13.6263 & 19.9244 & 338.475 & 330.509 & 145.801 & -5149.57 & 8.85904 & 0.0709934 & -17.7823 & -11.5925 \\
|
||||||
|
& -6365.98 & 0.0604318 & 3.95E+10 & 82980.4 & 0.00544314 & 14.5561 & 29.7907 & 373.119 & 316.579 & -3981.64 & -589.742 & 9.45594 & -0.734831 & -17.6115 & -12.1791 \\
|
||||||
|
& -4865.5 & 0.0605 & 2.70E+10 & 81023.3 & 0.022684 & 14.1321 & 19.6227 & 264.108 & 329.156 & -895.345 & -5298.19 & 9.31191 & -0.709717 & -17.9523 & -12.1791 \\
|
||||||
|
& -5753.31 & 0.0592495 & 5.53E+10 & 83182.6 & 0.0177641 & 14.3879 & 19.4463 & 326.461 & 329.266 & -736.031 & -36.6239 & 9.20092 & -1.01499 & -18.0679 & -11.5925 \\
|
||||||
|
& -4964.05 & 0.0495827 & 3.22E+10 & 86350.6 & 7.65341 & 14.4621 & 19.5375 & 354.537 & 329.072 & -98.6246 & -4398.36 & 9.44551 & -1.39255 & -18.4823 & -11.5925 \\
|
||||||
|
& -5161.97 & 0.0605 & 3.97E+10 & 79778.4 & 0.0244744 & 14.5572 & 22.6856 & 320.654 & 312.234 & -6486.32 & -4736.91 & 9.2983 & -1.06507 & -17.1146 & -11.5925 \\
|
||||||
|
& -5635.57 & 0.0605 & 4.11E+10 & 81383.2 & 0.0054374 & 14.5234 & 22.1856 & 287.145 & 323.708 & 562.055 & -5532.35 & 9.13131 & 0.712591 & -18.4878 & -12.1791 \\
|
||||||
|
& -5477.18 & 0.0605 & 2.88E+10 & 83253.2 & 0.012837 & 14.4646 & 20.9163 & 383.667 & 322.139 & -2281.67 & -4092.8 & 9.208 & -2.1219 & -18.2742 & -11.5925 \\
|
||||||
|
& -5240.77 & 0.0605 & 5.57E+10 & 82110.1 & 13.4906 & 15.0371 & 19.3914 & 205.418 & 324.571 & -3193.3 & -6020.24 & 8.90623 & -2.27157 & -16.7489 & -11.5924 \\
|
||||||
|
& -5635.19 & 0.0605 & 2.74E+10 & 84813.3 & 0.0447807 & 15.5438 & 19.7167 & 245.99 & 327.78 & -1235.47 & -3310.1 & 9.39152 & -1.5261 & -17.3734 & -11.5925 \\
|
||||||
|
& -3246.38 & 0.0605 & 3.80E+10 & 83219.6 & 0.00544314 & 14.7169 & 23.0755 & 303.558 & 313.476 & -1673.84 & -4972.31 & 8.87996 & 0.484031 & -18.4883 & -11.5925 \\
|
||||||
|
& -6168.66 & 0.0558529 & 4.47E+10 & 84716.8 & 2.94032 & 13.5461 & 19.8038 & 247.696 & 325.746 & -550.653 & -5601.88 & 9.47937 & -0.144748 & -17.0377 & -11.5925 \\
|
||||||
|
& -4213.77 & 0.0581465 & 3.19E+10 & 80103 & 0.00538645 & 16.5718 & 25.1075 & 282.189 & 323.021 & -137.129 & -5451.53 & 9.11948 & -1.01489 & -17.6702 & -11.5729 \\
|
||||||
|
& -3384.87 & 0.0595524 & 4.13E+10 & 82072.8 & 0.00541907 & 15.7239 & 19.2661 & 329.103 & 324.07 & -1101.79 & -4311.6 & 9.49093 & -0.162895 & -18.1284 & -11.5925 \\
|
||||||
|
& -5437.75 & 0.0590881 & 2.77E+10 & 87335.7 & 0.486232 & 13.3387 & 19.7886 & 289.14 & 323.126 & -2199.9 & -5897.58 & 9.47676 & 0.0210599 & -17.9211 & -18.4163 \\
|
||||||
|
& -4154.64 & 0.0605 & 3.50E+10 & 80441.7 & 0.0226616 & 15.9632 & 19.6977 & 323.442 & 319.648 & -2319.97 & -4943.11 & 9.3123 & -1.96752 & -16.6269 & -11.5925 \\
|
||||||
|
& -4727.06 & 0.0605 & 5.57E+10 & 83475 & 0.0300529 & 15.2459 & 20.5533 & 339.345 & 325.59 & 2361.6 & -3509.27 & 9.4069 & -2.61652 & -17.0723 & -11.5925 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -5297.76 & 0.05870373 & 3.70E+10 & 8.28E+04 & 9.53E-01 & 1.47E+01 & 2.14E+01 & 3.15E+02 & 3.23E+02 & -1.02E+03 & -4.30E+03 & 9.24E+00 & -8.91E-01 & -1.78E+01 & -1.26E+01 \\
|
||||||
|
Med. & -5457.465 & 0.0605 & 38465350000 & 82881.55 & 0.02021285 & 14.6698 & 20.4192 & 319.616 & 324.022 & -813.1665 & -4883.675 & 9.297875 & -0.923398 & -17.91035 & -11.5925 \\
|
||||||
|
Std. Dev. & 817.3212483051 & 0.0033237366 & 10498474328.5921 & 2030.9259258268 & 2.8130712982 & 0.7900945894 & 2.3862355564 & 50.2442400537 & 5.1944841414 & 1705.6218308227 & 1790.4080474217 & 0.210871267 & 0.8822055188 & 0.5520760648 & 2.2743612411 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Local Search, 30 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -8720.03 & 0.09075 & 6.14E+10 & & 468222 & 25.6736 & 29.4629 & 465.724 & 492.399 & -709.211 & -9489.6 & 14.5445 & & -23.8513 & -19.687 \\
|
||||||
|
& -6962.47 & 0.09075 & 8.45E+10 & & 544786 & 26.105 & 37.1947 & 476.184 & 495.891 & -2092.66 & -1548.36 & 14.1196 & & -27.6662 & -18.039 \\
|
||||||
|
& -6961.87 & 0.0899048 & 4.08E+10 & & 566160 & 22.9069 & 32.2468 & 572.709 & 495.119 & -724.496 & -1085.52 & 13.6181 & & -26.3649 & -9.98237 \\
|
||||||
|
& -7022.1 & 0.09075 & 4.80E+10 & & 507124 & 23.5184 & 32.8019 & 474.808 & 504.87 & 1441.2 & -298.734 & 14.6858 & & -27.5487 & -11.4003 \\
|
||||||
|
& -7554.77 & 0.09075 & 5.62E+10 & & 534480 & 21.3856 & 45.2881 & 407.728 & 499.809 & -1117.15 & -6155.89 & 14.1285 & & -28.0375 & -18.8073 \\
|
||||||
|
& -7930.32 & 0.0901661 & 3.89E+10 & & 507272 & 21.786 & 39.2269 & 585.973 & 489.683 & -559.234 & -6021.34 & 14.2482 & & -26.9184 & -17.1526 \\
|
||||||
|
& -7671.66 & 0.09075 & 6.33E+10 & & 544748 & 23.9949 & 39.7063 & 410.723 & 496.021 & -706.327 & -8420.73 & 14.4966 & & -27.7328 & -18.8073 \\
|
||||||
|
& -9153.89 & 0.0896031 & 5.37E+10 & & 527930 & 22.0799 & 31.5884 & 432.908 & 498.172 & -1000.39 & -7023.77 & 14.0189 & & -27.4268 & -19.6821 \\
|
||||||
|
& -7475.88 & 0.0825127 & 7.43E+10 & & 441660 & 24.9363 & 30.6169 & 441.306 & 495.542 & 727.837 & -2984.97 & 14.1608 & & -27.0882 & -18.1224 \\
|
||||||
|
& -7140.58 & 0.09075 & 6.14E+10 & & 461520 & 22.1797 & 32.6729 & 418.557 & 493.104 & -2038.59 & -8148.21 & 13.8726 & & -27.3871 & -17.6842 \\
|
||||||
|
& -6765.1 & 0.09075 & 5.12E+10 & & 442200 & 22.8712 & 30.8062 & 469.99 & 489.318 & -3404.86 & -1965.15 & 13.9313 & & -27.7029 & -11.4003 \\
|
||||||
|
& -7278.84 & 0.0705322 & 4.88E+10 & & 387826 & 23.6452 & 29.7264 & 530.633 & 493.766 & 659.589 & -2082.03 & 14.7356 & & -26.4235 & -18.039 \\
|
||||||
|
& -9804.77 & 0.09075 & 3.16E+10 & & 594900 & 22.2526 & 30.373 & 506.952 & 499.083 & -6243.21 & -7264.67 & 14.0242 & & -25.9648 & -11.4003 \\
|
||||||
|
& -7258.63 & 0.0815248 & 5.80E+10 & & 506574 & 22.0017 & 38.3344 & 481.961 & 499.895 & -182.164 & -7375.24 & 14.1801 & & -26.8838 & -17.1526 \\
|
||||||
|
& -7752.47 & 0.09075 & 6.81E+10 & & 594960 & 23.3336 & 32.0357 & 448.093 & 497.64 & 121.125 & -6137.77 & 13.9758 & & -25.8605 & -19.5175 \\
|
||||||
|
& -6427.54 & 0.0901747 & 6.75E+10 & & 499020 & 24.788 & 39.219 & 550.8 & 497.778 & 197.675 & -6537.16 & 14.0494 & & -26.9742 & -18.3945 \\
|
||||||
|
& -8048.75 & 0.09075 & 7.70E+10 & & 345160 & 22.4005 & 31.9327 & 351.905 & 500.411 & -5356.76 & -5318.86 & 14.3997 & & -25.4607 & -18.3945 \\
|
||||||
|
& -6922.6 & 0.0897579 & 7.72E+10 & & 424932 & 21.4528 & 33.8182 & 440.397 & 494.772 & -670.487 & 239.404 & 13.8857 & & -25.2136 & -18.039 \\
|
||||||
|
& -7534.96 & 0.09075 & 5.81E+10 & & 510105 & 24.7605 & 34.6564 & 481.573 & 493.94 & -2080.9 & -6633.34 & 14.5802 & & -27.3547 & -11.4003 \\
|
||||||
|
& -6626.39 & 0.0840877 & 6.24E+10 & & 356637 & 26.2454 & 36.2963 & 508.941 & 499.447 & -1921.38 & -7225.03 & 14.007 & & -28.186 & -19.6828 \\
|
||||||
|
& -8739.22 & 0.0833573 & 6.99E+10 & & 546298 & 21.9408 & 33.581 & 543.216 & 491.856 & -1567.4 & -6976.34 & 13.9828 & & -28.2896 & -11.3613 \\
|
||||||
|
& -8818.69 & 0.0885963 & 3.97E+10 & & 438240 & 21.1752 & 36.2528 & 460.593 & 499.554 & 72.1365 & -6627.36 & 14.0521 & & -27.4075 & -11.4003 \\
|
||||||
|
& -7258.09 & 0.0861417 & 3.49E+10 & & 544831 & 25.094 & 30.5593 & 548.84 & 499.339 & -5073.7 & -8456.63 & 14.2453 & & -25.5684 & -19.8561 \\
|
||||||
|
& -8087.75 & 0.0903371 & 5.46E+10 & & 446157 & 22.9032 & 32.1424 & 421.646 & 492.63 & -3900.26 & 1353.84 & 13.7392 & & -26.6823 & -19.2993 \\
|
||||||
|
& -8482.43 & 0.090039 & 4.66E+10 & & 552581 & 22.3861 & 36.5625 & 457.61 & 499.245 & 1.35286 & -7844.48 & 13.7055 & & -28.0855 & -19.687 \\
|
||||||
|
& -6528.81 & 0.09075 & 6.70E+10 & & 498480 & 19.5627 & 34.625 & 464.465 & 492.466 & -6965.86 & -9712.34 & 14.1021 & & -26.9693 & -18.8073 \\
|
||||||
|
& -8463.27 & 0.09075 & 6.34E+10 & & 479280 & 24.0583 & 34.4645 & 608.036 & 473.426 & -7745.87 & -7636.44 & 14.4946 & & -26.258 & -12.3742 \\
|
||||||
|
& -8956.81 & 0.0893575 & 4.27E+10 & & 431907 & 23.3802 & 37.1508 & 605.869 & 486.893 & -6378.37 & -7262.28 & 13.9651 & & -27.4611 & -12.3742 \\
|
||||||
|
& -8443.14 & 0.09075 & 4.63E+10 & & 660240 & 23.5017 & 36.0516 & 503.756 & 478.32 & -1278.35 & -4455.43 & 13.9693 & & -27.2419 & -17.1526 \\
|
||||||
|
& -6843.66 & 0.0894151 & 7.43E+10 & & 481320 & 23.2531 & 29.9386 & 478.08 & 499.71 & -4075.12 & -5839.02 & 14.2467 & & -27.0834 & -9.98237 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -7721.183 & 0.0885336 & 5.74E+10 & 4.95E+05 & 2.32E+01 & 3.43E+01 & 4.85E+02 & 4.95E+02 & -2.09E+03 & -5.50E+03 & -5.50E+03 & 1.41E+01 & -2.69E+01 & -2.69E+01 & -1.62E+01 \\
|
||||||
|
Med. & -7544.865 & 0.0902559 & 58031700000 & 502797 & 23.08 & 33.6996 & 475.496 & 495.7165 & -1197.75 & -6582.26 & -6582.26 & 14.0771 & -27.0858 & -27.0858 & -18.039 \\
|
||||||
|
Std. Dev. & 879.654776568 & 0.004344398 & 13715570303.9744 & 70598.3086040107 & 1.561052308 & 3.7073113996 & 61.9062241299 & 6.4821907987 & 2517.8253815732 & 3015.9014409368 & 3015.9014409368 & 0.2842525403 & 1.0005521456 & 1.0005521456 & 3.6031873219 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Iterative Local Search, 10 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -2353.48 & 0.03025 & 1.88E+10 & 24168.4 & 59.988 & 6.39537 & 9.35607 & 117.932 & 155.879 & -145.889 & -1646.86 & 4.03982 & -0.302471 & -8.64558 & -11.5852 \\
|
||||||
|
& -2294.56 & 0.0278096 & 2.01E+10 & 23308.8 & 54.3803 & 7.99499 & 9.10285 & 166.587 & 155.557 & -479.37 & -2949.33 & 4.21134 & -0.408084 & -8.79464 & -8.1772 \\
|
||||||
|
& -2847.49 & 0.0296792 & 1.70E+10 & 32302.9 & 46.5895 & 7.22062 & 9.07374 & 161.811 & 148.705 & -56.1015 & -2988.2 & 3.99908 & -0.616554 & -8.82742 & -8.1772 \\
|
||||||
|
& -1682.61 & 0.0286486 & 1.90E+10 & 26081.4 & 61.7176 & 6.47021 & 10.7403 & 138.539 & 151.489 & -235.288 & -2321.48 & 4.49389 & -1.30566 & -8.4728 & -8.1772 \\
|
||||||
|
& -3163.22 & 0.03025 & 1.44E+10 & 15665.2 & 46.493 & 7.87339 & 11.6635 & 143.48 & 152.545 & -1197.31 & -3124.24 & 4.06124 & -1.18534 & -8.60302 & -8.1772 \\
|
||||||
|
& -1564.28 & 0.0292164 & 3.22E+10 & 9609.37 & 63.0245 & 6.84956 & 9.12636 & 77.5438 & 158.222 & -1306.25 & -2504.62 & 4.49975 & -0.577118 & -8.69359 & -8.1772 \\
|
||||||
|
& -3005.3 & 0.03025 & 2.60E+10 & 29121.3 & 42.5946 & 7.03493 & 11.0323 & 121.662 & 156.691 & -457.272 & -2495.34 & 4.48833 & -0.676501 & -8.81122 & -8.1772 \\
|
||||||
|
& -2196.03 & 0.03025 & 9.37E+09 & 24244.2 & 67.6498 & 6.47648 & 9.77288 & 118.753 & 156.684 & -78.0806 & -1123.95 & 4.65848 & -1.34652 & -8.80146 & -8.1772 \\
|
||||||
|
& -2451.6 & 0.0301868 & 1.89E+10 & 31828.8 & 54.9617 & 7.78021 & 9.1522 & 198.115 & 153.576 & -44.7555 & -1719.94 & 4.49672 & 0.0368576 & -8.82573 & -8.1772 \\
|
||||||
|
& -2649.5 & 0.03025 & 2.35E+10 & 24122.5 & 78.0887 & 7.02654 & 9.08477 & 145.058 & 146.57 & -359.588 & -2690.69 & 4.44189 & -0.600237 & -8.85059 & -8.1772 \\
|
||||||
|
& -1978.6 & 0.0302292 & 1.92E+10 & 26676.1 & 67.6724 & 6.72453 & 9.32724 & 184.502 & 154.944 & -978.036 & -2567.04 & 4.49746 & 0.352732 & -8.72541 & -8.1772 \\
|
||||||
|
& -2590.35 & 0.03025 & 1.86E+10 & 22140.2 & 67.8106 & 7.22723 & 9.27195 & 184.804 & 149.624 & 228.958 & -2156.38 & 4.63231 & 0.000805103 & -8.82982 & -19.2635 \\
|
||||||
|
& -2492.14 & 0.0302253 & 1.45E+10 & 23038.2 & 54.4243 & 6.35673 & 9.37548 & 64.1279 & 158.208 & -1552.16 & -2516.99 & 4.1252 & -0.977698 & -8.62522 & -8.1772 \\
|
||||||
|
& -3024.64 & 0.0302113 & 2.35E+10 & 21144 & 68.705 & 7.64069 & 15.3717 & 175.471 & 156.45 & -188.369 & -2261.48 & 4.50388 & -0.189495 & -8.82071 & -19.27 \\
|
||||||
|
& -2787.77 & 0.0299542 & 3.51E+10 & 30959.5 & 87.3445 & 7.258 & 9.17457 & 174.921 & 154.939 & -549.431 & -2337.16 & 4.51863 & -0.100004 & -8.71327 & -11.6076 \\
|
||||||
|
& -2412.16 & 0.03025 & 2.46E+10 & 10038.6 & 49.23 & 7.00135 & 9.18972 & 203.733 & 153.641 & -279.738 & -813.436 & 4.13809 & 0.0861884 & -8.70443 & -22.0364 \\
|
||||||
|
& -2156.47 & 0.03025 & 3.45E+09 & 7563.02 & 90.5625 & 6.32097 & 9.77328 & 161.579 & 153.295 & -83.3801 & -1182.55 & 4.45449 & -0.394575 & -8.82684 & -19.2635 \\
|
||||||
|
& -3044.69 & 0.03025 & 1.32E+10 & 18228.9 & 53.0633 & 6.98713 & 9.22125 & 139.567 & 151.861 & -101.129 & -2341.09 & 4.27863 & -0.295665 & -8.4791 & -20.3627 \\
|
||||||
|
& -2294.74 & 0.0128203 & 6.91E+09 & 13513 & 64.0312 & 8.51433 & 9.30183 & 142.512 & 156.996 & -1863.03 & -1398.12 & 4.49987 & -0.496323 & -8.84694 & -8.1772 \\
|
||||||
|
& -2946.18 & 0.0182463 & 4.41E+09 & 10620.3 & 76.5551 & 7.01442 & 9.13523 & 175.317 & 152.924 & 63.6296 & -2397.78 & 4.50181 & 0.0984758 & -8.7853 & -21.9278 \\
|
||||||
|
& -2412.71 & 0.03025 & 6.12E+09 & 21231.5 & 47.6436 & 6.37215 & 9.12583 & 152.503 & 151.491 & 662.344 & -2139.36 & 4.50024 & -0.769488 & -8.30502 & -8.1772 \\
|
||||||
|
& -2669.73 & 0.03025 & 2.28E+10 & 18635 & 46.5956 & 6.62651 & 9.1969 & 179.575 & 155.756 & -1622.23 & -2579.85 & 4.49683 & -0.639761 & -8.72133 & -19.1981 \\
|
||||||
|
& -2235.49 & 0.03025 & 1.40E+10 & 28141.5 & 72.5154 & 6.69095 & 9.79442 & 127.291 & 145.443 & -2020.13 & -1634.23 & 4.42869 & -1.00679 & -8.78072 & -19.2635 \\
|
||||||
|
& -2985.69 & 0.0300519 & 9.28E+09 & 37770.1 & 96.61 & 7.83742 & 14.026 & 184.708 & 146.4 & -590.179 & -2498.13 & 4.26544 & -0.0294331 & -8.87411 & -8.1772 \\
|
||||||
|
& -2353.88 & 0.03025 & 1.14E+10 & 38853.2 & 55.7939 & 7.34237 & 9.23642 & 155.955 & 156.938 & -298.932 & -2034.12 & 4.68428 & 0.020851 & -8.83159 & -21.9278 \\
|
||||||
|
& -1899.75 & 0.03025 & 3.03E+10 & 19359.5 & 64.5918 & 7.09233 & 10.284 & 99.3849 & 153.521 & -801.555 & -2416.07 & 4.08334 & -0.124733 & -8.71859 & -19.27 \\
|
||||||
|
& -1623.43 & 0.03025 & 1.17E+10 & 18194 & 72.4011 & 6.26496 & 9.88647 & 133.847 & 156.223 & -1354.62 & -2445.64 & 4.09028 & -1.742 & -8.69851 & -20.3627 \\
|
||||||
|
& -3222.55 & 0.03025 & 1.86E+10 & 33430.4 & 75.1929 & 6.67338 & 9.50346 & 219.224 & 156.195 & -894.692 & -1572.32 & 4.40705 & -0.587073 & -8.64298 & -8.1772 \\
|
||||||
|
& -3498.88 & 0.0256026 & 1.53E+10 & 23899.7 & 72.4134 & 6.26663 & 9.14707 & 169.249 & 154.033 & -1419.25 & -2628.3 & 4.49581 & -0.0643777 & -8.10856 & -8.1772 \\
|
||||||
|
& -3044.4 & 0.0301924 & 3.41E+10 & 11088.4 & 51.1488 & 6.8859 & 9.41934 & 103.838 & 155.568 & -2127.94 & -2428.83 & 4.49982 & -1.26039 & -8.74994 & -8.1772 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -2529.4106666667 & 0.02890247 & 1.79E+10 & 2.25E+04 & 6.37E+01 & 7.01E+00 & 9.90E+00 & 1.51E+02 & 1.54E+02 & -6.71E+02 & -2.20E+03 & 4.38E+00 & -5.03E-01 & -8.70E+00 & -1.28E+01 \\
|
||||||
|
Med. & -2471.87 & 0.03025 & 18559750000 & 23173.5 & 63.52785 & 6.99424 & 9.314535 & 154.229 & 154.486 & -468.321 & -2369.435 & 4.49111 & -0.4522035 & -8.737675 & -8.1772 \\
|
||||||
|
Std. Dev. & 495.6619476322 & 0.0038284652 & 8448356883.66839 & 8305.8388465674 & 13.8542434661 & 0.5807405608 & 1.4575514207 & 36.4813660807 & 3.4660196682 & 719.7744604556 & 569.5535684889 & 0.1984223336 & 0.5169316097 & 0.17100592 & 5.8206220674 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Iterative Local Search, 20 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -4549.4 & 0.0605 & 2.51E+10 & 83731.6 & 0.651206 & 14.7944 & 21.9681 & 279.695 & 321.001 & -1566.84 & -5338.46 & 9.21621 & -0.142619 & -17.715 & -11.8869 \\
|
||||||
|
& -6050.16 & 0.0605 & 4.55E+10 & 80745.8 & 0.268086 & 16.284 & 19.3337 & 343.725 & 313.969 & -1993.75 & -4148.75 & 9.46446 & -0.548652 & -18.1772 & -11.5925 \\
|
||||||
|
& -5398.23 & 0.0591995 & 5.06E+10 & 82401.3 & 0.032413 & 15.3526 & 23.9768 & 296.889 & 321.956 & -4633.19 & -4848.21 & 9.01646 & 0.218791 & -18.4282 & -11.5925 \\
|
||||||
|
& -5675.22 & 0.0605 & 4.42E+10 & 82591.3 & 0.00544314 & 14.7622 & 22.6234 & 331.276 & 323.228 & -4014.04 & -5436.05 & 9.26537 & 0.626347 & -18.2969 & -12.1455 \\
|
||||||
|
& -3976.37 & 0.0588747 & 3.25E+10 & 82036.8 & 0.0128344 & 13.9223 & 25.6647 & 325.015 & 329.982 & -1246.54 & -4823.87 & 9.12409 & -1.33997 & -17.3054 & -11.5925 \\
|
||||||
|
& -5082.38 & 0.0605 & 2.61E+10 & 87382.5 & 0.0201434 & 14.3422 & 21.3897 & 396.392 & 326.205 & -392.775 & -6280.62 & 9.13477 & -2.26781 & -18.1703 & -11.5925 \\
|
||||||
|
& -5891.86 & 0.0605 & 3.85E+10 & 89279.1 & 0.684922 & 15.2594 & 23.0754 & 329.302 & 326.381 & -337.14 & -3871.16 & 9.02972 & -0.0242582 & -18.4513 & -11.5925 \\
|
||||||
|
& -5003.93 & 0.0605 & 3.00E+10 & 85879 & 0.0151074 & 14.8268 & 19.9157 & 325.527 & 314.19 & -1212.15 & -4189.52 & 9.17302 & 0.0681864 & -18.2963 & -11.5925 \\
|
||||||
|
& -5418.57 & 0.0605 & 4.30E+10 & 82890.7 & 0.00544314 & 15.9129 & 20.2634 & 332.571 & 325.788 & -1548.21 & -5548.41 & 9.39065 & -0.906265 & -18.5396 & -11.5925 \\
|
||||||
|
& -5516.7 & 0.0599162 & 3.51E+10 & 82665.9 & 0.03008 & 16.1692 & 19.9074 & 388.651 & 327.169 & 923.714 & -563.104 & 9.20288 & -1.59829 & -18.1054 & -11.5925 \\
|
||||||
|
& -3937.92 & 0.0547374 & 3.35E+10 & 86354.3 & 0.0153078 & 15.3926 & 19.532 & 312.15 & 322.111 & -1598.94 & -4648.01 & 8.87789 & -1.49817 & -17.8506 & -11.5925 \\
|
||||||
|
& -4588.88 & 0.0605 & 3.51E+10 & 81101.4 & 0.00542657 & 15.7841 & 21.7808 & 357.263 & 330.684 & -1319.67 & -4261.9 & 9.33048 & -1.93719 & -18.2627 & -12.1791 \\
|
||||||
|
& -5082.97 & 0.0559769 & 2.42E+10 & 76218.4 & 0.00544314 & 15.1429 & 22.4065 & 346.209 & 324.783 & 1500.62 & -6032.9 & 9.18852 & -1.88437 & -18.2285 & -11.5925 \\
|
||||||
|
& -6070.01 & 0.0594556 & 3.21E+10 & 83486.2 & 0.0201913 & 14.966 & 19.342 & 393.145 & 324.682 & -404.273 & -5094.61 & 8.57302 & 0.126058 & -16.446 & -11.5925 \\
|
||||||
|
& -5043.31 & 0.0605 & 1.85E+10 & 82935.5 & 0.220955 & 14.0295 & 20.2134 & 316.233 & 325.996 & 2079.2 & -4572.43 & 9.70061 & -1.33897 & -18.2227 & -11.5925 \\
|
||||||
|
& -5161.34 & 0.0605 & 3.87E+10 & 85337.7 & 0.00541984 & 14.9478 & 19.5923 & 274.232 & 327.758 & -1727.17 & -4684.33 & 9.01989 & 0.290542 & -18.4796 & -12.1791 \\
|
||||||
|
& -4589.3 & 0.060484 & 3.08E+10 & 83765.5 & 0.01776 & 14.714 & 20.0449 & 314.196 & 325.896 & -1632.08 & -5179.06 & 8.52944 & -0.524841 & -18.3505 & -18.4163 \\
|
||||||
|
& -4332.6 & 0.0600202 & 6.77E+10 & 81498.7 & 0.0153003 & 16.5447 & 20.0096 & 314.165 & 307.423 & 1567.7 & -4461.73 & 9.43874 & -1.20997 & -18.3268 & -11.5925 \\
|
||||||
|
& -6267.41 & 0.0605 & 4.65E+10 & 81603.3 & 1.88596 & 16.1234 & 20.2482 & 316.52 & 317.494 & -5550.9 & -3693.39 & 9.4962 & 0.510028 & -18.1621 & -11.5925 \\
|
||||||
|
& -4588.98 & 0.0605 & 4.06E+10 & 85076.2 & 0.0225431 & 14.0527 & 20.8396 & 325.67 & 324.854 & -2326.43 & -5346.36 & 9.33666 & -1.32004 & -18.555 & -11.5925 \\
|
||||||
|
& -5477.69 & 0.0604347 & 5.13E+10 & 80799.5 & 0.00535823 & 14.7108 & 20.8015 & 261.241 & 319.17 & 485.619 & -4782.73 & 9.17792 & -1.22079 & -17.7232 & -18.1189 \\
|
||||||
|
& -6109.42 & 0.0472961 & 2.54E+10 & 83373.9 & 0.177322 & 15.6127 & 22.9987 & 286.368 & 321.812 & -2998.41 & -4456.01 & 8.6997 & -2.29998 & -18.4625 & -11.5925 \\
|
||||||
|
& -4885.09 & 0.0570039 & 4.39E+10 & 80163.4 & 0.255374 & 15.6927 & 23.8355 & 324.354 & 325.002 & -1578.78 & -4075.51 & 9.10547 & -0.131814 & -17.7901 & -11.5925 \\
|
||||||
|
& -4035.51 & 0.0600693 & 4.32E+10 & 81415.3 & 0.0152417 & 14.9575 & 19.5572 & 296.019 & 321.434 & 824.034 & -4011.91 & 9.31902 & -1.51993 & -17.8755 & -11.5924 \\
|
||||||
|
& -3917.22 & 0.0586818 & 4.16E+10 & 87878.4 & 0.0128369 & 14.4221 & 19.8007 & 375.301 & 300.768 & 1043.95 & -5037.25 & 9.57629 & -1.73324 & -18.2571 & -11.5925 \\
|
||||||
|
& -5754.04 & 0.0575467 & 2.76E+10 & 83622.3 & 0.272711 & 17.8043 & 20.3977 & 370.717 & 317.424 & -1133.68 & -4973.3 & 9.0252 & -1.10353 & -18.3087 & -11.5925 \\
|
||||||
|
& -4786.87 & 0.0605 & 5.54E+10 & 81190 & 0.0324796 & 14.4401 & 19.8204 & 358.965 & 296.103 & 692.475 & -4121.91 & 8.76355 & -0.860885 & -18.484 & -18.4163 \\
|
||||||
|
& -4510.47 & 0.0597792 & 3.24E+10 & 83530.5 & 4.0042 & 16.0272 & 20.4454 & 225.399 & 326.39 & -478.625 & -5370.33 & 9.32801 & -1.13941 & -18.4418 & -11.5729 \\
|
||||||
|
& -5398.26 & 0.0605 & 2.12E+10 & 85510.6 & 0.43707 & 14.8375 & 19.3774 & 332.986 & 327.903 & -985.672 & -316.499 & 8.94067 & 0.336942 & -17.9256 & -11.5925 \\
|
||||||
|
& -4253.69 & 0.0600716 & 3.11E+10 & 84551 & 0.0128391 & 15.5474 & 21.1043 & 361.928 & 322.473 & -2662.15 & -5159.38 & 7.88002 & -0.784451 & -18.5094 & -11.5924 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -5045.1266666667 & 0.05921826 & 3.70E+10 & 8.33E+04 & 3.06E-01 & 1.52E+01 & 2.10E+01 & 3.27E+02 & 3.21E+02 & -1.07E+03 & -4.51E+03 & 9.11E+00 & -8.39E-01 & -1.81E+01 & -1.23E+01 \\
|
||||||
|
Med. & -5062.845 & 0.06045935 & 35130150000 & 83154.7 & 0.02016735 & 15.05445 & 20.33055 & 325.5985 & 323.955 & -1229.345 & -4733.53 & 9.17547 & -1.0048975 & -18.2599 & -11.5925 \\
|
||||||
|
Std. Dev. & 698.8094252189 & 0.0026900942 & 10975111347.1528 & 2619.8872400694 & 0.791604351 & 0.8539153929 & 1.6317417953 & 39.4394076324 & 8.0216805796 & 1808.4389363295 & 1268.809305736 & 0.3628603461 & 0.8562948838 & 0.4364346365 & 2.0376857083 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Iterative Local Search, 30 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -9410.25 & 0.0833082 & 2.47E+10 & 320497 & & 21.2591 & 29.99 & 368.495 & 473.165 & -6278.62 & -8324.6 & 13.026 & & -28.105 & -19.687 \\
|
||||||
|
& -8976.5 & 0.0769728 & 2.89E+10 & 255960 & & 21.382 & 29.5846 & 390.77 & 473.46 & -6837.82 & -9171.82 & 13.5603 & & -28.2307 & -19.8561 \\
|
||||||
|
& -9489.96 & 0.0869344 & 3.07E+10 & 349495 & & 20.4102 & 29.7188 & 389.025 & 476.709 & -6935.81 & -9139.13 & 13.5408 & & -28.2876 & -19.687 \\
|
||||||
|
& -9074.77 & 0.0754036 & 2.83E+10 & 337920 & & 21.3585 & 29.459 & 330.204 & 459.783 & -7391.32 & -8408.34 & 12.9661 & & -28.411 & -19.687 \\
|
||||||
|
& -9568.94 & 0.0569313 & 3.10E+10 & 338600 & & 21.4582 & 29.6347 & 379.984 & 477.567 & -6970.99 & -9684.67 & 13.1975 & & -28.3428 & -19.8561 \\
|
||||||
|
& -9351.64 & -0.0490078 & 2.68E+10 & 345600 & & 20.7617 & 29.9363 & 7.87853 & 475.43 & -6241.29 & -9773.48 & 13.359 & & -28.1681 & -19.8561 \\
|
||||||
|
& -124.883 & 0.0683202 & 3.31E+10 & 277860 & & 20.4901 & 29.4315 & 374.832 & 472.378 & -7256.36 & -9653.19 & 13.1889 & & -28.1063 & -19.687 \\
|
||||||
|
& -9924.25 & 0.0791061 & 3.19E+10 & 372780 & & 20.9216 & 29.7907 & 373.748 & 455.648 & -6665.63 & 283.59 & 12.8196 & & -28.1588 & -19.8561 \\
|
||||||
|
& -9311.68 & 0.0746452 & 3.24E+10 & 334380 & & 21.3375 & 29.6274 & 372.786 & 472.562 & -6572.91 & -8859.86 & 13.3459 & & -28.316 & -19.8561 \\
|
||||||
|
& -9035.97 & 0.0662556 & 3.18E+10 & 349440 & & 20.4238 & 30.1293 & 352.798 & 478.836 & -7007.39 & -9049.74 & 13.4248 & & -28.2245 & -19.8561 \\
|
||||||
|
& -9864.99 & -0.0636759 & 3.03E+10 & 312060 & & 21.0197 & 29.4352 & 376.266 & 466.391 & -8396.7 & -9143.81 & 13.5041 & & -28.2922 & -19.8561 \\
|
||||||
|
& -8996.29 & 0.0789434 & 2.87E+10 & -1.66599 & & 20.6647 & 29.6205 & 358.251 & 479.544 & -8751 & -10268.6 & 13.2663 & & -28.1594 & -19.8561 \\
|
||||||
|
& -9391.2 & 0.0749528 & 2.68E+10 & 338160 & & 20.4382 & 29.777 & 345.008 & 458.006 & -5866.24 & -8878.84 & 13.203 & & -28.3379 & -19.8561 \\
|
||||||
|
& -9450.26 & 0.0845282 & 2.94E+10 & 368202 & & 20.5752 & 29.6886 & 368.195 & 474.65 & -8633.36 & 464.389 & 13.6934 & & -3.43673 & -19.8561 \\
|
||||||
|
& -9746.52 & 0.0751487 & 2.75E+10 & 357000 & & 21.1032 & 29.7037 & 351.542 & 467.12 & -7698.6 & -8541.65 & 13.0522 & & -28.3234 & -19.8561 \\
|
||||||
|
& -9865.04 & 0.0805431 & -1.00E+01 & 330432 & & 20.6857 & 29.5885 & -7.69393 & 473.355 & -6756.61 & -9540.85 & 13.5152 & & -28.2612 & -19.687 \\
|
||||||
|
& -9272.8 & 0.0795918 & 3.80E+10 & 286598 & & 20.6426 & 19.8658 & 371.765 & 486.552 & -6547.46 & -9854.97 & 13.5049 & & -28.1688 & -19.8561 \\
|
||||||
|
& -10062.5 & 0.0768222 & 3.74E+10 & 350160 & & 20.0135 & 29.7339 & 390.477 & 475.739 & -6719.72 & -9440.52 & 13.4156 & & -28.2127 & -19.8561 \\
|
||||||
|
& -9134.53 & 0.0711327 & 3.08E+10 & 360420 & & 20.3865 & 29.9421 & 378.888 & 462.393 & -8281.31 & -9663.62 & 12.4277 & & -28.2074 & -19.8561 \\
|
||||||
|
& -9390.69 & -0.0309291 & 3.97E+10 & 295997 & & 20.7647 & 30.6015 & 359.88 & 484.513 & -5065.59 & -9134.86 & 13.6274 & & -28.3904 & -19.8561 \\
|
||||||
|
& -9134.47 & 0.0804281 & 4.70E+10 & 334360 & & 20.6145 & 27.8698 & 381.484 & 474.708 & -4226.77 & -8811.44 & 13.4809 & & -28.0928 & -19.8561 \\
|
||||||
|
& -9568.88 & 0.0785976 & 3.02E+10 & 296580 & & 20.5503 & 29.785 & 398.854 & 455.846 & -7596.93 & -8997.45 & 13.5753 & & -28.1787 & -19.8561 \\
|
||||||
|
& -9272.85 & 0.0817261 & 2.52E+10 & 306300 & & 20.8571 & 29.6731 & 380.627 & 470.576 & -6667.08 & -9397.49 & 13.1267 & & -28.1215 & -19.6839 \\
|
||||||
|
& -9212.05 & 0.0702678 & 2.59E+10 & 335460 & & 20.1083 & 30.1306 & 381.129 & 463.962 & -8674.4 & -9315.6 & 12.7335 & & -28.2767 & -19.8561 \\
|
||||||
|
& -9726.64 & 0.0741334 & 3.79E+10 & 363524 & & 21.4061 & 29.8537 & 332.172 & 458.809 & -6933.42 & -9365.76 & 13.402 & & -28.2994 & -19.8561 \\
|
||||||
|
& -10457.2 & 0.081483 & 3.46E+10 & 313260 & & 21.4092 & 29.7197 & 333.342 & 476.861 & -8640.37 & -9338.14 & 13.5832 & & -28.3677 & -19.8561 \\
|
||||||
|
& -9173.48 & 0.0870585 & 3.42E+10 & 312000 & & 20.6161 & 29.6849 & 336 & 471.518 & -6486.48 & -9721.42 & 13.651 & & -28.1929 & -19.8561 \\
|
||||||
|
& -9133.36 & 0.0840459 & 3.16E+10 & 381645 & & 20.5456 & 30.4368 & 362.339 & 459.739 & -6593.12 & -9396.22 & 13.5676 & & -28.2393 & -19.8561 \\
|
||||||
|
& -9391.21 & 0.0797617 & 4.23E+10 & 346620 & & 20.5208 & 29.7877 & 368.834 & 477.598 & -6145.95 & -9651.49 & 13.1248 & & -28.3445 & -19.8561 \\
|
||||||
|
& -9568.74 & 0.074442 & 2.17E+10 & 311040 & & 18.9379 & 29.5349 & 355.219 & 475.06 & -8780 & -9200.77 & 13.5091 & & -28.3269 & -19.8561 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -9136.0847666667 & 0.06459572 & 3.06E+10 & 3.19E+05 & 2.07E+01 & 2.94E+01 & 3.42E+02 & 4.71E+02 & 4.71E+02 & -7.05E+03 & -8.63E+03 & 1.33E+01 & -2.74E+01 & -2.74E+01 & -1.98E+01 \\
|
||||||
|
Med. & -9390.945 & 0.0768975 & 30729500000 & 334920 & 20.65365 & 29.71125 & 368.6645 & 473.26 & 473.26 & -6885.62 & -9258.185 & 13.4088 & -28.235 & -28.235 & -19.8561 \\
|
||||||
|
Std. Dev. & 1736.4971456685 & 0.0388817803 & 7961125157.97467 & 67087.9428677671 & 0.5231419506 & 1.8518270929 & 94.7625256457 & 8.3344717638 & 8.3344717638 & 1094.2052693977 & 2486.4963265932 & 0.3016843863 & 4.5304816776 & 4.5304816776 & 0.0690085236 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\small{Random Search Running Times in Seconds}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\begin{tabular}{l || l | l | l}
|
||||||
|
|
||||||
|
\textbf{Dimensions} & 10 & 20 & 30 \\
|
||||||
|
\hline \\
|
||||||
|
Function 1 & 0.0028796196 & 0.0027256012 & 0.004216671 \\
|
||||||
|
Function 2 & 0.0037307739 & 0.0027096272 & 0.0042328835 \\
|
||||||
|
Function 3 & 0.0027823448 & 0.0035073757 & 0.003777504 \\
|
||||||
|
Function 4 & 0.0041363239 & 0.0041460991 & 0.0026381016 \\
|
||||||
|
Function 5 & 0.0037288666 & 0.003030777 & 0.0026414394 \\
|
||||||
|
Function 6 & 0.0037727356 & 0.0028493404 & 0.0026321411 \\
|
||||||
|
Function 7 & 0.0035896301 & 0.0027751923 & 0.0037312508 \\
|
||||||
|
Function 8 & 0.0035607815 & 0.0028815269 & 0.0037713051 \\
|
||||||
|
Function 9 & 0.0044622421 & 0.0026602745 & 0.0027508736 \\
|
||||||
|
Function 10 & 0.0040593147 & 0.0027322769 & 0.0027010441 \\
|
||||||
|
Function 11 & 0.0030579567 & 0.0026328564 & 0.0026137829 \\
|
||||||
|
Function 12 & 0.0029666424 & 0.0026972294 & 0.0026974678 \\
|
||||||
|
Function 13 & 0.0027945042 & 0.003254652 & 0.0025961399 \\
|
||||||
|
Function 14 & 0.0028162003 & 0.004308939 & 0.0026042461 \\
|
||||||
|
Function 15 & 0.0027165413 & 0.0028510094 & 0.0024940968 \\
|
||||||
|
\hline
|
||||||
|
\end{tabular}
|
||||||
|
}\\[0.5cm]
|
||||||
|
|
||||||
|
|
||||||
|
\small{Local Search Running Times in Seconds}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\begin{tabular}{l || l | l | l}
|
||||||
|
|
||||||
|
\textbf{Dimensions} & 10 & 20 & 30 \\
|
||||||
|
\hline \\
|
||||||
|
Function 1 & 0.2714903355 & 0.3662781715 & 0.8037896156 \\
|
||||||
|
Function 2 & 0.0122189522 & 0.1061990261 & 0.0311796665 \\
|
||||||
|
Function 3 & 0.0036041737 & 0.0034754276 & 0.0039658546 \\
|
||||||
|
Function 4 & 0.0045986176 & 0.0037958622 & 0.0057651997 \\
|
||||||
|
Function 5 & 3.5046873093 & 107.3777658939 & 237.3515529633 \\
|
||||||
|
Function 6 & 0.0053646564 & 0.0071499348 & 0.0078163147 \\
|
||||||
|
Function 7 & 0.1593027115 & 0.1934890747 & 36.0658888817 \\
|
||||||
|
Function 8 & 0.0124971867 & 0.1606588364 & 0.0684037209 \\
|
||||||
|
Function 9 & 0.0049269199 & 0.0049231052 & 0.0069723129 \\
|
||||||
|
Function 10 & 0.0056340694 & 0.0120668411 & 0.0046567917 \\
|
||||||
|
Function 11 & 0.0049116611 & 0.1781361103 & 3.4641461372 \\
|
||||||
|
Function 12 & 0.0035014153 & 0.0031409264 & 0.0071520805 \\
|
||||||
|
Function 13 & 0.0022878647 & 0.0031747818 & 0.008487463 \\
|
||||||
|
Function 14 & 0.0166265965 & 0.1419093609 & 0.2867805958 \\
|
||||||
|
Function 15 & 0.0709223747 & 0.0597565174 & 0.1033499241 \\
|
||||||
|
\hline \\
|
||||||
|
\end{tabular}
|
||||||
|
} \\[0.5cm]
|
||||||
|
|
||||||
|
|
||||||
|
\small{Iterative Local Search Running Times in Seconds}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\begin{tabular}{l || l | l | l}
|
||||||
|
|
||||||
|
\textbf{Dimensions} & 10 & 20 & 30 \\
|
||||||
|
\hline \\
|
||||||
|
Function 1 & 5.355587244 & 21.5247523785 & 47.5882720947 \\
|
||||||
|
Function 2 & 0.4999251366 & 1.151144743 & 2.4649145603 \\
|
||||||
|
Function 3 & 0.0042607784 & 0.0112228394 & 0.0144929886 \\
|
||||||
|
Function 4 & 0.0058951378 & 0.0114533901 & 0.0161828995 \\
|
||||||
|
Function 5 & 150.1059572697 & 2928.3961615563 & N/A \\
|
||||||
|
Function 6 & 0.0101454258 & 0.0255510807 & 0.0222308636 \\
|
||||||
|
Function 7 & 32.4964332581 & 41.5021996498 & 168.8056237698 \\
|
||||||
|
Function 8 & 0.3526818752 & 1.6770370007 & 3.8826031685 \\
|
||||||
|
Function 9 & 0.0110986233 & 0.0125215054 & 0.0229070187 \\
|
||||||
|
Function 10 & 0.0899729729 & 0.2644715309 & 0.7342042923 \\
|
||||||
|
Function 11 & 30.093629837 & 165.0208876133 & 384.6772966385 \\
|
||||||
|
Function 12 & 297.458874464 & 25.3617525101 & 21.2174470425 \\
|
||||||
|
Function 13 & 0.0197796822 & 0.0436241627 & 0.0463643074 \\
|
||||||
|
Function 14 & 1.5726833344 & 7.7616007328 & 9.7854065895 \\
|
||||||
|
Function 15 & 6.6486163139 & 23.9164574146 & 32.7224471569 \\
|
||||||
|
\hline \\
|
||||||
|
\end{tabular}
|
||||||
|
} \\[0.5cm]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\section{Previous Results}
|
||||||
|
|
||||||
|
\hskip+1.0cm\scalebox{0.7}{
|
||||||
|
\begin{tabular}{| l | l | l | l | l | l|}
|
||||||
|
\hline
|
||||||
|
Function & Dimensionality & Mean & Median & Deviation & Avg. Time \\ \hline
|
||||||
|
Schwefel's & 10 & 40.03647 & 0.0623 & 547.27404 & 3.1285 \\ \hline
|
||||||
|
& 20 & -273.92765 & 16.52 & 883.3137 & 2.942 \\ \hline
|
||||||
|
& 30 & -63.79153 & 255.3487 & 925.71758 & 3.132 \\ \hline
|
||||||
|
|
||||||
|
De Jong's & 10 & 3775.96667 & 3772 & 3116.12957 & 0.667 \\ \hline
|
||||||
|
& 20 & 3748.5 & 4105 & 2885.095608 & 0.132 \\ \hline
|
||||||
|
& 30 & 3429.8334 & 3429 & 2608.0073 & 0.0933 \\ \hline
|
||||||
|
|
||||||
|
Rosenbrock & 10 & 2093118197.7 & 951513386 & 2466626292.4 & 0.90 \\ \hline
|
||||||
|
& 20 & 2109933654.57 & 997811808.5 &2696811330.46 & 1.679 \\ \hline
|
||||||
|
& 30 & 1784558137.97 & 543961363 & 2372214627.61 & 2.98 \\ \hline
|
||||||
|
|
||||||
|
Rastrigin & 10 & 318.2 & 262.5 & 289.575003684 & 1.04 \\ \hline
|
||||||
|
& 20 & 202.2666667 & 111 & 232.951516834 & 1.98 \\ \hline
|
||||||
|
& 30 & 309.9 & 246 & 264.111829763 & 2.788 \\ \hline
|
||||||
|
|
||||||
|
Griegwangk & 10 & 27.326983 & 22.8316 & 19.94304775 & 1.32 \\ \hline
|
||||||
|
& 20 & 22.247975 & 18.4916 & 17.41881647 & 4.98 \\ \hline
|
||||||
|
& 30 & 25.04950833 & 24.1043 & 19.11108789 & 5.67 \\ \hline
|
||||||
|
|
||||||
|
Sine Envelope Sine Wave & 10 & -4.70534 &-4.6083 & 0.2380683 & 1.112 \\ \hline
|
||||||
|
& 20 & -9.805175 & -9.6732 & 0.3503130 & 2.223 \\ \hline
|
||||||
|
& 30 & -15.1282 & -15.0166 & 0.410964 & 4.121 \\ \hline
|
||||||
|
|
||||||
|
Stretched V Sine Wave & 10 & -5.85114 & -5.8511 & 4.5168102e-15 & 3.55 \\ \hline
|
||||||
|
& 20 & -12.3524 & -12.35 & 5.420172e-15 & 6.88 \\ \hline
|
||||||
|
& 30 & -18.8536 & -18.8537 & 3.61344822e-15 & 9.87 \\ \hline
|
||||||
|
|
||||||
|
Ackley's One & 10 & 187.917 & 184.0721 & 33.190007 & 3.1298 \\ \hline
|
||||||
|
& 20 & 389.45238 & 382.962 & 40.1378 & 4.6731 \\ \hline
|
||||||
|
& 30 & 593.39786 & 599.1116 & 64.28003 & 8.7728 \\ \hline
|
||||||
|
|
||||||
|
Ackley's Two & 10 & 217.1978 & 217.922 & 2.26465 & 3.055 \\ \hline
|
||||||
|
& 20 & 456.7024 & 459.70244 & 6.447889 & 7.001 \\ \hline
|
||||||
|
& 30 & 698.9139 & 700.1936 & 4.42437 & 8.4356 \\ \hline
|
||||||
|
|
||||||
|
Egg Holder & 10 & -374.3114 & -529.396 & 877.367109 & 1.998 \\ \hline
|
||||||
|
& 20 & -197.32204 & -339.2197 & 1196.0543 & 4.7621 \\ \hline
|
||||||
|
& 30 & -533.43484 & -507.8253 & 1366.0572 & 6.9981 \\ \hline
|
||||||
|
|
||||||
|
Rana & 10 & 126.682 & 92.4435 & 762.991158 & 5.1433 \\ \hline
|
||||||
|
& 20 & 44.632258 & 144.748 & 897.22947 & 9.4239 \\ \hline
|
||||||
|
& 30 & 147.21517 & 280.21517 & 1161.5825 & 14.221 \\ \hline
|
||||||
|
|
||||||
|
Pathological & 10 & 4.7605744 & 4.5666& 0.320334 & 3.1561 \\ \hline
|
||||||
|
& 20 & 10.02892 & 9.9324 & 0.485784 & 3.9714 \\ \hline
|
||||||
|
& 30 & 15.28425 & 15.1622 & 0.66075 & 4.9912 \\ \hline
|
||||||
|
|
||||||
|
Michalewicz & 10 & 0.904288 & 0.942 & 0.544008 & 1.3241 \\ \hline
|
||||||
|
& 20 & 1.73464 & 1.5955 & 0.733736 & 3.1149 \\ \hline
|
||||||
|
& 30 & 2.108609 & 1.9897 & 0.974908 & 4.8229 \\ \hline
|
||||||
|
|
||||||
|
Masters Cosine Wave & 10 &0.6488827 & 0.5623 & 2.0702 & 2.3341 \\ \hline
|
||||||
|
& 20 & -1.492407& -1.0483& 2.270724 & 3.4256 \\ \hline
|
||||||
|
& 30 & 0.885458 & 0.9459 & 3.441345 & 5.3243 \\ \hline
|
||||||
|
|
||||||
|
Shekel's Foxhole & 10 & -0.2105669 & -0.2023 & 0.038925 & 4.5623 \\ \hline
|
||||||
|
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
|
||||||
|
\end{document}
|
||||||
|
|
||||||
@ -0,0 +1,738 @@
|
|||||||
|
\documentclass[paper=a4, fontsize=11pt]{scrartcl}
|
||||||
|
\usepackage[T1]{fontenc}
|
||||||
|
\usepackage{fourier}
|
||||||
|
\usepackage[english]{babel}
|
||||||
|
\usepackage[protrusion=true,expansion=true]{microtype}
|
||||||
|
\usepackage{amsmath,amsfonts,amsthm}
|
||||||
|
\usepackage[pdftex]{graphicx}
|
||||||
|
\usepackage{url}
|
||||||
|
\usepackage{sectsty}
|
||||||
|
\allsectionsfont{\centering \normalfont\scshape}
|
||||||
|
|
||||||
|
\usepackage{fancyhdr}
|
||||||
|
\pagestyle{fancyplain}
|
||||||
|
\fancyhead{}
|
||||||
|
\fancyfoot[L]{}
|
||||||
|
\fancyfoot[C]{}
|
||||||
|
\fancyfoot[R]{\thepage}
|
||||||
|
\renewcommand{\headrulewidth}{0pt}
|
||||||
|
\renewcommand{\footrulewidth}{0pt}
|
||||||
|
\setlength{\headheight}{13.6pt}
|
||||||
|
\numberwithin{equation}{section}
|
||||||
|
\numberwithin{figure}{section}
|
||||||
|
\numberwithin{table}{section}
|
||||||
|
\newcommand{\horrule}[1]{\rule{\linewidth}{#1}}
|
||||||
|
|
||||||
|
\title{
|
||||||
|
%\vspace{-1in}
|
||||||
|
\usefont{OT1}{bch}{b}{n}
|
||||||
|
\normalfont \normalsize \textsc{Central Washington University of the Computer Science Department} \\ [25pt]
|
||||||
|
\horrule{0.5pt} \\[0.4cm]
|
||||||
|
\huge Project 2 \\
|
||||||
|
\horrule{2pt} \\[0.5cm]
|
||||||
|
}
|
||||||
|
|
||||||
|
\author{\normalsize Mitchell Hansen \\[-6pt]}
|
||||||
|
|
||||||
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
|
||||||
|
\begin{document}
|
||||||
|
\maketitle
|
||||||
|
|
||||||
|
\section{Introduction}
|
||||||
|
For this lab we took the 15 functions that we programmed in the previous
|
||||||
|
lab and ran them through 3 different optimization functions, each more
|
||||||
|
accurate than the previous. We have random search, which blindly tests
|
||||||
|
randomized solutions looking for an optimum. Secondly we have local search,
|
||||||
|
which takes an initial randomized solution and then attempts to optimize it
|
||||||
|
until it's at its minimum. Thirdly we have iterative local search, which
|
||||||
|
combines the two previous functions.
|
||||||
|
|
||||||
|
\section{Methods}
|
||||||
|
A significant portion of the code from the previous lab was rewritten to
|
||||||
|
allow the functions and search methods to be run from the command line.
|
||||||
|
Arguments specifying the dimensionality of the solution, id of the funtion,
|
||||||
|
id of the search method, and a seed are all handled by the program. Using
|
||||||
|
this backbone, we wrote a trivial python script that executes each search
|
||||||
|
method on all of the 15 functions being tested for each dimensionality.
|
||||||
|
There currently is an issue with runtimes being significant for a select
|
||||||
|
few functions on high dimensionalties. As a result some data points have
|
||||||
|
been omitted.
|
||||||
|
|
||||||
|
\section{Analysis}
|
||||||
|
There were various interesting results both in the new data, what the new
|
||||||
|
functions were able to find in terms of minimums, and how close some data
|
||||||
|
points got to the last lab where the search was purely random.
|
||||||
|
|
||||||
|
Comparing the new data from the Iterative Local Search (ILS) and the Local
|
||||||
|
Search (LS) with the previous results, we see that the purely naive method
|
||||||
|
that we used previously is actually quite sufficient for a few select functions,
|
||||||
|
namely: Sine Envelope Sine Wave, Pathological, Rosenbrok, and Ackleys
|
||||||
|
Two functions. Each of these functions evaluated to very similar solutions
|
||||||
|
in all three methods, naive, ILS and LS. Often being within 10\% of each other.
|
||||||
|
|
||||||
|
The differences between the two new methods used in this lab, ILS and LS
|
||||||
|
are mainly negligible in their cumulative accuracy. There are
|
||||||
|
some examples where the search methods differ more than others. Griegwangk
|
||||||
|
and Egg Holder differ the most between the two methods, with a 100 - 200 \%
|
||||||
|
difference seen between the methods. For single runs of the functions though,
|
||||||
|
ILS is superior to LS as can be seen in the graphics below for two seperate
|
||||||
|
runs of ILS on differing functions. The top line being the single run results,
|
||||||
|
and the bottom being the running best solution.
|
||||||
|
|
||||||
|
|
||||||
|
\scalebox{0.4}{\includegraphics{figure_1}}
|
||||||
|
\scalebox{0.4}{\includegraphics{figure_2}}
|
||||||
|
|
||||||
|
|
||||||
|
There were also a few problems with the expirementation, one being the fact that
|
||||||
|
we neglected the fact that the delta value within the LS and ILS functions could
|
||||||
|
throw the function outside of its specified bounds. The implimentation checked
|
||||||
|
each of these bounds each function call, but only returned 0 if it exceeded them.
|
||||||
|
Thus some results have errenous values of either 0 or some other integer value.
|
||||||
|
|
||||||
|
Another problem, as mentioned again in the conclusion, is the runtime of these
|
||||||
|
search methods. In particular, function 5 and 13 ran extremely slow. Slow enough
|
||||||
|
for the results having to be omitted as it would take longer to obtain the results
|
||||||
|
than we have time for the lab. We hope to speed up the implimentation prior to the
|
||||||
|
next lab and include those results then.
|
||||||
|
|
||||||
|
All testing was done on an i7-3630QM with 16GB ram using a single thread.
|
||||||
|
Complete results can be viewed in the sections below.
|
||||||
|
|
||||||
|
\section{Conclusion}
|
||||||
|
The search functions in this lab seem to behave much more accurately than the
|
||||||
|
random search in the previous lab. Not only are the new functions seemingly accurate,
|
||||||
|
but they also appear to repeat their values consistently giving the appearance that
|
||||||
|
they are coming up with a somewhat correct answer. Unfortunately, it seems where these
|
||||||
|
new functions fall down is in their performance. In the 30 dimension trials for this lab,
|
||||||
|
there were multiple functions where results had to be omitted because of running time issues.
|
||||||
|
Some taking up to multiple hours to run for their full permutation count. We hope to see
|
||||||
|
functions which take care of these issues in the upcoming labs.
|
||||||
|
|
||||||
|
\section{Results}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Random Search, 10 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
& -1223.19 & 13286 & 3.18E+09 & 24260 & 114.244 & 5.52567 & 18.2586 & 154.502 & 145.101 & -1714.21 & -1147.36 & 3.95561 & -1.64259 & -3.31459 & -19.1981 \\
|
||||||
|
& -1399.06 & 19906 & 1.96E+09 & 16700 & 90.6946 & 6.07905 & 20.4112 & 102.177 & 142.077 & -1648.81 & -1560.96 & 4.09041 & -1.39102 & -4.9016 & -19.1981 \\
|
||||||
|
& -1010.48 & 14917 & 6.76E+09 & 24520 & 51.8071 & 6.28399 & 21.7189 & 125.452 & 138.122 & -1661.62 & -1122.59 & 4.06708 & -1.34332 & -5.57971 & -19.1981 \\
|
||||||
|
& -1934.21 & 13942 & 4.96E+09 & 29540 & 122.229 & 5.67277 & 19.682 & 123.082 & 139.72 & -2343.48 & -1408.66 & 4.03919 & -1.86534 & -4.43512 & -19.1981 \\
|
||||||
|
& -794.372 & 14642 & 3.77E+09 & 19260 & 51.4992 & 5.64587 & 17.3606 & 130.231 & 146.019 & -1927.46 & -1392.74 & 4.02665 & -0.919067 & -2.9242 & -19.1981 \\
|
||||||
|
& -1431.49 & 10213 & 5.51E+09 & 15600 & 99.3078 & 6.37517 & 19.3915 & 106.105 & 147.534 & -1693.06 & -1590.86 & 3.81288 & -1.94752 & -4.41244 & -19.1981 \\
|
||||||
|
& -1222.58 & 14061 & 6.24E+09 & 27600 & 61.8407 & 5.95916 & 21.8219 & 134.971 & 138.811 & -1120.32 & -1274.41 & 3.90444 & -0.949296 & -5.21336 & -19.1981 \\
|
||||||
|
& -1659.45 & 14265 & 3.86E+09 & 12040 & 77.3118 & 6.59486 & 19.6264 & 107.374 & 137.152 & -2115.97 & -1151.79 & 3.9272 & -2.01492 & -4.14032 & -10.3861 \\
|
||||||
|
& -1495.15 & 16757 & 7.43E+09 & 29540 & 90.1975 & 5.82742 & 17.0893 & 118.252 & 133.507 & -1385.32 & -1145.69 & 4.03879 & -1.59431 & -4.89982 & -19.1981 \\
|
||||||
|
& -869.356 & 16426 & 4.32E+09 & 32060 & 34.4248 & 5.79818 & 19.4186 & 111.549 & 133.483 & -1677.77 & -859.394 & 4.00669 & -0.948425 & -5.22481 & -13.4604 \\
|
||||||
|
& -1039.51 & 18099 & 3.32E+09 & 34560 & 91.9329 & 6.26357 & 21.1327 & 114.807 & 140.81 & -1668.54 & -1128.23 & 4.07449 & -1.05552 & -3.01325 & -19.1981 \\
|
||||||
|
& -919.612 & 12176 & 6.94E+09 & 25180 & 90.0024 & 6.26924 & 16.2619 & 113.944 & 128.653 & -2016.45 & -873.684 & 4.06288 & -1.4206 & -4.45688 & -19.1981 \\
|
||||||
|
& -1506.41 & 14320 & 5.21E+08 & 31120 & 108.815 & 6.47541 & 18.8506 & 92.2691 & 142.909 & -2022.87 & -1605.81 & 4.00233 & -1.76635 & -2.84643 & -19.1981 \\
|
||||||
|
& -1418.55 & 20724 & 2.01E+09 & 26780 & 82.2471 & 6.03665 & 19.3898 & 106.227 & 133.068 & -2409.09 & -1688.21 & 4.16777 & -2.26334 & -4.32598 & -19.1981 \\
|
||||||
|
& -1011.89 & 18565 & 7.75E+09 & 23560 & 95.8855 & 6.32915 & 17.2525 & 114.955 & 133.399 & -3479.56 & -988.721 & 4.03036 & -1.32162 & -4.01327 & -19.1981 \\
|
||||||
|
& -1408.31 & 15157 & 5.16E+09 & 23300 & 85.4808 & 5.70288 & 18.6271 & 150.783 & 138.84 & -2023.31 & -1078.79 & 4.00918 & -1.95085 & -4.44009 & -19.1981 \\
|
||||||
|
& -748.024 & 23811 & 2.54E+09 & 18940 & 147.794 & 5.43586 & 20.034 & 137.888 & 138.61 & -2389.56 & -1342.49 & 3.70141 & -1.36667 & -4.68997 & -19.1981 \\
|
||||||
|
& -1638.56 & 18165 & 5.13E+08 & 15660 & 116.448 & 5.86329 & 18.6616 & 114.843 & 144.891 & -2692.09 & -1348.9 & 4.1051 & -2.70373 & -4.78687 & -19.1981 \\
|
||||||
|
& -840.918 & 20571 & 5.90E+09 & 29040 & 88.0594 & 6.07913 & 21.4554 & 108.89 & 148.508 & -1373.66 & -709.318 & 4.16242 & -1.27512 & -3.5624 & -19.1981 \\
|
||||||
|
& -918.388 & 19467 & 5.67E+09 & 18540 & 86.3758 & 5.50249 & 16.6631 & 117.034 & 138.953 & -1841.87 & -1118.65 & 4.05446 & -1.43019 & -4.92021 & -19.1981 \\
|
||||||
|
& -1914.4 & 14579 & 4.51E+09 & 35380 & 112.945 & 6.45106 & 17.4196 & 113.865 & 137.99 & -1295.97 & -1449.41 & 4.04415 & -1.21765 & -3.82571 & -19.1981 \\
|
||||||
|
& -1152.74 & 16613 & 8.29E+09 & 27140 & 93.3062 & 6.2383 & 18.8128 & 120.968 & 143.59 & -1384.85 & -1405.02 & 4.21014 & -0.963691 & -5.42752 & -13.344 \\
|
||||||
|
& -1484.78 & 18381 & 5.35E+09 & 36160 & 58.6201 & 6.30713 & 17.9042 & 130.23 & 135.182 & -1930.82 & -1426.85 & 4.00492 & -1.37802 & -4.93164 & -19.1981 \\
|
||||||
|
& -1049.66 & 8486 & 3.92E+09 & 19580 & 112.332 & 6.55806 & 19.9514 & 141.112 & 135.308 & -1799.74 & -884.562 & 4.23598 & -2.511 & -3.99085 & -13.4604 \\
|
||||||
|
& -1093.92 & 16661 & 6.44E+09 & 24500 & 92.3324 & 6.30898 & 20.232 & 121.163 & 130.104 & -1710.14 & -967.321 & 4.06013 & -2.29801 & -6.0234 & -19.1981 \\
|
||||||
|
& -1412.25 & 18188 & 5.81E+09 & 26880 & 109.978 & 5.70144 & 13.2134 & 90.171 & 134.353 & -1785.93 & -819.994 & 3.77693 & -1.0307 & -5.92079 & -19.1981 \\
|
||||||
|
& -1186.11 & 13189 & 3.77E+09 & 19320 & 106.128 & 6.27975 & 18.8899 & 136.097 & 146.582 & -1703.69 & -1298.35 & 3.73847 & -0.944601 & -3.52666 & -19.1981 \\
|
||||||
|
& -1356.38 & 18343 & 2.37E+09 & 13500 & 65.7106 & 5.97634 & 21.5128 & 115.854 & 140.762 & -2070.81 & -1319.27 & 4.18465 & -1.24715 & -5.7363 & -19.1981 \\
|
||||||
|
& -1528.85 & 17689 & 1.03E+10 & 18840 & 107.888 & 6.00707 & 15.2945 & 94.1472 & 144.7 & -3449.27 & -1375.83 & 4.14164 & -1.64993 & -3.02796 & -19.1981 \\
|
||||||
|
& -1063.76 & 15783 & 2.62E+09 & 32600 & 97.6585 & 6.46626 & 22.6138 & 123.843 & 131.62 & -1700.03 & -1291.51 & 4.01155 & -1.00427 & -4.24119 & -19.1981 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -1257.7453333333 & 16246.0666666667 & 4.72E+09 & 2.44E+04 & 9.14E+01 & 6.07E+00 & 1.90E+01 & 1.19E+02 & 1.39E+02 & -1.93E+03 & -1.23E+03 & 4.02E+00 & -1.51E+00 & -4.43E+00 & -1.83E+01 \\
|
||||||
|
Med. & -1222.885 & 16519.5 & 4735540000 & 24510 & 92.13265 & 6.07909 & 19.13985 & 116.444 & 138.8255 & -1792.835 & -1282.96 & 4.03899 & -1.38452 & -4.437605 & -19.1981 \\
|
||||||
|
Std. Dev. & 315.9494375281 & 3243.999978032 & 2288019739.0926 & 6682.1279905999 & 24.08152579 & 0.3362814731 & 2.0863159585 & 15.7480637114 & 5.4144432384 & 542.1878355628 & 253.2605529849 & 0.1313044821 & 0.4955181247 & 0.901789508 & 2.3117761941 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Random Search, 20 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
& -1910.87 & 40054 & 1.87E+10 & 122800 & 316.532 & 13.3092 & 54.0294 & 272.443 & 302.424 & -4041.92 & -1817.15 & 8.84883 & -2.46942 & -5.56505 & -12.4756 \\
|
||||||
|
& -1696.99 & 45772 & 7.63E+09 & 147520 & 297.122 & 14.295 & 50.705 & 272.861 & 303.698 & -4286.27 & -2370.93 & 8.81079 & -1.86537 & -5.17117 & -14.6404 \\
|
||||||
|
& -3381.59 & 40944 & 1.07E+10 & 111960 & 261.007 & 14.2375 & 48.5996 & 293.562 & 312.574 & -2033.17 & -1370.09 & 7.86544 & -1.92465 & -5.47614 & -14.6404 \\
|
||||||
|
& -2309.24 & 43289 & 2.03E+10 & 114080 & 249.051 & 13.181 & 46.7492 & 254.37 & 312.802 & -2243.62 & -1637.31 & 8.75968 & -3.27363 & -7.05667 & -14.6404 \\
|
||||||
|
& -3308.55 & 45667 & 1.77E+10 & 98840 & 258.288 & 13.4705 & 48.7221 & 303.815 & 317.885 & -2736.5 & -1716.73 & 8.69769 & -2.13134 & -8.56678 & -14.6404 \\
|
||||||
|
& -1776.1 & 35152 & 1.97E+10 & 128360 & 282.098 & 14.0992 & 49.7351 & 295.107 & 293.715 & -3083.96 & -1337.39 & 8.904 & -2.72989 & -4.97279 & -14.6404 \\
|
||||||
|
& -2495.52 & 42321 & 1.55E+10 & 149040 & 314.038 & 12.2785 & 48.4086 & 311.61 & 312.538 & -2241.98 & -1953.24 & 8.84628 & -2.22338 & -5.40834 & -14.6404 \\
|
||||||
|
& -1753.65 & 54835 & 2.34E+10 & 144560 & 253.313 & 14.0274 & 52.4705 & 265.832 & 306.999 & -2809.58 & -1617.48 & 9.03187 & -2.36168 & -7.64268 & -14.6404 \\
|
||||||
|
& -1574.77 & 42698 & 1.66E+10 & 105920 & 253.962 & 13.7941 & 47.9362 & 205.073 & 314.335 & -3135.48 & -1120.02 & 8.97631 & -1.76927 & -7.50096 & -12.4756 \\
|
||||||
|
& -2206.58 & 47544 & 1.38E+10 & 92400 & 267.474 & 12.5201 & 50.5015 & 300.654 & 299.266 & -2352.88 & -1563.85 & 8.57557 & -1.90659 & -4.65802 & -14.6404 \\
|
||||||
|
& -2159.17 & 27230 & 1.29E+10 & 135280 & 252.351 & 13.261 & 45.9535 & 202.094 & 304.092 & -3073.25 & -2085.45 & 8.53621 & -1.87303 & -8.01815 & -14.6404 \\
|
||||||
|
& -2138.87 & 42123 & 1.63E+10 & 172680 & 271.911 & 13.2188 & 44.2571 & 238.99 & 310.573 & -2373.5 & -1490.61 & 8.82391 & -1.49354 & -5.60778 & -14.6404 \\
|
||||||
|
& -1491.32 & 41559 & 1.57E+10 & 120560 & 264.467 & 14.7017 & 49.9226 & 270.661 & 314.715 & -4606.54 & -1594.99 & 8.57499 & -2.34868 & -6.03643 & -12.4756 \\
|
||||||
|
& -1716.67 & 43371 & 1.96E+10 & 146160 & 256.312 & 13.2972 & 47.8418 & 296.121 & 299.162 & -2949.32 & -1714.88 & 8.94451 & -1.42826 & -7.75141 & -14.6404 \\
|
||||||
|
& -1353.62 & 30573 & 2.00E+10 & 160960 & 304.457 & 13.6341 & 44.8636 & 281.098 & 302.466 & -2253.05 & -1586.38 & 8.79012 & -3.8665 & -4.02897 & -14.6404 \\
|
||||||
|
& -1876.11 & 37188 & 1.04E+10 & 89680 & 268.29 & 12.9539 & 43.9137 & 251.274 & 294.581 & -3173.41 & -1092.65 & 8.51772 & -2.85364 & -4.28 & -14.6404 \\
|
||||||
|
& -1933.32 & 34504 & 2.32E+10 & 145600 & 255.038 & 14.1056 & 52.7792 & 271.035 & 300.68 & -3193.08 & -1565.67 & 8.89243 & -1.22167 & -6.49108 & -14.6404 \\
|
||||||
|
& -1778.81 & 32208 & 1.21E+10 & 164080 & 250.116 & 13.5012 & 48.5518 & 321.977 & 304.95 & -2310.76 & -1192.91 & 8.99516 & -2.24735 & -3.84925 & -14.6404 \\
|
||||||
|
& -1429.94 & 42230 & 1.93E+10 & 139160 & 279.049 & 14.2125 & 47.4455 & 275.462 & 308.209 & -2107.31 & -2552.48 & 8.58813 & -1.87343 & -8.05159 & -14.6404 \\
|
||||||
|
& -1987.26 & 40385 & 2.04E+10 & 151400 & 276.766 & 12.8164 & 53.0487 & 282.964 & 315.214 & -2678.56 & -2387 & 8.6457 & -3.03476 & -6.09081 & -14.6404 \\
|
||||||
|
& -1213.51 & 37332 & 1.51E+10 & 158560 & 261.681 & 13.675 & 47.6498 & 259.175 & 303.699 & -2531.58 & -1766.16 & 8.84001 & -2.59238 & -3.25015 & -12.4756 \\
|
||||||
|
& -1695.59 & 32437 & 1.90E+10 & 128640 & 275.131 & 13.0249 & 46.3462 & 248.083 & 306.287 & -2644.79 & -1901.53 & 8.95061 & -2.73942 & -4.03195 & -14.6404 \\
|
||||||
|
& -1844.9 & 40572 & 2.06E+10 & 166080 & 249.066 & 14.0993 & 44.662 & 267.558 & 302.1 & -2844.67 & -2131.51 & 9.07241 & -1.88671 & -4.71827 & -14.6404 \\
|
||||||
|
& -2039.62 & 48632 & 1.87E+10 & 138960 & 245.71 & 13.6312 & 38.0255 & 275.48 & 312.316 & -1829.93 & -2004.13 & 8.91483 & -1.52482 & -8.56454 & -14.6404 \\
|
||||||
|
& -1689.48 & 44937 & 1.61E+10 & 124600 & 282.54 & 13.3536 & 45.8805 & 290.598 & 305.559 & -2133.36 & -1566.78 & 8.72377 & -3.27145 & -6.96928 & -14.6404 \\
|
||||||
|
& -1633.96 & 51091 & 1.63E+10 & 117000 & 229.145 & 13.471 & 45.3704 & 307.327 & 311.438 & -1961.85 & -2460.35 & 8.80427 & -2.06181 & -7.33068 & -14.6404 \\
|
||||||
|
& -2439.89 & 45380 & 7.91E+09 & 140880 & 310.028 & 13.7849 & 39.4548 & 280.565 & 301.484 & -3402.53 & -2003.01 & 8.76709 & -2.71809 & -5.60029 & -14.6404 \\
|
||||||
|
& -2364.89 & 38298 & 1.23E+10 & 145760 & 238.505 & 14.0388 & 46.8051 & 290.425 & 309.168 & -2827.15 & -1049.02 & 8.70034 & -2.46217 & -7.9223 & -14.6404 \\
|
||||||
|
& -1855.61 & 47187 & 1.06E+10 & 137680 & 248.9 & 13.9612 & 48.5063 & 302.025 & 305.866 & -2152.73 & -1918.95 & 8.4452 & -2.47409 & -6.79521 & -12.4756 \\
|
||||||
|
& -1260.76 & 46347 & 1.16E+10 & 130600 & 281.027 & 13.9532 & 49.979 & 252.528 & 301.864 & -1840.58 & -1860.37 & 8.04923 & -2.63026 & -7.00734 & -14.6404 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -1943.9053333333 & 41395.3333333333 & 1.61E+10 & 1.34E+05 & 2.68E+02 & 1.36E+01 & 4.76E+01 & 2.75E+02 & 3.06E+02 & -2.73E+03 & -1.75E+03 & 8.73E+00 & -2.31E+00 & -6.15E+00 & -1.43E+01 \\
|
||||||
|
Med. & -1850.255 & 42176.5 & 16269000000 & 138320 & 263.074 & 13.63265 & 47.889 & 275.471 & 305.7125 & -2661.675 & -1715.805 & 8.797195 & -2.298015 & -6.06362 & -14.6404 \\
|
||||||
|
Std. Dev. & 503.7270251575 & 6204.5677408321 & 4287970694.60419 & 21553.682353336 & 22.5100649673 & 0.5550063904 & 3.5839928835 & 28.0219424426 & 6.1634317364 & 693.8827057417 & 399.7555341973 & 0.2654236567 & 0.6045133055 & 1.5251158054 & 0.8205653224 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Random Search, 30 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -2360.73 & 76232 & 3.56E+10 & 354540 & 429.015 & 21.1792 & 78.9986 & 444.386 & 468.28 & -3685.03 & -2174.65 & 13.6966 & -3.39811 & -7.7131 & -18.886 \\
|
||||||
|
& -2223.96 & 77130 & 2.33E+10 & 427440 & 482.271 & 21.4306 & 78.5455 & 477.565 & 481.841 & -3105.05 & -1719.81 & 13.3772 & -2.28782 & -5.96241 & -18.886 \\
|
||||||
|
& -2310.43 & 79924 & 2.53E+10 & 381060 & 438.418 & 22.4267 & 85.9081 & 431.309 & 476.297 & -2717.31 & -3512.41 & 13.4609 & -2.95267 & -9.47434 & -18.886 \\
|
||||||
|
& -1233.77 & 74245 & 3.11E+10 & 405060 & 500.971 & 20.6669 & 77.8205 & 460.647 & 478.937 & -3095.92 & -2547.58 & 13.8835 & -3.62638 & -9.1978 & -18.886 \\
|
||||||
|
& -1528.18 & 72335 & 3.46E+10 & 407400 & 480.637 & 21.006 & 85.0255 & 492.562 & 464.278 & -5541.45 & -2344.66 & 13.9109 & -2.60874 & -9.66453 & -13.3197 \\
|
||||||
|
& -1924.52 & 62337 & 2.96E+10 & 350940 & 400.405 & 21.2461 & 72.6017 & 482.764 & 483.487 & -3317.2 & -2334.39 & 13.194 & -2.35764 & -10.1073 & -18.886 \\
|
||||||
|
& -2423.33 & 65161 & 2.41E+10 & 257700 & 455.803 & 21.3956 & 79.6323 & 453.521 & 483.905 & -2929.27 & -3939.27 & 13.9277 & -2.81222 & -11.1674 & -18.886 \\
|
||||||
|
& -3297.71 & 56282 & 2.22E+10 & 333720 & 423.001 & 21.1405 & 72.1825 & 455.991 & 472.822 & -3685.92 & -2244.92 & 13.5072 & -3.4393 & -8.00828 & -13.3197 \\
|
||||||
|
& -2605.99 & 55514 & 2.99E+10 & 258960 & 432.783 & 20.5493 & 77.5907 & 487.188 & 468.534 & -2270.3 & -1770.48 & 13.8159 & -2.20179 & -7.3187 & -18.886 \\
|
||||||
|
& -2324.67 & 56507 & 3.57E+10 & 321720 & 497.616 & 21.1693 & 81.6804 & 548.596 & 472.64 & -4354.96 & -2232.69 & 13.4288 & -3.11785 & -9.96651 & -18.886 \\
|
||||||
|
& -3097.93 & 68919 & 3.54E+10 & 281160 & 311.272 & 21.3644 & 76.7997 & 473.195 & 482.708 & -3184.8 & -1491.68 & 13.6581 & -4.08066 & -11.396 & -18.886 \\
|
||||||
|
& -1810.75 & 74399 & 2.33E+10 & 325980 & 488.695 & 20.1705 & 79.5259 & 425.339 & 467.469 & -3514.4 & -2784.74 & 13.3882 & -2.20352 & -5.85588 & -13.3197 \\
|
||||||
|
& -2695.32 & 74130 & 2.86E+10 & 286740 & 445.725 & 21.7638 & 78.5654 & 443.915 & 465.322 & -3661.35 & -2224.44 & 13.7479 & -2.58372 & -8.03162 & -18.886 \\
|
||||||
|
& -1394.7 & 45078 & 4.43E+10 & 366540 & 392.949 & 19.8834 & 78.3782 & 443.771 & 474.73 & -2292.49 & -2073.16 & 13.8387 & -1.60751 & -8.19351 & -18.886 \\
|
||||||
|
& -2988.33 & 76470 & 3.30E+10 & 356040 & 403.55 & 22.1061 & 66.4539 & 479.545 & 474.343 & -5213 & -2393.19 & 13.621 & -1.97457 & -7.41504 & -18.886 \\
|
||||||
|
& -2107.9 & 64868 & 2.55E+10 & 335760 & 315.955 & 21.3366 & 76.1817 & 456.456 & 475.502 & -3594.52 & -1990.68 & 13.2762 & -1.95789 & -9.20082 & -18.886 \\
|
||||||
|
& -2979.51 & 66791 & 3.77E+10 & 300600 & 476.424 & 22.0409 & 79.8235 & 481.37 & 480.499 & -3226.79 & -2044.33 & 13.4292 & -3.30836 & -8.31641 & -18.886 \\
|
||||||
|
& -2513.73 & 63196 & 2.56E+10 & 378900 & 308.324 & 22.2978 & 73.8612 & 430.727 & 475.581 & -3321.28 & -2602.19 & 13.2479 & -3.04897 & -7.98816 & -18.886 \\
|
||||||
|
& -1622.69 & 62073 & 2.68E+10 & 370080 & 453.052 & 21.0996 & 80.359 & 480.134 & 477.041 & -3560.86 & -2227.66 & 13.4122 & -2.65343 & -9.57417 & -13.3197 \\
|
||||||
|
& -898.919 & 71119 & 3.36E+10 & 275400 & 394.268 & 21.1665 & 76.3499 & 475.151 & 482.187 & -3499.9 & -2010.88 & 13.8071 & -1.77653 & -6.50943 & -18.886 \\
|
||||||
|
& -2228.64 & 59344 & 2.66E+10 & 365520 & 446.585 & 21.9857 & 81.2511 & 473.327 & 469.027 & -4350.96 & -2530.21 & 13.1101 & -2.27438 & -6.5663 & -18.886 \\
|
||||||
|
& -2524.28 & 70564 & 2.93E+10 & 375960 & 467.46 & 20.6626 & 76.0828 & 458.589 & 473.993 & -3010.55 & -2107.52 & 13.5724 & -2.93552 & -3.36125 & -13.3197 \\
|
||||||
|
& -2141.75 & 69544 & 3.38E+10 & 275040 & 410.104 & 20.1186 & 72.5838 & 471.886 & 472.222 & -2617.06 & -2105.35 & 13.348 & -2.82033 & -5.74818 & -18.886 \\
|
||||||
|
& -2203.34 & 72793 & 3.62E+10 & 380220 & 406.591 & 22.0699 & 71.4693 & 407.417 & 484.37 & -2650.34 & -2240.99 & 13.9337 & -1.66277 & -8.93489 & -18.886 \\
|
||||||
|
& -1836.75 & 73045 & 3.98E+10 & 379800 & 514.007 & 22.242 & 79.9503 & 478.007 & 471.384 & -4044.69 & -2652.98 & 13.6287 & -2.44844 & -7.85381 & -18.886 \\
|
||||||
|
& -1787.41 & 50713 & 3.85E+10 & 296100 & 445.226 & 20.0734 & 76.9451 & 459.307 & 448.98 & -3725.34 & -1302.85 & 13.9199 & -2.28859 & -7.12064 & -18.886 \\
|
||||||
|
& -3185.05 & 70905 & 2.69E+10 & 374640 & 424.633 & 22.0167 & 74.4666 & 459.963 & 469.897 & -4000.25 & -2647.59 & 13.8378 & -3.37299 & -10.0858 & -18.886 \\
|
||||||
|
& -2551.98 & 57495 & 3.29E+10 & 370380 & 352.025 & 21.2376 & 81.8073 & 458.902 & 480.163 & -2257.61 & -2125.84 & 13.3929 & -1.83939 & -6.25541 & -18.886 \\
|
||||||
|
& -2406.77 & 78787 & 2.80E+10 & 403560 & 472.017 & 20.9728 & 79.011 & 416.437 & 481.494 & -2723.46 & -1653.13 & 13.5631 & -1.96041 & -5.54987 & -13.3197 \\
|
||||||
|
& -2783.41 & 66765 & 2.70E+10 & 322920 & 330.427 & 21.3975 & 80.8424 & 466.09 & 467.905 & -3393.49 & -2011.66 & 13.5916 & -2.33285 & -5.45623 & -18.886 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -2266.4149666667 & 67088.8333333333 & 3.08E+10 & 3.44E+05 & 4.27E+02 & 2.13E+01 & 7.77E+01 & 4.62E+02 & 4.74E+02 & -3.42E+03 & -2.27E+03 & 1.36E+01 & -2.60E+00 & -7.93E+00 & -1.78E+01 \\
|
||||||
|
Med. & -2317.55 & 69231.5 & 29737350000 & 355290 & 435.6005 & 21.24185 & 78.46185 & 460.305 & 474.5365 & -3357.385 & -2226.05 & 13.582 & -2.51608 & -7.99822 & -18.886 \\
|
||||||
|
Std. Dev. & 584.4233609571 & 8739.8871218282 & 5587568894.7127 & 47357.674511872 & 57.409444418 & 0.6956118635 & 4.0959187591 & 26.9132515273 & 7.5943841166 & 775.2133686027 & 526.201488921 & 0.2412609758 & 0.6322303515 & 1.8774263418 & 2.2645829281 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Local Search, 10 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -1722.16 & 0.03025 & 1.31E+10 & 5978.56 & 69.3764 & 7.53377 & 9.04847 & 181.426 & 151.893 & -754.573 & -2406.31 & 4.19767 & -2.05904 & -8.82787 & -8.1772 \\
|
||||||
|
& -2788.25 & 0.03025 & 1.59E+10 & 29804.2 & 69.0028 & 6.62654 & 9.08272 & 121.985 & 157.316 & 482.297 & -2056.85 & 4.27003 & -0.232247 & -8.81589 & -8.1772 \\
|
||||||
|
& -1839.66 & 0.03025 & 2.15E+10 & 17368.8 & 63.9238 & 7.28417 & 9.19482 & 170.218 & 140.056 & -1188.15 & -2643.43 & 4.22798 & 0.00103445 & -8.76307 & -8.1772 \\
|
||||||
|
& -3498.88 & 0.0300057 & 1.25E+10 & 20604.7 & 68.4473 & 5.87015 & 10.7235 & 109.273 & 156.978 & 765.691 & -2240.2 & 4.50026 & 0.0612122 & -8.79559 & -19.1981 \\
|
||||||
|
& -2373.7 & 0.03025 & 1.93E+10 & 13430.3 & 34.2408 & 7.98904 & 9.18401 & 130.203 & 151.598 & 357.187 & -2195.2 & 4.49177 & 0.843228 & -8.72636 & -19.2635 \\
|
||||||
|
& -2669.32 & 0.03025 & 1.38E+10 & 13495.7 & 60.3948 & 7.3257 & 9.63616 & 126.236 & 150.787 & -701.815 & -2689.55 & 4.52112 & -0.563567 & -8.62028 & -19.2635 \\
|
||||||
|
& -2412.63 & 0.0288532 & 1.22E+10 & 30023.6 & 74.2869 & 7.63259 & 9.07038 & 153.164 & 157.549 & -394.844 & -2613.69 & 4.45488 & 0.147295 & -8.74889 & -19.2635 \\
|
||||||
|
& -2452.59 & 0.03025 & 2.45E+10 & 22446.9 & 42.4768 & 6.59059 & 9.49866 & 167.659 & 153.588 & -2790.05 & -884.232 & 4.49433 & -1.58924 & -8.68392 & -22.0872 \\
|
||||||
|
& -2827.23 & 0.03025 & 6.57E+09 & 18043.7 & 64.0325 & 6.60687 & 9.40728 & 137.864 & 155.09 & 167.839 & -3035.03 & 4.10601 & 2.38E-05 & -8.77553 & -8.1772 \\
|
||||||
|
& -3577.85 & 0.0248398 & 1.74E+10 & 25988.4 & 74.2439 & 6.73684 & 9.28461 & 132.581 & 144.729 & -91.2695 & -1509.7 & 4.46538 & -1.92424 & -8.45917 & -11.5852 \\
|
||||||
|
& -1326.85 & 0.03025 & 2.68E+10 & 7867.05 & 70.423 & 7.52619 & 9.06149 & 111.908 & 154.855 & -117.596 & -2566.91 & 4.49956 & -0.783772 & -8.59181 & -8.1772 \\
|
||||||
|
& -3735.8 & 0.03025 & 1.80E+10 & 22752.2 & 50.6174 & 6.33447 & 9.1547 & 139.641 & 152.514 & -1316.55 & -2518 & 4.47642 & -0.0463014 & -8.75746 & -19.27 \\
|
||||||
|
& -2393.27 & 0.03025 & 1.14E+10 & 14274.5 & 40.5886 & 6.41117 & 10.6114 & 224.812 & 151.689 & -1288.83 & -2630.38 & 3.96854 & -1.52409 & -8.57556 & -8.1772 \\
|
||||||
|
& -2136.79 & 0.0251012 & 1.16E+10 & 13427.8 & 74.9665 & 6.62278 & 9.18783 & 124.629 & 154.597 & -1036 & -2279.34 & 4.08355 & -0.337027 & -8.83124 & -19.27 \\
|
||||||
|
& -2551.26 & 0.03025 & 2.09E+10 & 14787 & 71.8753 & 6.1052 & 10.2295 & 144.82 & 149.299 & -2255.52 & -2667.36 & 4.48691 & -0.522676 & -8.6941 & -19.27 \\
|
||||||
|
& -2610.58 & 0.0275071 & 2.19E+10 & 23744.9 & 51.3633 & 6.37208 & 9.21428 & 170.428 & 154.633 & 499.723 & -1960.24 & 4.49717 & -0.225219 & -8.82633 & -22.0364 \\
|
||||||
|
& -3301.42 & 0.03025 & 1.42E+10 & 17620.1 & 58.3238 & 6.90171 & 9.09557 & 109.074 & 155.68 & -413.377 & -2868.24 & 4.28546 & -0.609139 & -8.78285 & -22.0364 \\
|
||||||
|
& -2610.41 & 0.0293121 & 1.09E+10 & 27801.8 & 95.0175 & 6.93148 & 9.15471 & 167.973 & 150.033 & 689.394 & -2833.55 & 4.28827 & 0.0360106 & -8.26686 & -8.1772 \\
|
||||||
|
& -3183.07 & 0.0290287 & 1.69E+10 & 20238.5 & 60.3234 & 6.74264 & 9.67354 & 102.391 & 156.435 & -1058.05 & -2757.67 & 4.00982 & -0.78522 & -8.08889 & -8.1772 \\
|
||||||
|
& -2432.85 & 0.0301624 & 3.56E+10 & 22842.4 & 35.3722 & 6.46636 & 11.4128 & 170.701 & 148.289 & 1105.59 & -2210.27 & 4.77573 & -1.19427 & -8.509 & -21.9278 \\
|
||||||
|
& -2630.3 & 0.0278163 & 2.73E+10 & 25658.9 & 83.3985 & 6.10535 & 9.14037 & 121.269 & 154.065 & -958.512 & -2575.3 & 4.48104 & -1.21108 & -7.78537 & -11.6076 \\
|
||||||
|
& -2095.89 & 0.03025 & 9.81E+09 & 20270.9 & 40.2026 & 7.20097 & 9.0472 & 104.663 & 154.173 & -505.146 & -1771.42 & 4.11262 & -0.613923 & -8.83158 & -8.1772 \\
|
||||||
|
& -3301.53 & 0.0302264 & 1.35E+10 & 13708.2 & 62.5321 & 8.05472 & 11.0529 & 165.973 & 155.957 & -2069.58 & -2830.66 & 4.2232 & 0.124692 & -8.83616 & -19.2635 \\
|
||||||
|
& -2314.48 & 0.0296459 & 1.98E+10 & 21595.5 & 66.9216 & 5.32096 & 9.18803 & 119.401 & 156.968 & -728.852 & -3393.19 & 4.37753 & -1.01123 & -8.81274 & -11.6076 \\
|
||||||
|
& -3380.47 & 0.0302365 & 9.73E+09 & 26948.7 & 96.7156 & 7.25999 & 9.21712 & 187.299 & 153.919 & -213.715 & -2813.35 & 3.95788 & 0.110539 & -8.7079 & -8.1772 \\
|
||||||
|
& -3143.51 & 0.0199678 & 2.03E+10 & 11383 & 68.7048 & 6.28303 & 9.25189 & 188.644 & 157.746 & 110.405 & -1688.99 & 4.49839 & -1.74384 & -8.7247 & -8.1772 \\
|
||||||
|
& -2847.49 & 0.029854 & 1.84E+10 & 23915.5 & 72.8991 & 6.63326 & 9.38279 & 105.037 & 150.94 & 862.177 & -2078.53 & 4.58513 & -1.08988 & -8.61938 & -11.5852 \\
|
||||||
|
& -2926.46 & 0.03025 & 1.77E+10 & 21728.5 & 81.07 & 6.60333 & 9.31926 & 150.771 & 152.901 & -1333.47 & -2068.27 & 4.05192 & -0.656969 & -8.55918 & -21.9278 \\
|
||||||
|
& -2195.99 & 0.03025 & 2.26E+10 & 19417.8 & 76.3799 & 6.66027 & 9.09237 & 125.473 & 153.158 & -1148.96 & -2269.14 & 4.4759 & -1.44312 & -8.40539 & -8.1772 \\
|
||||||
|
& -2610.57 & 0.03025 & 1.95E+10 & 10095 & 51.5363 & 6.21274 & 9.11763 & 159.311 & 154.965 & -304.918 & -2250.28 & 4.4864 & 0.365686 & -8.75928 & -8.1772 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -2663.042 & 0.02921857 & 1.75E+10 & 1.92E+04 & 6.43E+01 & 6.76E+00 & 9.49E+00 & 1.44E+02 & 1.53E+02 & -5.21E+02 & -2.38E+03 & 4.35E+00 & -6.16E-01 & -8.64E+00 & -1.39E+01 \\
|
||||||
|
Med. & -2610.575 & 0.03025 & 17541950000 & 20254.7 & 67.68445 & 6.6299 & 9.20455 & 138.7525 & 153.992 & -459.2615 & -2462.155 & 4.46013 & -0.586353 & -8.72553 & -11.5964 \\
|
||||||
|
Std. Dev. & 565.9521286104 & 0.0022586778 & 6227253551.6775 & 6364.105861632 & 15.8722794272 & 0.6200294914 & 0.6404997265 & 30.5973750771 & 3.8796949489 & 947.6193603132 & 502.5410994529 & 0.2088816019 & 0.7371789862 & 0.2404532 & 5.8867273045 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Local Search, 20 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -5793.64 & 0.0605 & 2.54E+10 & 84243.8 & 0.00544314 & 15.7534 & 22.794 & 388.743 & 318.54 & 872.22 & -4878.35 & 9.29745 & -0.868549 & -17.4433 & -18.4163 \\
|
||||||
|
& -6425.36 & 0.0605 & 3.40E+10 & 83330.5 & 2.59579 & 15.18 & 19.4765 & 282.217 & 326.136 & -890.302 & -5205.81 & 9.19442 & -1.71722 & -18.36 & -18.1189 \\
|
||||||
|
& -5773.94 & 0.0605 & 5.03E+10 & 81685.7 & 0.0325362 & 14.6438 & 19.2967 & 414.614 & 325.683 & -38.8792 & -4808.13 & 8.96731 & -1.28757 & -18.4541 & -18.1189 \\
|
||||||
|
& -4312.14 & 0.0605 & 4.46E+10 & 84028.2 & 0.012734 & 14.8892 & 20.1529 & 248.993 & 329.517 & -49.3419 & -5762.01 & 8.95851 & -0.769716 & -18.3303 & -11.5925 \\
|
||||||
|
& -5201.29 & 0.0528605 & 2.90E+10 & 85961.1 & 0.0175917 & 14.6958 & 20.7801 & 295.784 & 326.719 & 131.108 & -4284.49 & 9.19177 & -0.978247 & -17.4535 & -11.5925 \\
|
||||||
|
& -5437.26 & 0.0484009 & 4.27E+10 & 81010.4 & 0.0128073 & 14.7319 & 20.2851 & 308.26 & 321.815 & -3179.98 & -5910.02 & 8.83616 & -1.10339 & -18.3132 & -12.1791 \\
|
||||||
|
& -5911.51 & 0.0605 & 4.18E+10 & 82890.4 & 0.0127846 & 15.3506 & 24.845 & 297.244 & 320.763 & 124.661 & 359.29 & 9.15435 & -0.821465 & -18.5011 & -11.5925 \\
|
||||||
|
& -5852.3 & 0.0605 & 3.89E+10 & 80465.7 & 0.50121 & 13.8042 & 24.586 & 358.358 & 328.323 & -1240.58 & -5701.28 & 9.48494 & -2.10437 & -17.7341 & -11.5925 \\
|
||||||
|
& -4549.95 & 0.0554514 & 2.43E+10 & 82872.7 & 0.0150484 & 14.0756 & 21.9814 & 378.772 & 320.122 & 866.459 & -4804.78 & 9.32682 & 0.177762 & -18.4128 & -11.5924 \\
|
||||||
|
& -5615.93 & 0.0594314 & 3.87E+10 & 85395.4 & 0.0177641 & 14.9938 & 21.737 & 348.39 & 330.393 & -40.7054 & -803.14 & 9.46576 & -0.757524 & -17.8996 & -12.1455 \\
|
||||||
|
& -6128.66 & 0.0599989 & 5.86E+09 & 82482.7 & 0.022684 & 15.7937 & 23.5337 & 318.578 & 313.61 & -1594.82 & -5561.16 & 9.47295 & -0.0502173 & -18.3411 & -12.1791 \\
|
||||||
|
& -5911.12 & 0.06046 & 3.82E+10 & 79597.1 & 0.00540948 & 14.6048 & 19.8786 & 343.956 & 316.997 & -1549.64 & -4889 & 9.12806 & 0.593797 & -17.5708 & -11.5925 \\
|
||||||
|
& -5378.96 & 0.0546049 & 3.55E+10 & 79844.9 & 0.513188 & 13.4111 & 20.819 & 237.504 & 323.974 & -236.878 & -2783.49 & 9.40669 & -1.5814 & -17.8182 & -11.5925 \\
|
||||||
|
& -6208.09 & 0.0605 & 4.11E+10 & 82806.8 & 0.0423564 & 13.6263 & 19.9244 & 338.475 & 330.509 & 145.801 & -5149.57 & 8.85904 & 0.0709934 & -17.7823 & -11.5925 \\
|
||||||
|
& -6365.98 & 0.0604318 & 3.95E+10 & 82980.4 & 0.00544314 & 14.5561 & 29.7907 & 373.119 & 316.579 & -3981.64 & -589.742 & 9.45594 & -0.734831 & -17.6115 & -12.1791 \\
|
||||||
|
& -4865.5 & 0.0605 & 2.70E+10 & 81023.3 & 0.022684 & 14.1321 & 19.6227 & 264.108 & 329.156 & -895.345 & -5298.19 & 9.31191 & -0.709717 & -17.9523 & -12.1791 \\
|
||||||
|
& -5753.31 & 0.0592495 & 5.53E+10 & 83182.6 & 0.0177641 & 14.3879 & 19.4463 & 326.461 & 329.266 & -736.031 & -36.6239 & 9.20092 & -1.01499 & -18.0679 & -11.5925 \\
|
||||||
|
& -4964.05 & 0.0495827 & 3.22E+10 & 86350.6 & 7.65341 & 14.4621 & 19.5375 & 354.537 & 329.072 & -98.6246 & -4398.36 & 9.44551 & -1.39255 & -18.4823 & -11.5925 \\
|
||||||
|
& -5161.97 & 0.0605 & 3.97E+10 & 79778.4 & 0.0244744 & 14.5572 & 22.6856 & 320.654 & 312.234 & -6486.32 & -4736.91 & 9.2983 & -1.06507 & -17.1146 & -11.5925 \\
|
||||||
|
& -5635.57 & 0.0605 & 4.11E+10 & 81383.2 & 0.0054374 & 14.5234 & 22.1856 & 287.145 & 323.708 & 562.055 & -5532.35 & 9.13131 & 0.712591 & -18.4878 & -12.1791 \\
|
||||||
|
& -5477.18 & 0.0605 & 2.88E+10 & 83253.2 & 0.012837 & 14.4646 & 20.9163 & 383.667 & 322.139 & -2281.67 & -4092.8 & 9.208 & -2.1219 & -18.2742 & -11.5925 \\
|
||||||
|
& -5240.77 & 0.0605 & 5.57E+10 & 82110.1 & 13.4906 & 15.0371 & 19.3914 & 205.418 & 324.571 & -3193.3 & -6020.24 & 8.90623 & -2.27157 & -16.7489 & -11.5924 \\
|
||||||
|
& -5635.19 & 0.0605 & 2.74E+10 & 84813.3 & 0.0447807 & 15.5438 & 19.7167 & 245.99 & 327.78 & -1235.47 & -3310.1 & 9.39152 & -1.5261 & -17.3734 & -11.5925 \\
|
||||||
|
& -3246.38 & 0.0605 & 3.80E+10 & 83219.6 & 0.00544314 & 14.7169 & 23.0755 & 303.558 & 313.476 & -1673.84 & -4972.31 & 8.87996 & 0.484031 & -18.4883 & -11.5925 \\
|
||||||
|
& -6168.66 & 0.0558529 & 4.47E+10 & 84716.8 & 2.94032 & 13.5461 & 19.8038 & 247.696 & 325.746 & -550.653 & -5601.88 & 9.47937 & -0.144748 & -17.0377 & -11.5925 \\
|
||||||
|
& -4213.77 & 0.0581465 & 3.19E+10 & 80103 & 0.00538645 & 16.5718 & 25.1075 & 282.189 & 323.021 & -137.129 & -5451.53 & 9.11948 & -1.01489 & -17.6702 & -11.5729 \\
|
||||||
|
& -3384.87 & 0.0595524 & 4.13E+10 & 82072.8 & 0.00541907 & 15.7239 & 19.2661 & 329.103 & 324.07 & -1101.79 & -4311.6 & 9.49093 & -0.162895 & -18.1284 & -11.5925 \\
|
||||||
|
& -5437.75 & 0.0590881 & 2.77E+10 & 87335.7 & 0.486232 & 13.3387 & 19.7886 & 289.14 & 323.126 & -2199.9 & -5897.58 & 9.47676 & 0.0210599 & -17.9211 & -18.4163 \\
|
||||||
|
& -4154.64 & 0.0605 & 3.50E+10 & 80441.7 & 0.0226616 & 15.9632 & 19.6977 & 323.442 & 319.648 & -2319.97 & -4943.11 & 9.3123 & -1.96752 & -16.6269 & -11.5925 \\
|
||||||
|
& -4727.06 & 0.0605 & 5.57E+10 & 83475 & 0.0300529 & 15.2459 & 20.5533 & 339.345 & 325.59 & 2361.6 & -3509.27 & 9.4069 & -2.61652 & -17.0723 & -11.5925 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -5297.76 & 0.05870373 & 3.70E+10 & 8.28E+04 & 9.53E-01 & 1.47E+01 & 2.14E+01 & 3.15E+02 & 3.23E+02 & -1.02E+03 & -4.30E+03 & 9.24E+00 & -8.91E-01 & -1.78E+01 & -1.26E+01 \\
|
||||||
|
Med. & -5457.465 & 0.0605 & 38465350000 & 82881.55 & 0.02021285 & 14.6698 & 20.4192 & 319.616 & 324.022 & -813.1665 & -4883.675 & 9.297875 & -0.923398 & -17.91035 & -11.5925 \\
|
||||||
|
Std. Dev. & 817.3212483051 & 0.0033237366 & 10498474328.5921 & 2030.9259258268 & 2.8130712982 & 0.7900945894 & 2.3862355564 & 50.2442400537 & 5.1944841414 & 1705.6218308227 & 1790.4080474217 & 0.210871267 & 0.8822055188 & 0.5520760648 & 2.2743612411 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Local Search, 30 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -8720.03 & 0.09075 & 6.14E+10 & & 468222 & 25.6736 & 29.4629 & 465.724 & 492.399 & -709.211 & -9489.6 & 14.5445 & & -23.8513 & -19.687 \\
|
||||||
|
& -6962.47 & 0.09075 & 8.45E+10 & & 544786 & 26.105 & 37.1947 & 476.184 & 495.891 & -2092.66 & -1548.36 & 14.1196 & & -27.6662 & -18.039 \\
|
||||||
|
& -6961.87 & 0.0899048 & 4.08E+10 & & 566160 & 22.9069 & 32.2468 & 572.709 & 495.119 & -724.496 & -1085.52 & 13.6181 & & -26.3649 & -9.98237 \\
|
||||||
|
& -7022.1 & 0.09075 & 4.80E+10 & & 507124 & 23.5184 & 32.8019 & 474.808 & 504.87 & 1441.2 & -298.734 & 14.6858 & & -27.5487 & -11.4003 \\
|
||||||
|
& -7554.77 & 0.09075 & 5.62E+10 & & 534480 & 21.3856 & 45.2881 & 407.728 & 499.809 & -1117.15 & -6155.89 & 14.1285 & & -28.0375 & -18.8073 \\
|
||||||
|
& -7930.32 & 0.0901661 & 3.89E+10 & & 507272 & 21.786 & 39.2269 & 585.973 & 489.683 & -559.234 & -6021.34 & 14.2482 & & -26.9184 & -17.1526 \\
|
||||||
|
& -7671.66 & 0.09075 & 6.33E+10 & & 544748 & 23.9949 & 39.7063 & 410.723 & 496.021 & -706.327 & -8420.73 & 14.4966 & & -27.7328 & -18.8073 \\
|
||||||
|
& -9153.89 & 0.0896031 & 5.37E+10 & & 527930 & 22.0799 & 31.5884 & 432.908 & 498.172 & -1000.39 & -7023.77 & 14.0189 & & -27.4268 & -19.6821 \\
|
||||||
|
& -7475.88 & 0.0825127 & 7.43E+10 & & 441660 & 24.9363 & 30.6169 & 441.306 & 495.542 & 727.837 & -2984.97 & 14.1608 & & -27.0882 & -18.1224 \\
|
||||||
|
& -7140.58 & 0.09075 & 6.14E+10 & & 461520 & 22.1797 & 32.6729 & 418.557 & 493.104 & -2038.59 & -8148.21 & 13.8726 & & -27.3871 & -17.6842 \\
|
||||||
|
& -6765.1 & 0.09075 & 5.12E+10 & & 442200 & 22.8712 & 30.8062 & 469.99 & 489.318 & -3404.86 & -1965.15 & 13.9313 & & -27.7029 & -11.4003 \\
|
||||||
|
& -7278.84 & 0.0705322 & 4.88E+10 & & 387826 & 23.6452 & 29.7264 & 530.633 & 493.766 & 659.589 & -2082.03 & 14.7356 & & -26.4235 & -18.039 \\
|
||||||
|
& -9804.77 & 0.09075 & 3.16E+10 & & 594900 & 22.2526 & 30.373 & 506.952 & 499.083 & -6243.21 & -7264.67 & 14.0242 & & -25.9648 & -11.4003 \\
|
||||||
|
& -7258.63 & 0.0815248 & 5.80E+10 & & 506574 & 22.0017 & 38.3344 & 481.961 & 499.895 & -182.164 & -7375.24 & 14.1801 & & -26.8838 & -17.1526 \\
|
||||||
|
& -7752.47 & 0.09075 & 6.81E+10 & & 594960 & 23.3336 & 32.0357 & 448.093 & 497.64 & 121.125 & -6137.77 & 13.9758 & & -25.8605 & -19.5175 \\
|
||||||
|
& -6427.54 & 0.0901747 & 6.75E+10 & & 499020 & 24.788 & 39.219 & 550.8 & 497.778 & 197.675 & -6537.16 & 14.0494 & & -26.9742 & -18.3945 \\
|
||||||
|
& -8048.75 & 0.09075 & 7.70E+10 & & 345160 & 22.4005 & 31.9327 & 351.905 & 500.411 & -5356.76 & -5318.86 & 14.3997 & & -25.4607 & -18.3945 \\
|
||||||
|
& -6922.6 & 0.0897579 & 7.72E+10 & & 424932 & 21.4528 & 33.8182 & 440.397 & 494.772 & -670.487 & 239.404 & 13.8857 & & -25.2136 & -18.039 \\
|
||||||
|
& -7534.96 & 0.09075 & 5.81E+10 & & 510105 & 24.7605 & 34.6564 & 481.573 & 493.94 & -2080.9 & -6633.34 & 14.5802 & & -27.3547 & -11.4003 \\
|
||||||
|
& -6626.39 & 0.0840877 & 6.24E+10 & & 356637 & 26.2454 & 36.2963 & 508.941 & 499.447 & -1921.38 & -7225.03 & 14.007 & & -28.186 & -19.6828 \\
|
||||||
|
& -8739.22 & 0.0833573 & 6.99E+10 & & 546298 & 21.9408 & 33.581 & 543.216 & 491.856 & -1567.4 & -6976.34 & 13.9828 & & -28.2896 & -11.3613 \\
|
||||||
|
& -8818.69 & 0.0885963 & 3.97E+10 & & 438240 & 21.1752 & 36.2528 & 460.593 & 499.554 & 72.1365 & -6627.36 & 14.0521 & & -27.4075 & -11.4003 \\
|
||||||
|
& -7258.09 & 0.0861417 & 3.49E+10 & & 544831 & 25.094 & 30.5593 & 548.84 & 499.339 & -5073.7 & -8456.63 & 14.2453 & & -25.5684 & -19.8561 \\
|
||||||
|
& -8087.75 & 0.0903371 & 5.46E+10 & & 446157 & 22.9032 & 32.1424 & 421.646 & 492.63 & -3900.26 & 1353.84 & 13.7392 & & -26.6823 & -19.2993 \\
|
||||||
|
& -8482.43 & 0.090039 & 4.66E+10 & & 552581 & 22.3861 & 36.5625 & 457.61 & 499.245 & 1.35286 & -7844.48 & 13.7055 & & -28.0855 & -19.687 \\
|
||||||
|
& -6528.81 & 0.09075 & 6.70E+10 & & 498480 & 19.5627 & 34.625 & 464.465 & 492.466 & -6965.86 & -9712.34 & 14.1021 & & -26.9693 & -18.8073 \\
|
||||||
|
& -8463.27 & 0.09075 & 6.34E+10 & & 479280 & 24.0583 & 34.4645 & 608.036 & 473.426 & -7745.87 & -7636.44 & 14.4946 & & -26.258 & -12.3742 \\
|
||||||
|
& -8956.81 & 0.0893575 & 4.27E+10 & & 431907 & 23.3802 & 37.1508 & 605.869 & 486.893 & -6378.37 & -7262.28 & 13.9651 & & -27.4611 & -12.3742 \\
|
||||||
|
& -8443.14 & 0.09075 & 4.63E+10 & & 660240 & 23.5017 & 36.0516 & 503.756 & 478.32 & -1278.35 & -4455.43 & 13.9693 & & -27.2419 & -17.1526 \\
|
||||||
|
& -6843.66 & 0.0894151 & 7.43E+10 & & 481320 & 23.2531 & 29.9386 & 478.08 & 499.71 & -4075.12 & -5839.02 & 14.2467 & & -27.0834 & -9.98237 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -7721.183 & 0.0885336 & 5.74E+10 & 4.95E+05 & 2.32E+01 & 3.43E+01 & 4.85E+02 & 4.95E+02 & -2.09E+03 & -5.50E+03 & -5.50E+03 & 1.41E+01 & -2.69E+01 & -2.69E+01 & -1.62E+01 \\
|
||||||
|
Med. & -7544.865 & 0.0902559 & 58031700000 & 502797 & 23.08 & 33.6996 & 475.496 & 495.7165 & -1197.75 & -6582.26 & -6582.26 & 14.0771 & -27.0858 & -27.0858 & -18.039 \\
|
||||||
|
Std. Dev. & 879.654776568 & 0.004344398 & 13715570303.9744 & 70598.3086040107 & 1.561052308 & 3.7073113996 & 61.9062241299 & 6.4821907987 & 2517.8253815732 & 3015.9014409368 & 3015.9014409368 & 0.2842525403 & 1.0005521456 & 1.0005521456 & 3.6031873219 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Iterative Local Search, 10 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -2353.48 & 0.03025 & 1.88E+10 & 24168.4 & 59.988 & 6.39537 & 9.35607 & 117.932 & 155.879 & -145.889 & -1646.86 & 4.03982 & -0.302471 & -8.64558 & -11.5852 \\
|
||||||
|
& -2294.56 & 0.0278096 & 2.01E+10 & 23308.8 & 54.3803 & 7.99499 & 9.10285 & 166.587 & 155.557 & -479.37 & -2949.33 & 4.21134 & -0.408084 & -8.79464 & -8.1772 \\
|
||||||
|
& -2847.49 & 0.0296792 & 1.70E+10 & 32302.9 & 46.5895 & 7.22062 & 9.07374 & 161.811 & 148.705 & -56.1015 & -2988.2 & 3.99908 & -0.616554 & -8.82742 & -8.1772 \\
|
||||||
|
& -1682.61 & 0.0286486 & 1.90E+10 & 26081.4 & 61.7176 & 6.47021 & 10.7403 & 138.539 & 151.489 & -235.288 & -2321.48 & 4.49389 & -1.30566 & -8.4728 & -8.1772 \\
|
||||||
|
& -3163.22 & 0.03025 & 1.44E+10 & 15665.2 & 46.493 & 7.87339 & 11.6635 & 143.48 & 152.545 & -1197.31 & -3124.24 & 4.06124 & -1.18534 & -8.60302 & -8.1772 \\
|
||||||
|
& -1564.28 & 0.0292164 & 3.22E+10 & 9609.37 & 63.0245 & 6.84956 & 9.12636 & 77.5438 & 158.222 & -1306.25 & -2504.62 & 4.49975 & -0.577118 & -8.69359 & -8.1772 \\
|
||||||
|
& -3005.3 & 0.03025 & 2.60E+10 & 29121.3 & 42.5946 & 7.03493 & 11.0323 & 121.662 & 156.691 & -457.272 & -2495.34 & 4.48833 & -0.676501 & -8.81122 & -8.1772 \\
|
||||||
|
& -2196.03 & 0.03025 & 9.37E+09 & 24244.2 & 67.6498 & 6.47648 & 9.77288 & 118.753 & 156.684 & -78.0806 & -1123.95 & 4.65848 & -1.34652 & -8.80146 & -8.1772 \\
|
||||||
|
& -2451.6 & 0.0301868 & 1.89E+10 & 31828.8 & 54.9617 & 7.78021 & 9.1522 & 198.115 & 153.576 & -44.7555 & -1719.94 & 4.49672 & 0.0368576 & -8.82573 & -8.1772 \\
|
||||||
|
& -2649.5 & 0.03025 & 2.35E+10 & 24122.5 & 78.0887 & 7.02654 & 9.08477 & 145.058 & 146.57 & -359.588 & -2690.69 & 4.44189 & -0.600237 & -8.85059 & -8.1772 \\
|
||||||
|
& -1978.6 & 0.0302292 & 1.92E+10 & 26676.1 & 67.6724 & 6.72453 & 9.32724 & 184.502 & 154.944 & -978.036 & -2567.04 & 4.49746 & 0.352732 & -8.72541 & -8.1772 \\
|
||||||
|
& -2590.35 & 0.03025 & 1.86E+10 & 22140.2 & 67.8106 & 7.22723 & 9.27195 & 184.804 & 149.624 & 228.958 & -2156.38 & 4.63231 & 0.000805103 & -8.82982 & -19.2635 \\
|
||||||
|
& -2492.14 & 0.0302253 & 1.45E+10 & 23038.2 & 54.4243 & 6.35673 & 9.37548 & 64.1279 & 158.208 & -1552.16 & -2516.99 & 4.1252 & -0.977698 & -8.62522 & -8.1772 \\
|
||||||
|
& -3024.64 & 0.0302113 & 2.35E+10 & 21144 & 68.705 & 7.64069 & 15.3717 & 175.471 & 156.45 & -188.369 & -2261.48 & 4.50388 & -0.189495 & -8.82071 & -19.27 \\
|
||||||
|
& -2787.77 & 0.0299542 & 3.51E+10 & 30959.5 & 87.3445 & 7.258 & 9.17457 & 174.921 & 154.939 & -549.431 & -2337.16 & 4.51863 & -0.100004 & -8.71327 & -11.6076 \\
|
||||||
|
& -2412.16 & 0.03025 & 2.46E+10 & 10038.6 & 49.23 & 7.00135 & 9.18972 & 203.733 & 153.641 & -279.738 & -813.436 & 4.13809 & 0.0861884 & -8.70443 & -22.0364 \\
|
||||||
|
& -2156.47 & 0.03025 & 3.45E+09 & 7563.02 & 90.5625 & 6.32097 & 9.77328 & 161.579 & 153.295 & -83.3801 & -1182.55 & 4.45449 & -0.394575 & -8.82684 & -19.2635 \\
|
||||||
|
& -3044.69 & 0.03025 & 1.32E+10 & 18228.9 & 53.0633 & 6.98713 & 9.22125 & 139.567 & 151.861 & -101.129 & -2341.09 & 4.27863 & -0.295665 & -8.4791 & -20.3627 \\
|
||||||
|
& -2294.74 & 0.0128203 & 6.91E+09 & 13513 & 64.0312 & 8.51433 & 9.30183 & 142.512 & 156.996 & -1863.03 & -1398.12 & 4.49987 & -0.496323 & -8.84694 & -8.1772 \\
|
||||||
|
& -2946.18 & 0.0182463 & 4.41E+09 & 10620.3 & 76.5551 & 7.01442 & 9.13523 & 175.317 & 152.924 & 63.6296 & -2397.78 & 4.50181 & 0.0984758 & -8.7853 & -21.9278 \\
|
||||||
|
& -2412.71 & 0.03025 & 6.12E+09 & 21231.5 & 47.6436 & 6.37215 & 9.12583 & 152.503 & 151.491 & 662.344 & -2139.36 & 4.50024 & -0.769488 & -8.30502 & -8.1772 \\
|
||||||
|
& -2669.73 & 0.03025 & 2.28E+10 & 18635 & 46.5956 & 6.62651 & 9.1969 & 179.575 & 155.756 & -1622.23 & -2579.85 & 4.49683 & -0.639761 & -8.72133 & -19.1981 \\
|
||||||
|
& -2235.49 & 0.03025 & 1.40E+10 & 28141.5 & 72.5154 & 6.69095 & 9.79442 & 127.291 & 145.443 & -2020.13 & -1634.23 & 4.42869 & -1.00679 & -8.78072 & -19.2635 \\
|
||||||
|
& -2985.69 & 0.0300519 & 9.28E+09 & 37770.1 & 96.61 & 7.83742 & 14.026 & 184.708 & 146.4 & -590.179 & -2498.13 & 4.26544 & -0.0294331 & -8.87411 & -8.1772 \\
|
||||||
|
& -2353.88 & 0.03025 & 1.14E+10 & 38853.2 & 55.7939 & 7.34237 & 9.23642 & 155.955 & 156.938 & -298.932 & -2034.12 & 4.68428 & 0.020851 & -8.83159 & -21.9278 \\
|
||||||
|
& -1899.75 & 0.03025 & 3.03E+10 & 19359.5 & 64.5918 & 7.09233 & 10.284 & 99.3849 & 153.521 & -801.555 & -2416.07 & 4.08334 & -0.124733 & -8.71859 & -19.27 \\
|
||||||
|
& -1623.43 & 0.03025 & 1.17E+10 & 18194 & 72.4011 & 6.26496 & 9.88647 & 133.847 & 156.223 & -1354.62 & -2445.64 & 4.09028 & -1.742 & -8.69851 & -20.3627 \\
|
||||||
|
& -3222.55 & 0.03025 & 1.86E+10 & 33430.4 & 75.1929 & 6.67338 & 9.50346 & 219.224 & 156.195 & -894.692 & -1572.32 & 4.40705 & -0.587073 & -8.64298 & -8.1772 \\
|
||||||
|
& -3498.88 & 0.0256026 & 1.53E+10 & 23899.7 & 72.4134 & 6.26663 & 9.14707 & 169.249 & 154.033 & -1419.25 & -2628.3 & 4.49581 & -0.0643777 & -8.10856 & -8.1772 \\
|
||||||
|
& -3044.4 & 0.0301924 & 3.41E+10 & 11088.4 & 51.1488 & 6.8859 & 9.41934 & 103.838 & 155.568 & -2127.94 & -2428.83 & 4.49982 & -1.26039 & -8.74994 & -8.1772 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -2529.4106666667 & 0.02890247 & 1.79E+10 & 2.25E+04 & 6.37E+01 & 7.01E+00 & 9.90E+00 & 1.51E+02 & 1.54E+02 & -6.71E+02 & -2.20E+03 & 4.38E+00 & -5.03E-01 & -8.70E+00 & -1.28E+01 \\
|
||||||
|
Med. & -2471.87 & 0.03025 & 18559750000 & 23173.5 & 63.52785 & 6.99424 & 9.314535 & 154.229 & 154.486 & -468.321 & -2369.435 & 4.49111 & -0.4522035 & -8.737675 & -8.1772 \\
|
||||||
|
Std. Dev. & 495.6619476322 & 0.0038284652 & 8448356883.66839 & 8305.8388465674 & 13.8542434661 & 0.5807405608 & 1.4575514207 & 36.4813660807 & 3.4660196682 & 719.7744604556 & 569.5535684889 & 0.1984223336 & 0.5169316097 & 0.17100592 & 5.8206220674 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Iterative Local Search, 20 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -4549.4 & 0.0605 & 2.51E+10 & 83731.6 & 0.651206 & 14.7944 & 21.9681 & 279.695 & 321.001 & -1566.84 & -5338.46 & 9.21621 & -0.142619 & -17.715 & -11.8869 \\
|
||||||
|
& -6050.16 & 0.0605 & 4.55E+10 & 80745.8 & 0.268086 & 16.284 & 19.3337 & 343.725 & 313.969 & -1993.75 & -4148.75 & 9.46446 & -0.548652 & -18.1772 & -11.5925 \\
|
||||||
|
& -5398.23 & 0.0591995 & 5.06E+10 & 82401.3 & 0.032413 & 15.3526 & 23.9768 & 296.889 & 321.956 & -4633.19 & -4848.21 & 9.01646 & 0.218791 & -18.4282 & -11.5925 \\
|
||||||
|
& -5675.22 & 0.0605 & 4.42E+10 & 82591.3 & 0.00544314 & 14.7622 & 22.6234 & 331.276 & 323.228 & -4014.04 & -5436.05 & 9.26537 & 0.626347 & -18.2969 & -12.1455 \\
|
||||||
|
& -3976.37 & 0.0588747 & 3.25E+10 & 82036.8 & 0.0128344 & 13.9223 & 25.6647 & 325.015 & 329.982 & -1246.54 & -4823.87 & 9.12409 & -1.33997 & -17.3054 & -11.5925 \\
|
||||||
|
& -5082.38 & 0.0605 & 2.61E+10 & 87382.5 & 0.0201434 & 14.3422 & 21.3897 & 396.392 & 326.205 & -392.775 & -6280.62 & 9.13477 & -2.26781 & -18.1703 & -11.5925 \\
|
||||||
|
& -5891.86 & 0.0605 & 3.85E+10 & 89279.1 & 0.684922 & 15.2594 & 23.0754 & 329.302 & 326.381 & -337.14 & -3871.16 & 9.02972 & -0.0242582 & -18.4513 & -11.5925 \\
|
||||||
|
& -5003.93 & 0.0605 & 3.00E+10 & 85879 & 0.0151074 & 14.8268 & 19.9157 & 325.527 & 314.19 & -1212.15 & -4189.52 & 9.17302 & 0.0681864 & -18.2963 & -11.5925 \\
|
||||||
|
& -5418.57 & 0.0605 & 4.30E+10 & 82890.7 & 0.00544314 & 15.9129 & 20.2634 & 332.571 & 325.788 & -1548.21 & -5548.41 & 9.39065 & -0.906265 & -18.5396 & -11.5925 \\
|
||||||
|
& -5516.7 & 0.0599162 & 3.51E+10 & 82665.9 & 0.03008 & 16.1692 & 19.9074 & 388.651 & 327.169 & 923.714 & -563.104 & 9.20288 & -1.59829 & -18.1054 & -11.5925 \\
|
||||||
|
& -3937.92 & 0.0547374 & 3.35E+10 & 86354.3 & 0.0153078 & 15.3926 & 19.532 & 312.15 & 322.111 & -1598.94 & -4648.01 & 8.87789 & -1.49817 & -17.8506 & -11.5925 \\
|
||||||
|
& -4588.88 & 0.0605 & 3.51E+10 & 81101.4 & 0.00542657 & 15.7841 & 21.7808 & 357.263 & 330.684 & -1319.67 & -4261.9 & 9.33048 & -1.93719 & -18.2627 & -12.1791 \\
|
||||||
|
& -5082.97 & 0.0559769 & 2.42E+10 & 76218.4 & 0.00544314 & 15.1429 & 22.4065 & 346.209 & 324.783 & 1500.62 & -6032.9 & 9.18852 & -1.88437 & -18.2285 & -11.5925 \\
|
||||||
|
& -6070.01 & 0.0594556 & 3.21E+10 & 83486.2 & 0.0201913 & 14.966 & 19.342 & 393.145 & 324.682 & -404.273 & -5094.61 & 8.57302 & 0.126058 & -16.446 & -11.5925 \\
|
||||||
|
& -5043.31 & 0.0605 & 1.85E+10 & 82935.5 & 0.220955 & 14.0295 & 20.2134 & 316.233 & 325.996 & 2079.2 & -4572.43 & 9.70061 & -1.33897 & -18.2227 & -11.5925 \\
|
||||||
|
& -5161.34 & 0.0605 & 3.87E+10 & 85337.7 & 0.00541984 & 14.9478 & 19.5923 & 274.232 & 327.758 & -1727.17 & -4684.33 & 9.01989 & 0.290542 & -18.4796 & -12.1791 \\
|
||||||
|
& -4589.3 & 0.060484 & 3.08E+10 & 83765.5 & 0.01776 & 14.714 & 20.0449 & 314.196 & 325.896 & -1632.08 & -5179.06 & 8.52944 & -0.524841 & -18.3505 & -18.4163 \\
|
||||||
|
& -4332.6 & 0.0600202 & 6.77E+10 & 81498.7 & 0.0153003 & 16.5447 & 20.0096 & 314.165 & 307.423 & 1567.7 & -4461.73 & 9.43874 & -1.20997 & -18.3268 & -11.5925 \\
|
||||||
|
& -6267.41 & 0.0605 & 4.65E+10 & 81603.3 & 1.88596 & 16.1234 & 20.2482 & 316.52 & 317.494 & -5550.9 & -3693.39 & 9.4962 & 0.510028 & -18.1621 & -11.5925 \\
|
||||||
|
& -4588.98 & 0.0605 & 4.06E+10 & 85076.2 & 0.0225431 & 14.0527 & 20.8396 & 325.67 & 324.854 & -2326.43 & -5346.36 & 9.33666 & -1.32004 & -18.555 & -11.5925 \\
|
||||||
|
& -5477.69 & 0.0604347 & 5.13E+10 & 80799.5 & 0.00535823 & 14.7108 & 20.8015 & 261.241 & 319.17 & 485.619 & -4782.73 & 9.17792 & -1.22079 & -17.7232 & -18.1189 \\
|
||||||
|
& -6109.42 & 0.0472961 & 2.54E+10 & 83373.9 & 0.177322 & 15.6127 & 22.9987 & 286.368 & 321.812 & -2998.41 & -4456.01 & 8.6997 & -2.29998 & -18.4625 & -11.5925 \\
|
||||||
|
& -4885.09 & 0.0570039 & 4.39E+10 & 80163.4 & 0.255374 & 15.6927 & 23.8355 & 324.354 & 325.002 & -1578.78 & -4075.51 & 9.10547 & -0.131814 & -17.7901 & -11.5925 \\
|
||||||
|
& -4035.51 & 0.0600693 & 4.32E+10 & 81415.3 & 0.0152417 & 14.9575 & 19.5572 & 296.019 & 321.434 & 824.034 & -4011.91 & 9.31902 & -1.51993 & -17.8755 & -11.5924 \\
|
||||||
|
& -3917.22 & 0.0586818 & 4.16E+10 & 87878.4 & 0.0128369 & 14.4221 & 19.8007 & 375.301 & 300.768 & 1043.95 & -5037.25 & 9.57629 & -1.73324 & -18.2571 & -11.5925 \\
|
||||||
|
& -5754.04 & 0.0575467 & 2.76E+10 & 83622.3 & 0.272711 & 17.8043 & 20.3977 & 370.717 & 317.424 & -1133.68 & -4973.3 & 9.0252 & -1.10353 & -18.3087 & -11.5925 \\
|
||||||
|
& -4786.87 & 0.0605 & 5.54E+10 & 81190 & 0.0324796 & 14.4401 & 19.8204 & 358.965 & 296.103 & 692.475 & -4121.91 & 8.76355 & -0.860885 & -18.484 & -18.4163 \\
|
||||||
|
& -4510.47 & 0.0597792 & 3.24E+10 & 83530.5 & 4.0042 & 16.0272 & 20.4454 & 225.399 & 326.39 & -478.625 & -5370.33 & 9.32801 & -1.13941 & -18.4418 & -11.5729 \\
|
||||||
|
& -5398.26 & 0.0605 & 2.12E+10 & 85510.6 & 0.43707 & 14.8375 & 19.3774 & 332.986 & 327.903 & -985.672 & -316.499 & 8.94067 & 0.336942 & -17.9256 & -11.5925 \\
|
||||||
|
& -4253.69 & 0.0600716 & 3.11E+10 & 84551 & 0.0128391 & 15.5474 & 21.1043 & 361.928 & 322.473 & -2662.15 & -5159.38 & 7.88002 & -0.784451 & -18.5094 & -11.5924 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -5045.1266666667 & 0.05921826 & 3.70E+10 & 8.33E+04 & 3.06E-01 & 1.52E+01 & 2.10E+01 & 3.27E+02 & 3.21E+02 & -1.07E+03 & -4.51E+03 & 9.11E+00 & -8.39E-01 & -1.81E+01 & -1.23E+01 \\
|
||||||
|
Med. & -5062.845 & 0.06045935 & 35130150000 & 83154.7 & 0.02016735 & 15.05445 & 20.33055 & 325.5985 & 323.955 & -1229.345 & -4733.53 & 9.17547 & -1.0048975 & -18.2599 & -11.5925 \\
|
||||||
|
Std. Dev. & 698.8094252189 & 0.0026900942 & 10975111347.1528 & 2619.8872400694 & 0.791604351 & 0.8539153929 & 1.6317417953 & 39.4394076324 & 8.0216805796 & 1808.4389363295 & 1268.809305736 & 0.3628603461 & 0.8562948838 & 0.4364346365 & 2.0376857083 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Iterative Local Search, 30 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -9410.25 & 0.0833082 & 2.47E+10 & 320497 & & 21.2591 & 29.99 & 368.495 & 473.165 & -6278.62 & -8324.6 & 13.026 & & -28.105 & -19.687 \\
|
||||||
|
& -8976.5 & 0.0769728 & 2.89E+10 & 255960 & & 21.382 & 29.5846 & 390.77 & 473.46 & -6837.82 & -9171.82 & 13.5603 & & -28.2307 & -19.8561 \\
|
||||||
|
& -9489.96 & 0.0869344 & 3.07E+10 & 349495 & & 20.4102 & 29.7188 & 389.025 & 476.709 & -6935.81 & -9139.13 & 13.5408 & & -28.2876 & -19.687 \\
|
||||||
|
& -9074.77 & 0.0754036 & 2.83E+10 & 337920 & & 21.3585 & 29.459 & 330.204 & 459.783 & -7391.32 & -8408.34 & 12.9661 & & -28.411 & -19.687 \\
|
||||||
|
& -9568.94 & 0.0569313 & 3.10E+10 & 338600 & & 21.4582 & 29.6347 & 379.984 & 477.567 & -6970.99 & -9684.67 & 13.1975 & & -28.3428 & -19.8561 \\
|
||||||
|
& -9351.64 & -0.0490078 & 2.68E+10 & 345600 & & 20.7617 & 29.9363 & 7.87853 & 475.43 & -6241.29 & -9773.48 & 13.359 & & -28.1681 & -19.8561 \\
|
||||||
|
& -124.883 & 0.0683202 & 3.31E+10 & 277860 & & 20.4901 & 29.4315 & 374.832 & 472.378 & -7256.36 & -9653.19 & 13.1889 & & -28.1063 & -19.687 \\
|
||||||
|
& -9924.25 & 0.0791061 & 3.19E+10 & 372780 & & 20.9216 & 29.7907 & 373.748 & 455.648 & -6665.63 & 283.59 & 12.8196 & & -28.1588 & -19.8561 \\
|
||||||
|
& -9311.68 & 0.0746452 & 3.24E+10 & 334380 & & 21.3375 & 29.6274 & 372.786 & 472.562 & -6572.91 & -8859.86 & 13.3459 & & -28.316 & -19.8561 \\
|
||||||
|
& -9035.97 & 0.0662556 & 3.18E+10 & 349440 & & 20.4238 & 30.1293 & 352.798 & 478.836 & -7007.39 & -9049.74 & 13.4248 & & -28.2245 & -19.8561 \\
|
||||||
|
& -9864.99 & -0.0636759 & 3.03E+10 & 312060 & & 21.0197 & 29.4352 & 376.266 & 466.391 & -8396.7 & -9143.81 & 13.5041 & & -28.2922 & -19.8561 \\
|
||||||
|
& -8996.29 & 0.0789434 & 2.87E+10 & -1.66599 & & 20.6647 & 29.6205 & 358.251 & 479.544 & -8751 & -10268.6 & 13.2663 & & -28.1594 & -19.8561 \\
|
||||||
|
& -9391.2 & 0.0749528 & 2.68E+10 & 338160 & & 20.4382 & 29.777 & 345.008 & 458.006 & -5866.24 & -8878.84 & 13.203 & & -28.3379 & -19.8561 \\
|
||||||
|
& -9450.26 & 0.0845282 & 2.94E+10 & 368202 & & 20.5752 & 29.6886 & 368.195 & 474.65 & -8633.36 & 464.389 & 13.6934 & & -3.43673 & -19.8561 \\
|
||||||
|
& -9746.52 & 0.0751487 & 2.75E+10 & 357000 & & 21.1032 & 29.7037 & 351.542 & 467.12 & -7698.6 & -8541.65 & 13.0522 & & -28.3234 & -19.8561 \\
|
||||||
|
& -9865.04 & 0.0805431 & -1.00E+01 & 330432 & & 20.6857 & 29.5885 & -7.69393 & 473.355 & -6756.61 & -9540.85 & 13.5152 & & -28.2612 & -19.687 \\
|
||||||
|
& -9272.8 & 0.0795918 & 3.80E+10 & 286598 & & 20.6426 & 19.8658 & 371.765 & 486.552 & -6547.46 & -9854.97 & 13.5049 & & -28.1688 & -19.8561 \\
|
||||||
|
& -10062.5 & 0.0768222 & 3.74E+10 & 350160 & & 20.0135 & 29.7339 & 390.477 & 475.739 & -6719.72 & -9440.52 & 13.4156 & & -28.2127 & -19.8561 \\
|
||||||
|
& -9134.53 & 0.0711327 & 3.08E+10 & 360420 & & 20.3865 & 29.9421 & 378.888 & 462.393 & -8281.31 & -9663.62 & 12.4277 & & -28.2074 & -19.8561 \\
|
||||||
|
& -9390.69 & -0.0309291 & 3.97E+10 & 295997 & & 20.7647 & 30.6015 & 359.88 & 484.513 & -5065.59 & -9134.86 & 13.6274 & & -28.3904 & -19.8561 \\
|
||||||
|
& -9134.47 & 0.0804281 & 4.70E+10 & 334360 & & 20.6145 & 27.8698 & 381.484 & 474.708 & -4226.77 & -8811.44 & 13.4809 & & -28.0928 & -19.8561 \\
|
||||||
|
& -9568.88 & 0.0785976 & 3.02E+10 & 296580 & & 20.5503 & 29.785 & 398.854 & 455.846 & -7596.93 & -8997.45 & 13.5753 & & -28.1787 & -19.8561 \\
|
||||||
|
& -9272.85 & 0.0817261 & 2.52E+10 & 306300 & & 20.8571 & 29.6731 & 380.627 & 470.576 & -6667.08 & -9397.49 & 13.1267 & & -28.1215 & -19.6839 \\
|
||||||
|
& -9212.05 & 0.0702678 & 2.59E+10 & 335460 & & 20.1083 & 30.1306 & 381.129 & 463.962 & -8674.4 & -9315.6 & 12.7335 & & -28.2767 & -19.8561 \\
|
||||||
|
& -9726.64 & 0.0741334 & 3.79E+10 & 363524 & & 21.4061 & 29.8537 & 332.172 & 458.809 & -6933.42 & -9365.76 & 13.402 & & -28.2994 & -19.8561 \\
|
||||||
|
& -10457.2 & 0.081483 & 3.46E+10 & 313260 & & 21.4092 & 29.7197 & 333.342 & 476.861 & -8640.37 & -9338.14 & 13.5832 & & -28.3677 & -19.8561 \\
|
||||||
|
& -9173.48 & 0.0870585 & 3.42E+10 & 312000 & & 20.6161 & 29.6849 & 336 & 471.518 & -6486.48 & -9721.42 & 13.651 & & -28.1929 & -19.8561 \\
|
||||||
|
& -9133.36 & 0.0840459 & 3.16E+10 & 381645 & & 20.5456 & 30.4368 & 362.339 & 459.739 & -6593.12 & -9396.22 & 13.5676 & & -28.2393 & -19.8561 \\
|
||||||
|
& -9391.21 & 0.0797617 & 4.23E+10 & 346620 & & 20.5208 & 29.7877 & 368.834 & 477.598 & -6145.95 & -9651.49 & 13.1248 & & -28.3445 & -19.8561 \\
|
||||||
|
& -9568.74 & 0.074442 & 2.17E+10 & 311040 & & 18.9379 & 29.5349 & 355.219 & 475.06 & -8780 & -9200.77 & 13.5091 & & -28.3269 & -19.8561 \\
|
||||||
|
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -9136.0847666667 & 0.06459572 & 3.06E+10 & 3.19E+05 & 2.07E+01 & 2.94E+01 & 3.42E+02 & 4.71E+02 & 4.71E+02 & -7.05E+03 & -8.63E+03 & 1.33E+01 & -2.74E+01 & -2.74E+01 & -1.98E+01 \\
|
||||||
|
Med. & -9390.945 & 0.0768975 & 30729500000 & 334920 & 20.65365 & 29.71125 & 368.6645 & 473.26 & 473.26 & -6885.62 & -9258.185 & 13.4088 & -28.235 & -28.235 & -19.8561 \\
|
||||||
|
Std. Dev. & 1736.4971456685 & 0.0388817803 & 7961125157.97467 & 67087.9428677671 & 0.5231419506 & 1.8518270929 & 94.7625256457 & 8.3344717638 & 8.3344717638 & 1094.2052693977 & 2486.4963265932 & 0.3016843863 & 4.5304816776 & 4.5304816776 & 0.0690085236 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
\small{Random Search Running Times in Seconds}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\begin{tabular}{l || l | l | l}
|
||||||
|
|
||||||
|
\textbf{Dimensions} & 10 & 20 & 30 \\
|
||||||
|
\hline \\
|
||||||
|
Function 1 & 0.0028796196 & 0.0027256012 & 0.004216671 \\
|
||||||
|
Function 2 & 0.0037307739 & 0.0027096272 & 0.0042328835 \\
|
||||||
|
Function 3 & 0.0027823448 & 0.0035073757 & 0.003777504 \\
|
||||||
|
Function 4 & 0.0041363239 & 0.0041460991 & 0.0026381016 \\
|
||||||
|
Function 5 & 0.0037288666 & 0.003030777 & 0.0026414394 \\
|
||||||
|
Function 6 & 0.0037727356 & 0.0028493404 & 0.0026321411 \\
|
||||||
|
Function 7 & 0.0035896301 & 0.0027751923 & 0.0037312508 \\
|
||||||
|
Function 8 & 0.0035607815 & 0.0028815269 & 0.0037713051 \\
|
||||||
|
Function 9 & 0.0044622421 & 0.0026602745 & 0.0027508736 \\
|
||||||
|
Function 10 & 0.0040593147 & 0.0027322769 & 0.0027010441 \\
|
||||||
|
Function 11 & 0.0030579567 & 0.0026328564 & 0.0026137829 \\
|
||||||
|
Function 12 & 0.0029666424 & 0.0026972294 & 0.0026974678 \\
|
||||||
|
Function 13 & 0.0027945042 & 0.003254652 & 0.0025961399 \\
|
||||||
|
Function 14 & 0.0028162003 & 0.004308939 & 0.0026042461 \\
|
||||||
|
Function 15 & 0.0027165413 & 0.0028510094 & 0.0024940968 \\
|
||||||
|
\hline
|
||||||
|
\end{tabular}
|
||||||
|
}\\[0.5cm]
|
||||||
|
|
||||||
|
|
||||||
|
\small{Local Search Running Times in Seconds}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\begin{tabular}{l || l | l | l}
|
||||||
|
|
||||||
|
\textbf{Dimensions} & 10 & 20 & 30 \\
|
||||||
|
\hline \\
|
||||||
|
Function 1 & 0.2714903355 & 0.3662781715 & 0.8037896156 \\
|
||||||
|
Function 2 & 0.0122189522 & 0.1061990261 & 0.0311796665 \\
|
||||||
|
Function 3 & 0.0036041737 & 0.0034754276 & 0.0039658546 \\
|
||||||
|
Function 4 & 0.0045986176 & 0.0037958622 & 0.0057651997 \\
|
||||||
|
Function 5 & 3.5046873093 & 107.3777658939 & 237.3515529633 \\
|
||||||
|
Function 6 & 0.0053646564 & 0.0071499348 & 0.0078163147 \\
|
||||||
|
Function 7 & 0.1593027115 & 0.1934890747 & 36.0658888817 \\
|
||||||
|
Function 8 & 0.0124971867 & 0.1606588364 & 0.0684037209 \\
|
||||||
|
Function 9 & 0.0049269199 & 0.0049231052 & 0.0069723129 \\
|
||||||
|
Function 10 & 0.0056340694 & 0.0120668411 & 0.0046567917 \\
|
||||||
|
Function 11 & 0.0049116611 & 0.1781361103 & 3.4641461372 \\
|
||||||
|
Function 12 & 0.0035014153 & 0.0031409264 & 0.0071520805 \\
|
||||||
|
Function 13 & 0.0022878647 & 0.0031747818 & 0.008487463 \\
|
||||||
|
Function 14 & 0.0166265965 & 0.1419093609 & 0.2867805958 \\
|
||||||
|
Function 15 & 0.0709223747 & 0.0597565174 & 0.1033499241 \\
|
||||||
|
\hline \\
|
||||||
|
\end{tabular}
|
||||||
|
} \\[0.5cm]
|
||||||
|
|
||||||
|
|
||||||
|
\small{Iterative Local Search Running Times in Seconds}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\begin{tabular}{l || l | l | l}
|
||||||
|
|
||||||
|
\textbf{Dimensions} & 10 & 20 & 30 \\
|
||||||
|
\hline \\
|
||||||
|
Function 1 & 5.355587244 & 21.5247523785 & 47.5882720947 \\
|
||||||
|
Function 2 & 0.4999251366 & 1.151144743 & 2.4649145603 \\
|
||||||
|
Function 3 & 0.0042607784 & 0.0112228394 & 0.0144929886 \\
|
||||||
|
Function 4 & 0.0058951378 & 0.0114533901 & 0.0161828995 \\
|
||||||
|
Function 5 & 150.1059572697 & 2928.3961615563 & N/A \\
|
||||||
|
Function 6 & 0.0101454258 & 0.0255510807 & 0.0222308636 \\
|
||||||
|
Function 7 & 32.4964332581 & 41.5021996498 & 168.8056237698 \\
|
||||||
|
Function 8 & 0.3526818752 & 1.6770370007 & 3.8826031685 \\
|
||||||
|
Function 9 & 0.0110986233 & 0.0125215054 & 0.0229070187 \\
|
||||||
|
Function 10 & 0.0899729729 & 0.2644715309 & 0.7342042923 \\
|
||||||
|
Function 11 & 30.093629837 & 165.0208876133 & 384.6772966385 \\
|
||||||
|
Function 12 & 297.458874464 & 25.3617525101 & 21.2174470425 \\
|
||||||
|
Function 13 & 0.0197796822 & 0.0436241627 & 0.0463643074 \\
|
||||||
|
Function 14 & 1.5726833344 & 7.7616007328 & 9.7854065895 \\
|
||||||
|
Function 15 & 6.6486163139 & 23.9164574146 & 32.7224471569 \\
|
||||||
|
\hline \\
|
||||||
|
\end{tabular}
|
||||||
|
} \\[0.5cm]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\section{Previous Results}
|
||||||
|
|
||||||
|
\hskip+1.0cm\scalebox{0.7}{
|
||||||
|
\begin{tabular}{| l | l | l | l | l | l|}
|
||||||
|
\hline
|
||||||
|
Function & Dimensionality & Mean & Median & Deviation & Avg. Time \\ \hline
|
||||||
|
Schwefel's & 10 & 40.03647 & 0.0623 & 547.27404 & 3.1285 \\ \hline
|
||||||
|
& 20 & -273.92765 & 16.52 & 883.3137 & 2.942 \\ \hline
|
||||||
|
& 30 & -63.79153 & 255.3487 & 925.71758 & 3.132 \\ \hline
|
||||||
|
|
||||||
|
De Jong's & 10 & 3775.96667 & 3772 & 3116.12957 & 0.667 \\ \hline
|
||||||
|
& 20 & 3748.5 & 4105 & 2885.095608 & 0.132 \\ \hline
|
||||||
|
& 30 & 3429.8334 & 3429 & 2608.0073 & 0.0933 \\ \hline
|
||||||
|
|
||||||
|
Rosenbrock & 10 & 2093118197.7 & 951513386 & 2466626292.4 & 0.90 \\ \hline
|
||||||
|
& 20 & 2109933654.57 & 997811808.5 &2696811330.46 & 1.679 \\ \hline
|
||||||
|
& 30 & 1784558137.97 & 543961363 & 2372214627.61 & 2.98 \\ \hline
|
||||||
|
|
||||||
|
Rastrigin & 10 & 318.2 & 262.5 & 289.575003684 & 1.04 \\ \hline
|
||||||
|
& 20 & 202.2666667 & 111 & 232.951516834 & 1.98 \\ \hline
|
||||||
|
& 30 & 309.9 & 246 & 264.111829763 & 2.788 \\ \hline
|
||||||
|
|
||||||
|
Griegwangk & 10 & 27.326983 & 22.8316 & 19.94304775 & 1.32 \\ \hline
|
||||||
|
& 20 & 22.247975 & 18.4916 & 17.41881647 & 4.98 \\ \hline
|
||||||
|
& 30 & 25.04950833 & 24.1043 & 19.11108789 & 5.67 \\ \hline
|
||||||
|
|
||||||
|
Sine Envelope Sine Wave & 10 & -4.70534 &-4.6083 & 0.2380683 & 1.112 \\ \hline
|
||||||
|
& 20 & -9.805175 & -9.6732 & 0.3503130 & 2.223 \\ \hline
|
||||||
|
& 30 & -15.1282 & -15.0166 & 0.410964 & 4.121 \\ \hline
|
||||||
|
|
||||||
|
Stretched V Sine Wave & 10 & -5.85114 & -5.8511 & 4.5168102e-15 & 3.55 \\ \hline
|
||||||
|
& 20 & -12.3524 & -12.35 & 5.420172e-15 & 6.88 \\ \hline
|
||||||
|
& 30 & -18.8536 & -18.8537 & 3.61344822e-15 & 9.87 \\ \hline
|
||||||
|
|
||||||
|
Ackley's One & 10 & 187.917 & 184.0721 & 33.190007 & 3.1298 \\ \hline
|
||||||
|
& 20 & 389.45238 & 382.962 & 40.1378 & 4.6731 \\ \hline
|
||||||
|
& 30 & 593.39786 & 599.1116 & 64.28003 & 8.7728 \\ \hline
|
||||||
|
|
||||||
|
Ackley's Two & 10 & 217.1978 & 217.922 & 2.26465 & 3.055 \\ \hline
|
||||||
|
& 20 & 456.7024 & 459.70244 & 6.447889 & 7.001 \\ \hline
|
||||||
|
& 30 & 698.9139 & 700.1936 & 4.42437 & 8.4356 \\ \hline
|
||||||
|
|
||||||
|
Egg Holder & 10 & -374.3114 & -529.396 & 877.367109 & 1.998 \\ \hline
|
||||||
|
& 20 & -197.32204 & -339.2197 & 1196.0543 & 4.7621 \\ \hline
|
||||||
|
& 30 & -533.43484 & -507.8253 & 1366.0572 & 6.9981 \\ \hline
|
||||||
|
|
||||||
|
Rana & 10 & 126.682 & 92.4435 & 762.991158 & 5.1433 \\ \hline
|
||||||
|
& 20 & 44.632258 & 144.748 & 897.22947 & 9.4239 \\ \hline
|
||||||
|
& 30 & 147.21517 & 280.21517 & 1161.5825 & 14.221 \\ \hline
|
||||||
|
|
||||||
|
Pathological & 10 & 4.7605744 & 4.5666& 0.320334 & 3.1561 \\ \hline
|
||||||
|
& 20 & 10.02892 & 9.9324 & 0.485784 & 3.9714 \\ \hline
|
||||||
|
& 30 & 15.28425 & 15.1622 & 0.66075 & 4.9912 \\ \hline
|
||||||
|
|
||||||
|
Michalewicz & 10 & 0.904288 & 0.942 & 0.544008 & 1.3241 \\ \hline
|
||||||
|
& 20 & 1.73464 & 1.5955 & 0.733736 & 3.1149 \\ \hline
|
||||||
|
& 30 & 2.108609 & 1.9897 & 0.974908 & 4.8229 \\ \hline
|
||||||
|
|
||||||
|
Masters Cosine Wave & 10 &0.6488827 & 0.5623 & 2.0702 & 2.3341 \\ \hline
|
||||||
|
& 20 & -1.492407& -1.0483& 2.270724 & 3.4256 \\ \hline
|
||||||
|
& 30 & 0.885458 & 0.9459 & 3.441345 & 5.3243 \\ \hline
|
||||||
|
|
||||||
|
Shekel's Foxhole & 10 & -0.2105669 & -0.2023 & 0.038925 & 4.5623 \\ \hline
|
||||||
|
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
|
||||||
|
\end{document}
|
||||||
|
|
||||||
@ -0,0 +1,13 @@
|
|||||||
|
#pragma once
|
||||||
|
|
||||||
|
class search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
function func;
|
||||||
|
|
||||||
|
search_function(function f) : func(f) {
|
||||||
|
};
|
||||||
|
|
||||||
|
virtual double search(int permutations, int dimensionality) = 0;
|
||||||
|
|
||||||
|
};
|
||||||
@ -0,0 +1,181 @@
|
|||||||
|
// http://www.math.keio.ac.jp/~matumoto/ver980409.html
|
||||||
|
|
||||||
|
// This is the ``Mersenne Twister'' random number generator MT19937, which
|
||||||
|
// generates pseudorandom integers uniformly distributed in 0..(2^32 - 1)
|
||||||
|
// starting from any odd seed in 0..(2^32 - 1). This version is a recode
|
||||||
|
// by Shawn Cokus (Cokus@math.washington.edu) on March 8, 1998 of a version by
|
||||||
|
// Takuji Nishimura (who had suggestions from Topher Cooper and Marc Rieffel in
|
||||||
|
// July-August 1997).
|
||||||
|
//
|
||||||
|
// Effectiveness of the recoding (on Goedel2.math.washington.edu, a DEC Alpha
|
||||||
|
// running OSF/1) using GCC -O3 as a compiler: before recoding: 51.6 sec. to
|
||||||
|
// generate 300 million random numbers; after recoding: 24.0 sec. for the same
|
||||||
|
// (i.e., 46.5% of original time), so speed is now about 12.5 million random
|
||||||
|
// number generations per second on this machine.
|
||||||
|
//
|
||||||
|
// According to the URL <http://www.math.keio.ac.jp/~matumoto/emt.html>
|
||||||
|
// (and paraphrasing a bit in places), the Mersenne Twister is ``designed
|
||||||
|
// with consideration of the flaws of various existing generators,'' has
|
||||||
|
// a period of 2^19937 - 1, gives a sequence that is 623-dimensionally
|
||||||
|
// equidistributed, and ``has passed many stringent tests, including the
|
||||||
|
// die-hard test of G. Marsaglia and the load test of P. Hellekalek and
|
||||||
|
// S. Wegenkittl.'' It is efficient in memory usage (typically using 2506
|
||||||
|
// to 5012 bytes of static data, depending on data type sizes, and the code
|
||||||
|
// is quite short as well). It generates random numbers in batches of 624
|
||||||
|
// at a time, so the caching and pipelining of modern systems is exploited.
|
||||||
|
// It is also divide- and mod-free.
|
||||||
|
//
|
||||||
|
// This library is free software; you can redistribute it and/or modify it
|
||||||
|
// under the terms of the GNU Library General Public License as published by
|
||||||
|
// the Free Software Foundation (either version 2 of the License or, at your
|
||||||
|
// option, any later version). This library is distributed in the hope that
|
||||||
|
// it will be useful, but WITHOUT ANY WARRANTY, without even the implied
|
||||||
|
// warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See
|
||||||
|
// the GNU Library General Public License for more details. You should have
|
||||||
|
// received a copy of the GNU Library General Public License along with this
|
||||||
|
// library; if not, write to the Free Software Foundation, Inc., 59 Temple
|
||||||
|
// Place, Suite 330, Boston, MA 02111-1307, USA.
|
||||||
|
//
|
||||||
|
// The code as Shawn received it included the following notice:
|
||||||
|
//
|
||||||
|
// Copyright (C) 1997 Makoto Matsumoto and Takuji Nishimura. When
|
||||||
|
// you use this, send an e-mail to <matumoto@math.keio.ac.jp> with
|
||||||
|
// an appropriate reference to your work.
|
||||||
|
//
|
||||||
|
// It would be nice to CC: <Cokus@math.washington.edu> when you write.
|
||||||
|
//
|
||||||
|
|
||||||
|
#include <stdio.h>
|
||||||
|
#include <stdlib.h>
|
||||||
|
|
||||||
|
//
|
||||||
|
// uint32 must be an unsigned integer type capable of holding at least 32
|
||||||
|
// bits; exactly 32 should be fastest, but 64 is better on an Alpha with
|
||||||
|
// GCC at -O3 optimization so try your options and see what's best for you
|
||||||
|
//
|
||||||
|
|
||||||
|
typedef unsigned long uint32;
|
||||||
|
|
||||||
|
#define N (624) // length of state vector
|
||||||
|
#define M (397) // a period parameter
|
||||||
|
#define K (0x9908B0DFU) // a magic constant
|
||||||
|
#define hiBit(u) ((u) & 0x80000000U) // mask all but highest bit of u
|
||||||
|
#define loBit(u) ((u) & 0x00000001U) // mask all but lowest bit of u
|
||||||
|
#define loBits(u) ((u) & 0x7FFFFFFFU) // mask the highest bit of u
|
||||||
|
#define mixBits(u, v) (hiBit(u)|loBits(v)) // move hi bit of u to hi bit of v
|
||||||
|
|
||||||
|
static uint32 state[N+1]; // state vector + 1 extra to not violate ANSI C
|
||||||
|
static uint32 *next; // next random value is computed from here
|
||||||
|
static int left = -1; // can *next++ this many times before reloading
|
||||||
|
|
||||||
|
void seedMT(uint32 seed)
|
||||||
|
{
|
||||||
|
//
|
||||||
|
// We initialize state[0..(N-1)] via the generator
|
||||||
|
//
|
||||||
|
// x_new = (69069 * x_old) mod 2^32
|
||||||
|
//
|
||||||
|
// from Line 15 of Table 1, p. 106, Sec. 3.3.4 of Knuth's
|
||||||
|
// _The Art of Computer Programming_, Volume 2, 3rd ed.
|
||||||
|
//
|
||||||
|
// Notes (SJC): I do not know what the initial state requirements
|
||||||
|
// of the Mersenne Twister are, but it seems this seeding generator
|
||||||
|
// could be better. It achieves the maximum period for its modulus
|
||||||
|
// (2^30) iff x_initial is odd (p. 20-21, Sec. 3.2.1.2, Knuth); if
|
||||||
|
// x_initial can be even, you have sequences like 0, 0, 0, ...;
|
||||||
|
// 2^31, 2^31, 2^31, ...; 2^30, 2^30, 2^30, ...; 2^29, 2^29 + 2^31,
|
||||||
|
// 2^29, 2^29 + 2^31, ..., etc. so I force seed to be odd below.
|
||||||
|
//
|
||||||
|
// Even if x_initial is odd, if x_initial is 1 mod 4 then
|
||||||
|
//
|
||||||
|
// the lowest bit of x is always 1,
|
||||||
|
// the next-to-lowest bit of x is always 0,
|
||||||
|
// the 2nd-from-lowest bit of x alternates ... 0 1 0 1 0 1 0 1 ... ,
|
||||||
|
// the 3rd-from-lowest bit of x 4-cycles ... 0 1 1 0 0 1 1 0 ... ,
|
||||||
|
// the 4th-from-lowest bit of x has the 8-cycle ... 0 0 0 1 1 1 1 0 ... ,
|
||||||
|
// ...
|
||||||
|
//
|
||||||
|
// and if x_initial is 3 mod 4 then
|
||||||
|
//
|
||||||
|
// the lowest bit of x is always 1,
|
||||||
|
// the next-to-lowest bit of x is always 1,
|
||||||
|
// the 2nd-from-lowest bit of x alternates ... 0 1 0 1 0 1 0 1 ... ,
|
||||||
|
// the 3rd-from-lowest bit of x 4-cycles ... 0 0 1 1 0 0 1 1 ... ,
|
||||||
|
// the 4th-from-lowest bit of x has the 8-cycle ... 0 0 1 1 1 1 0 0 ... ,
|
||||||
|
// ...
|
||||||
|
//
|
||||||
|
// The generator's potency (min. s>=0 with (69069-1)^s = 0 mod 2^32) is
|
||||||
|
// 16, which seems to be alright by p. 25, Sec. 3.2.1.3 of Knuth. It
|
||||||
|
// also does well in the dimension 2..5 spectral tests, but it could be
|
||||||
|
// better in dimension 6 (Line 15, Table 1, p. 106, Sec. 3.3.4, Knuth).
|
||||||
|
//
|
||||||
|
// Note that the random number user does not see the values generated
|
||||||
|
// here directly since reloadMT() will always munge them first, so maybe
|
||||||
|
// none of all of this matters. In fact, the seed values made here could
|
||||||
|
// even be extra-special desirable if the Mersenne Twister theory says
|
||||||
|
// so-- that's why the only change I made is to restrict to odd seeds.
|
||||||
|
//
|
||||||
|
|
||||||
|
register uint32 x = (seed | 1U) & 0xFFFFFFFFU, *s = state;
|
||||||
|
register int j;
|
||||||
|
|
||||||
|
for(left=0, *s++=x, j=N; --j;
|
||||||
|
*s++ = (x*=69069U) & 0xFFFFFFFFU);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
uint32 reloadMT(void)
|
||||||
|
{
|
||||||
|
register uint32 *p0=state, *p2=state+2, *pM=state+M, s0, s1;
|
||||||
|
register int j;
|
||||||
|
|
||||||
|
if(left < -1)
|
||||||
|
seedMT(4357U);
|
||||||
|
|
||||||
|
left=N-1, next=state+1;
|
||||||
|
|
||||||
|
for(s0=state[0], s1=state[1], j=N-M+1; --j; s0=s1, s1=*p2++)
|
||||||
|
*p0++ = *pM++ ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
|
||||||
|
|
||||||
|
for(pM=state, j=M; --j; s0=s1, s1=*p2++)
|
||||||
|
*p0++ = *pM++ ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
|
||||||
|
|
||||||
|
s1=state[0], *p0 = *pM ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
|
||||||
|
s1 ^= (s1 >> 11);
|
||||||
|
s1 ^= (s1 << 7) & 0x9D2C5680U;
|
||||||
|
s1 ^= (s1 << 15) & 0xEFC60000U;
|
||||||
|
return(s1 ^ (s1 >> 18));
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
uint32 randomMT(void)
|
||||||
|
{
|
||||||
|
uint32 y;
|
||||||
|
|
||||||
|
if(--left < 0)
|
||||||
|
return(reloadMT());
|
||||||
|
|
||||||
|
y = *next++;
|
||||||
|
y ^= (y >> 11);
|
||||||
|
y ^= (y << 7) & 0x9D2C5680U;
|
||||||
|
y ^= (y << 15) & 0xEFC60000U;
|
||||||
|
return(y ^ (y >> 18));
|
||||||
|
}
|
||||||
|
|
||||||
|
#ifdef NOCOMPILE
|
||||||
|
int main(void)
|
||||||
|
{
|
||||||
|
int j;
|
||||||
|
|
||||||
|
// you can seed with any uint32, but the best are odds in 0..(2^32 - 1)
|
||||||
|
|
||||||
|
seedMT(4357U);
|
||||||
|
|
||||||
|
// print the first 2,002 random numbers seven to a line as an example
|
||||||
|
|
||||||
|
for(j=0; j<2002; j++)
|
||||||
|
printf(" %10lu%s", (unsigned long) randomMT(), (j%7)==6 ? "\n" : "");
|
||||||
|
|
||||||
|
return(EXIT_SUCCESS);
|
||||||
|
}
|
||||||
|
#endif
|
||||||
@ -0,0 +1,8 @@
|
|||||||
|
|
||||||
|
struct timer{
|
||||||
|
std::chrono::high_resolution_clock::time_point t1;
|
||||||
|
std::chrono::high_resolution_clock::time_point t2;
|
||||||
|
void start(){t1 = std::chrono::high_resolution_clock::now();}
|
||||||
|
void end(){t2 = std::chrono::high_resolution_clock::now();}
|
||||||
|
double duration(){ return std::chrono::duration_cast<std::chrono::microseconds>( t2 - t1 ).count();}
|
||||||
|
};
|
||||||
@ -0,0 +1,15 @@
|
|||||||
|
cmake_minimum_required(VERSION 3.5.1)
|
||||||
|
|
||||||
|
project(Lab3)
|
||||||
|
|
||||||
|
set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR})
|
||||||
|
|
||||||
|
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
|
||||||
|
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
|
||||||
|
|
||||||
|
file(GLOB SOURCES "*.cpp")
|
||||||
|
file(GLOB HEADERS "*.h" "*.hpp")
|
||||||
|
|
||||||
|
set(SOURCE_FILES ${HEADERS} ${SOURCES} )
|
||||||
|
|
||||||
|
add_executable(Lab3 ${SOURCE_FILES})
|
||||||
Binary file not shown.
@ -0,0 +1,169 @@
|
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|
#pragma once
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|
#include "search_function.h"
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|
|
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|
class differential_evolution : public search_function {
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|
public:
|
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|
differential_evolution(function f) : search_function(f) {};
|
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|
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|
double search(int permutations, int dimensionality) {
|
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|
|
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|
// Set up random start
|
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|
std::vector<std::vector<double>> population = generate_population(50, dimensionality);
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|
|
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|
for (int g = 0; g < maximum_generation_number; g++) {
|
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|
|
||||||
|
for (int i = 0; i < population.size(); i++) {
|
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|
|
||||||
|
// Create the U, temp vector to hold values
|
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|
std::vector<double> u(dimensionality, 0);
|
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|
|
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|
// select a random dimension
|
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|
int j_rand = rand() % dimensionality;
|
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|
|
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|
for (int j = j_rand; j < dimensionality; j++){
|
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|
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|
// Accept changes if rng returns < 0.9
|
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|
if (randomMT() * 1.0 / RAND_MAX < 0.9)
|
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|
u.at(j) = check_bounds(compute_with_strategy(&population, j, i));
|
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|
else
|
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|
u.at(j) = population.at(i).at(j);
|
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|
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|
}
|
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|
||||||
|
// If the new population has a better fitness, replace it
|
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|
if (func.compute(population.at(i)) > func.compute(u))
|
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|
population.at(i) = u;
|
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|
|
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|
}
|
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|
}
|
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|
|
||||||
|
// Generation is done, return the best value from the population
|
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|
std::sort(population.begin(), population.end(), [this](std::vector<double> a, std::vector<double> b){
|
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|
return this->func.compute(a) < this->func.compute(b);
|
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|
});
|
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|
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|
return func.compute(population[0]);
|
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|
};
|
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|
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|
void set_strategy(int strategy){
|
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|
this->strategy = strategy;
|
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|
}
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|
private:
|
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|
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|
// G
|
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|
int maximum_generation_number = 30;
|
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|
// Tuning variable
|
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|
double tuning_variable_f = 0.8;
|
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|
double tuning_variable_lambda = 1.0;
|
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|
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|
int strategy = 0;
|
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|
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|
// Compute using different strategies
|
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|
double compute_with_strategy(std::vector<std::vector<double>> *population, int j, int i){
|
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|
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|
// Setup and find the best solution in the population that was passed in
|
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|
std::vector<double> best_solution;
|
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|
double best_fitness = 999999999999;
|
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|
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|
for (auto p: *population){
|
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|
double val = func.compute(p);
|
||||||
|
|
||||||
|
if (val < best_fitness){
|
||||||
|
best_fitness = val;
|
||||||
|
best_solution = p;
|
||||||
|
}
|
||||||
|
}
|
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|
||||||
|
// Depending on the strategy, determine the new solution at J
|
||||||
|
switch(strategy){
|
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|
case 1: {
|
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|
std::vector<int> r = distinct_indices(2, population[0].size());
|
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|
return best_solution[j] + tuning_variable_f * (population->at(r[0]).at(j) - population->at(r[1]).at(j));
|
||||||
|
}
|
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|
case 2: {
|
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|
std::vector<int> r = distinct_indices(3, population[0].size());
|
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|
return population->at(r[0]).at(j) + tuning_variable_f * (population->at(r[1]).at(j) - population->at(r[2]).at(j));
|
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|
}
|
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|
case 3:{
|
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|
std::vector<int> r = distinct_indices(2, population[0].size());
|
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|
return population->at(i).at(j) + tuning_variable_lambda * (best_solution.at(j) - population->at(i).at(j)) + tuning_variable_f * (population->at(r[0]).at(j) - population->at(r[1]).at(j));
|
||||||
|
}
|
||||||
|
case 4:{
|
||||||
|
std::vector<int> r = distinct_indices(4, population[0].size());
|
||||||
|
return best_solution.at(j) + tuning_variable_f * (population->at(r[0]).at(j) + population->at(r[1]).at(j) - population->at(r[2]).at(j) - population->at(r[3]).at(j));
|
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|
}
|
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|
case 5:{
|
||||||
|
std::vector<int> r = distinct_indices(5, population[0].size());
|
||||||
|
return population->at(r[4]).at(j) + tuning_variable_f * (population->at(r[0]).at(j) + population->at(r[1]).at(j) - population->at(r[2]).at(j) - population->at(r[3]).at(j));
|
||||||
|
}
|
||||||
|
case 6:{
|
||||||
|
std::vector<int> r = distinct_indices(2, population[0].size());
|
||||||
|
return best_solution.at(j) + tuning_variable_f * (population->at(r[0]).at(j) - population->at(r[1]).at(j));
|
||||||
|
}
|
||||||
|
case 7:{
|
||||||
|
std::vector<int> r = distinct_indices(3, population[0].size());
|
||||||
|
return population->at(r[0]).at(j) + tuning_variable_f * (population->at(r[1]).at(j) - population->at(r[2]).at(j));
|
||||||
|
}
|
||||||
|
case 8:{
|
||||||
|
std::vector<int> r = distinct_indices(2, population[0].size());
|
||||||
|
return population->at(i).at(j) + tuning_variable_lambda * (best_solution.at(j) - population->at(i).at(j)) + tuning_variable_f * (population->at(r[0]).at(j) - population->at(r[1]).at(j));
|
||||||
|
}
|
||||||
|
case 9:{
|
||||||
|
std::vector<int> r = distinct_indices(4, population[0].size());
|
||||||
|
return best_solution.at(j) + tuning_variable_f * (population->at(r[0]).at(j) + population->at(r[1]).at(j) - population->at(r[2]).at(j) - population->at(r[3]).at(j));
|
||||||
|
}
|
||||||
|
case 10:{
|
||||||
|
std::vector<int> r = distinct_indices(5, population[0].size());
|
||||||
|
return population->at(r[4]).at(j) + tuning_variable_f * (population->at(r[0]).at(j) + population->at(r[1]).at(j) - population->at(r[2]).at(j) - population->at(r[3]).at(j));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<int> distinct_indices(int count, int max){
|
||||||
|
|
||||||
|
std::vector<int> indices;
|
||||||
|
for (int q = 0; q < count; q++) {
|
||||||
|
int val = 1 + (rand() % (max - 1));
|
||||||
|
while (std::count(indices.begin(), indices.end(), val) != 0)
|
||||||
|
val = randomMT() % max;
|
||||||
|
indices.push_back(val);
|
||||||
|
}
|
||||||
|
return indices;
|
||||||
|
}
|
||||||
|
|
||||||
|
};
|
||||||
|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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@ -0,0 +1,319 @@
|
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|
|
||||||
|
std::vector<double> c = {0.806,0.517,0.1,0.908,0.965,0.669,0.524,0.902,0.351,0.876,0.462,
|
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|
0.491,0.463,0.741,0.352,0.869,0.813,0.811,0.0828,0.964,0.789,0.360,0.369,
|
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|
0.992,0.332,0.817,0.632,0.883,0.608,0.326};
|
||||||
|
|
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|
double a[][10] =
|
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|
{
|
||||||
|
{9.681,0.667,4.783,9.095,3.517,9.325,6.544,0.211,5.122,2.02},
|
||||||
|
{9.4,2.041,3.788,7.931,2.882,2.672,3.568,1.284,7.033,7.374},
|
||||||
|
{8.025,9.152,5.114,7.621,4.564,4.711,2.996,6.126,0.734,4.982},
|
||||||
|
{2.196,0.415,5.649,6.979,9.510,9.166,6.304,6.054,9.377,1.426},
|
||||||
|
{8.074,8.777,3.467,1.863,6.708,6.349,4.534,0.276,7.633,1.567},
|
||||||
|
{7.650,5.658,0.720,2.764,3.278,5.283,7.474,6.274,1.409,8.208},
|
||||||
|
{1.256,3.605,8.623,6.905,4.584,8.133,6.071,6.888,4.187,5.448},
|
||||||
|
{8.314,2.261,4.24,1.781,4.124,0.932,8.129,8.658,1.208,5.762},
|
||||||
|
{0.226,8.858,1.42,0.954,1.622,4.698,6.228,9.096,0.972,7.637},
|
||||||
|
{7.305,2.228,1.242,5.928,9.133,1.826,4.06,5.204,8.713,8.247},
|
||||||
|
{0.652,7.027,0.508,4.876,8.807,4.632,5.808,6.937,3.291,7.016},
|
||||||
|
{2.699,3.516,5.847,4.119,4.461,7.496,8.817,0.69,6.593,9.789},
|
||||||
|
{8.327,3.897,2.017,9.57,9.825,1.15,1.395,3.885,6.354,0.109},
|
||||||
|
{2.132,7.006,7.136,2.641,1.882,5.943,7.273,7.691,2.88,0.564},
|
||||||
|
{4.707,5.579,4.08,0.581,9.698,8.542,8.077,8.515,9.231,4.67},
|
||||||
|
{8.304,7.559,8.567,0.322,7.128,8.392,1.472,8.524,2.277,7.826},
|
||||||
|
{8.632,4.409,4.832,5.768,7.05,6.715,1.711,4.323,4.405,4.591},
|
||||||
|
{4.887,9.112,0.17,8.967,9.693,9.867,7.508,7.77,8.382,6.74},
|
||||||
|
{2.44,6.686,4.299,1.007,7.008,1.427,9.398,8.48,9.95,1.675},
|
||||||
|
{6.306,8.583,6.084,1.138,4.350,3.134,7.853,6.061,7.457,2.258},
|
||||||
|
{0.652,2.343,1.37,0.821,1.31,1.063,0.689,8.819,8.833,9.07},
|
||||||
|
{5.558,1.272,5.756,9.857,2.279,2.764,1.284,1.677,1.244,1.234},
|
||||||
|
{3.352,7.549,9.817,9.437,8.687,4.167,2.57,6.54,0.228,0.027},
|
||||||
|
{8.798,0.88,2.37,0.168,1.701,3.68,1.231,2.39,2.499,0.064},
|
||||||
|
{1.46,8.057,1.337,7.217,7.914,3.615,9.981,9.198,5.292,1.224},
|
||||||
|
{0.432,8.645,8.774,0.249,8.081,7.461,4.416,0.652,4.002,4.644},
|
||||||
|
{0.679,2.8,5.523,3.049,2.968,7.225,6.73,4.199,9.614,9.229},
|
||||||
|
{4.263,1.074,7.286,5.599,8.291,5.2,9.214,8.272,4.398,4.506},
|
||||||
|
{9.496,4.83,3.15,8.27,5.079,1.231,5.731,9.494,1.883,9.732},
|
||||||
|
{4.138,2.562,2.532,9.661,5.611,5.5,6.886,2.341,9.699,6.5}
|
||||||
|
};
|
||||||
|
|
||||||
|
double schwefel(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 512;
|
||||||
|
int lower_bound = -512;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += (-input[i]) * std::sin(std::sqrt(std::abs(input[i])));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double first_de_jong(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double rosenbrock(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 100 * std::pow((std::pow(input[i], 2) - input[i + 1]), 2) + std::pow((1 - input[i]), 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double rastrigin(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2) - 10 * std::cos(2 * M_PI * input[i]);
|
||||||
|
}
|
||||||
|
|
||||||
|
sum *= 2 * input.size();
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double griewangk(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2) / 4000;
|
||||||
|
}
|
||||||
|
|
||||||
|
double product = 1;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
product *= std::cos(input[i] / sqrt(i + 1));
|
||||||
|
}
|
||||||
|
|
||||||
|
return 1 + sum - product;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double sine_envelope_sine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 0.5 + (std::pow(std::sin(std::pow(input[i], 2) + std::pow(input[i + 1], 2) - 0.5), 2)) /
|
||||||
|
(1 + 0.001 * (std::pow(input[i], 2) + std::pow(input[i + 1], 2)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double stretched_v_sine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += std::pow(std::pow(input[i], 2) + std::pow(input[i + 1], 2), 1.0 / 4) *
|
||||||
|
std::pow(std::sin(50 * std::pow(std::pow(input[i], 2) + std::pow(input[i + 1], 2), 1.0 / 10)), 2) + 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double ackleys_one(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 32;
|
||||||
|
int lower_bound = -32;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += (1.0 / pow(M_E, 0.2)) *
|
||||||
|
std::sqrt(std::pow(input[i], 2) + std::pow(input[i + 1], 2)) +
|
||||||
|
3 * std::cos(2 * input[i]) +
|
||||||
|
std::sin(2 * input[i + 1]);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double ackleys_two(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 32;
|
||||||
|
int lower_bound = -32;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 20 + M_E -
|
||||||
|
(20 / (std::pow(M_E, 0.2) * std::sqrt(((std::pow(input[i], 2) + std::pow(input[i+1], 2) + 1) / 2)))) -
|
||||||
|
std::pow(M_E, 0.5 * std::cos(2 * M_PI * input[i]) + cos(2 * M_PI * input[i + 1]));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double egg_holder(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += -input[i] * std::sin(std::sqrt(abs(input[i] - input[i + 1] - 47))) -
|
||||||
|
(input[i + 1] + 47) * std::sin(std::sqrt(std::abs(input[i + 1] + 47 + input[i] / 2)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double rana(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += input[i] * std::sin(std::sqrt(std::abs(input[i + 1] - input[i] + 1))) *
|
||||||
|
std::cos(std::sqrt(std::abs(input[i + 1] + input[i] + 1))) +
|
||||||
|
(input[i + 1] + 1) *
|
||||||
|
std::cos(std::sqrt(std::abs(input[i + 1] - input[i] + 1))) *
|
||||||
|
std::sin(std::sqrt(std::abs(input[i + 1] + input[i] + 1)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double pathological(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += 0.5 +
|
||||||
|
(std::pow(std::sin(std::sqrt(100 * std::pow(input[i], 2) + std::pow(input[i + 1], 2))), 2) - 0.5) /
|
||||||
|
(1 + 0.001 * std::pow(std::pow(input[i], 2) - 2 * input[i] * input[i + 1] + std::pow(input[i + 1], 2), 2));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double michalewicz(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = M_PI;
|
||||||
|
int lower_bound = 0;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += std::sin(input[i]) * std::pow(std::sin(i * std::pow(input[i], 2) / M_PI), 20);
|
||||||
|
}
|
||||||
|
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double masters_cosine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += std::pow(M_E, -(1/8) * (std::pow(input[i], 2) + std::pow(input[i + 1], 2) + 0.5 * input[i + 1] * input[i])) *
|
||||||
|
std::cos(4 * std::sqrt(std::pow(input[i], 2) + std::pow(input[i + 1], 2) + 0.5 * input[i] * input[i + 1]));
|
||||||
|
}
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double shekels_foxholes(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 10;
|
||||||
|
int lower_bound = 0;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < c.size() - 1; i++) {
|
||||||
|
|
||||||
|
double bottom_sum = 0;
|
||||||
|
|
||||||
|
for (int q = 0; q < input.size(); q++){
|
||||||
|
bottom_sum = std::pow(input.at(q) - a[i][q], 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
sum += 1 / (bottom_sum + c[i]);
|
||||||
|
}
|
||||||
|
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double set_within(double val, double prior_upper, double prior_lower, double after_upper, double after_lower){
|
||||||
|
return ((after_upper - after_lower) * (val - prior_lower) / (prior_upper - prior_lower)) + after_lower;
|
||||||
|
}
|
||||||
|
|
||||||
|
struct function {
|
||||||
|
|
||||||
|
double (*function_pointer)(std::vector<double>);
|
||||||
|
|
||||||
|
double range = 0;
|
||||||
|
double upper_bound = 0;
|
||||||
|
double lower_bound = 0;
|
||||||
|
|
||||||
|
timer t;
|
||||||
|
|
||||||
|
function(){};
|
||||||
|
|
||||||
|
function(double (*func)(std::vector<double>), double upper_bound, double lower_bound) {
|
||||||
|
function_pointer = func;
|
||||||
|
this->upper_bound = upper_bound;
|
||||||
|
this->lower_bound = lower_bound;
|
||||||
|
}
|
||||||
|
|
||||||
|
double compute(std::vector<double> input) {
|
||||||
|
|
||||||
|
for (auto v: input) {
|
||||||
|
if (v <= lower_bound && v >= upper_bound) {
|
||||||
|
std::cout << "Function exceeded bounds";
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
double res = function_pointer(input);
|
||||||
|
return res;
|
||||||
|
|
||||||
|
};
|
||||||
|
};
|
||||||
@ -0,0 +1,167 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class genetic_algorithm : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
genetic_algorithm(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
elitism = elitism_rate * number_of_solutions;
|
||||||
|
|
||||||
|
// Set up random start population
|
||||||
|
std::vector<std::vector<double>> population;
|
||||||
|
for (int p = 0; p < number_of_solutions; p++) {
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
population.push_back(tmp);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
for (int i = 0; i < max_iterations; i++){
|
||||||
|
|
||||||
|
// Setup the random new population
|
||||||
|
std::vector<std::vector<double>> new_population;
|
||||||
|
for (int p = 0; p < number_of_solutions; p++) {
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
new_population.push_back(tmp);
|
||||||
|
}
|
||||||
|
|
||||||
|
for (int s = 0; s < number_of_solutions; s += 2){
|
||||||
|
|
||||||
|
auto p1p2 = select(&population);
|
||||||
|
|
||||||
|
crossover(&std::get<0>(p1p2), &std::get<1>(p1p2));
|
||||||
|
|
||||||
|
mutate(&std::get<0>(p1p2));
|
||||||
|
mutate(&std::get<1>(p1p2));
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
reduce(&population, &new_population);
|
||||||
|
|
||||||
|
for (auto q: population){
|
||||||
|
double val = func.compute(q);
|
||||||
|
if (val < best_fitness)
|
||||||
|
best_fitness = val;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return best_fitness;
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
private:
|
||||||
|
|
||||||
|
double crossover_rate = 0.90;
|
||||||
|
double elitism_rate = 0.2;
|
||||||
|
int elitism = 10;
|
||||||
|
double mutation_rate = 0.008;
|
||||||
|
double mutation_range = 0.1;
|
||||||
|
double muration_precision = 3.5;
|
||||||
|
double best_fitness = 99999999999999999;
|
||||||
|
int number_of_solutions = 50;
|
||||||
|
int max_iterations = 100;
|
||||||
|
|
||||||
|
|
||||||
|
double get_fitness(std::vector<std::vector<double>> *population){
|
||||||
|
|
||||||
|
double fitness_sum = 0;
|
||||||
|
|
||||||
|
for (auto p: *population){
|
||||||
|
double fitness = func.compute(p);
|
||||||
|
if (fitness >= 0)
|
||||||
|
fitness_sum += 1 / (1 + fitness);
|
||||||
|
else
|
||||||
|
fitness_sum += 1 + abs(fitness);
|
||||||
|
}
|
||||||
|
|
||||||
|
return fitness_sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
int select_parent(std::vector<std::vector<double>> *population){
|
||||||
|
|
||||||
|
double r = fmod(randomMT(), total_fitness(population));
|
||||||
|
|
||||||
|
int s = 0;
|
||||||
|
|
||||||
|
while (s < population->size()-1 && (r - func.compute(population->at(s)) > 0)) {
|
||||||
|
r -= func.compute(population->at(s++));
|
||||||
|
}
|
||||||
|
|
||||||
|
return s;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::tuple<std::vector<double>, std::vector<double>> select(std::vector<std::vector<double>> *population){
|
||||||
|
|
||||||
|
auto p1 = population->at(select_parent(population));
|
||||||
|
auto p2 = population->at(select_parent(population));
|
||||||
|
|
||||||
|
return std::make_tuple(p1, p2);
|
||||||
|
};
|
||||||
|
|
||||||
|
void mutate(std::vector<double> *solution){
|
||||||
|
for (auto i: *solution){
|
||||||
|
if ((randomMT() * 1.0 / UINT32_MAX) < mutation_rate){
|
||||||
|
i += ((randomMT() * 1.0 / UINT32_MAX) * 2 - 1) * (func.upper_bound - func.lower_bound) *
|
||||||
|
mutation_range * pow(2, (-1 * (randomMT() * 1.0 / UINT32_MAX) * muration_precision));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void crossover(std::vector<double> *parent1, std::vector<double> *parent2){
|
||||||
|
|
||||||
|
if ((randomMT() * 1.0 / UINT32_MAX) < crossover_rate){
|
||||||
|
|
||||||
|
int d = randomMT() % (parent1->size() - 1) + 1;
|
||||||
|
int dim = parent1->size();
|
||||||
|
|
||||||
|
std::vector<double> temp;
|
||||||
|
temp.insert(temp.begin(), parent1->begin(), parent1->begin() + d);
|
||||||
|
|
||||||
|
parent1->erase(parent1->begin(), parent1->begin() + d);
|
||||||
|
parent1->insert(parent1->end(), parent2->begin() + dim - d, parent2->end());
|
||||||
|
|
||||||
|
parent2->erase(parent2->begin() + dim - d, parent2->end());
|
||||||
|
parent2->insert(parent2->end(), temp.begin(), temp.end());
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void reduce(std::vector<std::vector<double>> *population, std::vector<std::vector<double>> *new_population){
|
||||||
|
|
||||||
|
std::sort(population->begin(), population->end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
|
||||||
|
std::sort(new_population->begin(), new_population->end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
|
||||||
|
for (int s = 0; s < elitism; s++) {
|
||||||
|
new_population->at(elitism + 1 - s) = population->at(s);
|
||||||
|
}
|
||||||
|
|
||||||
|
*population = *new_population;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double total_fitness(std::vector<std::vector<double>> *population){
|
||||||
|
|
||||||
|
double val = 0;
|
||||||
|
|
||||||
|
for (auto i: *population)
|
||||||
|
val += func.compute(i);
|
||||||
|
|
||||||
|
return val;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,65 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class iterative_local_search : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
iterative_local_search(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
// Set up random start
|
||||||
|
std::vector<double> global_best_solution;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
global_best_solution.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
// 30 iteration max
|
||||||
|
int iteration_max = 30;
|
||||||
|
for (int i = 0; i < iteration_max; i++){
|
||||||
|
|
||||||
|
// Random new solution
|
||||||
|
std::vector<double> best_solution;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
best_solution.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<double> temp_solution = best_solution;
|
||||||
|
|
||||||
|
// While a better solution is still being found
|
||||||
|
bool better_solution_found = true;
|
||||||
|
while (better_solution_found) {
|
||||||
|
|
||||||
|
better_solution_found = false;
|
||||||
|
double delta = 0.11;
|
||||||
|
|
||||||
|
temp_solution = best_solution;
|
||||||
|
std::vector<double> new_solution(dimensionality);
|
||||||
|
|
||||||
|
// Set up the new solution
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
|
||||||
|
temp_solution[i] += delta;
|
||||||
|
new_solution[i] = best_solution[i] - delta * (func.compute(temp_solution) - func.compute(best_solution));
|
||||||
|
temp_solution[i] = best_solution[i];
|
||||||
|
// temp[i] - delta * new with delta, and the old without
|
||||||
|
}
|
||||||
|
|
||||||
|
// test it
|
||||||
|
if (func.compute(new_solution) < func.compute(best_solution)) {
|
||||||
|
best_solution = new_solution;
|
||||||
|
better_solution_found = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Check to see if we found a better global solution
|
||||||
|
if (func.compute(best_solution) < func.compute(global_best_solution)){
|
||||||
|
global_best_solution = best_solution;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,53 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class local_search : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
local_search(function f) : search_function(f) {
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
// Set up the initial soution
|
||||||
|
std::vector<double> best_solution;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
best_solution.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<double> temp_solution = best_solution;
|
||||||
|
|
||||||
|
// While a better solution is being found
|
||||||
|
bool better_solution_found = true;
|
||||||
|
while (better_solution_found) {
|
||||||
|
|
||||||
|
better_solution_found = false;
|
||||||
|
double delta = 0.11;
|
||||||
|
|
||||||
|
temp_solution = best_solution;
|
||||||
|
std::vector<double> new_solution(dimensionality);
|
||||||
|
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
|
||||||
|
temp_solution[i] += delta;
|
||||||
|
new_solution[i] = best_solution[i] - delta * (func.compute(temp_solution) - func.compute(best_solution));
|
||||||
|
temp_solution[i] = best_solution[i];
|
||||||
|
// temp[i] - delta * new with delta, and the old without
|
||||||
|
}
|
||||||
|
|
||||||
|
// Check to see if we found a better solution
|
||||||
|
if (func.compute(new_solution) < func.compute(best_solution)) {
|
||||||
|
best_solution = new_solution;
|
||||||
|
better_solution_found = true;
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
return func.compute(best_solution);
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,124 @@
|
|||||||
|
#include <iostream>
|
||||||
|
#include <vector>
|
||||||
|
#include <cmath>
|
||||||
|
#include <map>
|
||||||
|
#include <chrono>
|
||||||
|
#include <cstring>
|
||||||
|
#include "twister.c"
|
||||||
|
#include "util.hpp"
|
||||||
|
#include "functions.hpp"
|
||||||
|
#include "random_walk.hpp"
|
||||||
|
#include "local_search.hpp"
|
||||||
|
#include "iterative_local_search.hpp"
|
||||||
|
#include "differential_evolution.hpp"
|
||||||
|
#include "genetic_algorithm.hpp"
|
||||||
|
#include "particle_search.hpp"
|
||||||
|
|
||||||
|
int main(int argc, char* args[]) {
|
||||||
|
|
||||||
|
std::map<int, function> function_lookup;
|
||||||
|
|
||||||
|
function_lookup.emplace(std::make_pair(0, function(&schwefel, 512, -512)));
|
||||||
|
function_lookup.emplace(std::make_pair(1, function(&first_de_jong, 100, -100)));
|
||||||
|
function_lookup.emplace(std::make_pair(2, function(&rosenbrock, 100, -100)));
|
||||||
|
function_lookup.emplace(std::make_pair(3, function(&rastrigin, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(4, function(&griewangk, 500, -500)));
|
||||||
|
function_lookup.emplace(std::make_pair(5, function(&sine_envelope_sine_wave, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(6, function(&stretched_v_sine_wave, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(7, function(&ackleys_one, 32, -32)));
|
||||||
|
function_lookup.emplace(std::make_pair(8, function(&ackleys_two, 32, -32)));
|
||||||
|
function_lookup.emplace(std::make_pair(9, function(&egg_holder, 500, -500)));
|
||||||
|
function_lookup.emplace(std::make_pair(10, function(&rana, 500, -500)));
|
||||||
|
function_lookup.emplace(std::make_pair(11, function(&pathological, 100, -100)));
|
||||||
|
function_lookup.emplace(std::make_pair(12, function(&michalewicz, M_PI, 0)));
|
||||||
|
function_lookup.emplace(std::make_pair(13, function(&masters_cosine_wave, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(14, function(&shekels_foxholes, 10, 0)));
|
||||||
|
|
||||||
|
function f;
|
||||||
|
int dimensionality = 0;
|
||||||
|
double seed = 0;
|
||||||
|
int search_function = 0;
|
||||||
|
|
||||||
|
// Get the command line args
|
||||||
|
if (argc == 1){
|
||||||
|
char arg_str[200];
|
||||||
|
std::cin.get(arg_str, 200);
|
||||||
|
char t = ' ';
|
||||||
|
|
||||||
|
f = function_lookup[atoi(strtok(arg_str, &t))];
|
||||||
|
dimensionality = atoi(strtok(NULL, &t));
|
||||||
|
seed = atoi(strtok(NULL, &t));
|
||||||
|
search_function = atoi(strtok(NULL, &t));
|
||||||
|
|
||||||
|
} else {
|
||||||
|
|
||||||
|
f = function_lookup[atoi(args[1])];
|
||||||
|
dimensionality = atoi(args[2]);
|
||||||
|
seed = atoi(args[3]);
|
||||||
|
search_function = atoi(args[4]);
|
||||||
|
}
|
||||||
|
|
||||||
|
// srand(time(nullptr));
|
||||||
|
// f = function_lookup[10];
|
||||||
|
// dimensionality = 20;
|
||||||
|
// seed = rand();
|
||||||
|
// search_function = 2;
|
||||||
|
|
||||||
|
// Set up the search functions
|
||||||
|
seedMT(seed);
|
||||||
|
random_walk r_w(f);
|
||||||
|
local_search l_s(f);
|
||||||
|
iterative_local_search it_s(f);
|
||||||
|
differential_evolution dif_ev(f);
|
||||||
|
genetic_algorithm gen_alg(f);
|
||||||
|
particle_search prtcl_src(f);
|
||||||
|
|
||||||
|
// return the results of the search
|
||||||
|
if (search_function == 0)
|
||||||
|
std::cout << r_w.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 1)
|
||||||
|
std::cout << l_s.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 2)
|
||||||
|
std::cout << it_s.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 3)
|
||||||
|
std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 4)
|
||||||
|
std::cout << gen_alg.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 5)
|
||||||
|
std::cout << prtcl_src.search(1, dimensionality) << std::endl;
|
||||||
|
|
||||||
|
//std::cout << r_w.search(1, dimensionality) << std::endl;
|
||||||
|
//std::cout << l_s.search(1, dimensionality) << std::endl;
|
||||||
|
//std::cout << it_s.search(1, dimensionality) << std::endl;
|
||||||
|
//
|
||||||
|
// std::cout << "=====================" << std::endl;
|
||||||
|
//
|
||||||
|
// dif_ev.set_strategy(1);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
// dif_ev.set_strategy(2);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
// dif_ev.set_strategy(3);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
// dif_ev.set_strategy(4);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
// dif_ev.set_strategy(5);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
// dif_ev.set_strategy(6);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
// dif_ev.set_strategy(7);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
// dif_ev.set_strategy(8);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
// dif_ev.set_strategy(9);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
// dif_ev.set_strategy(10);
|
||||||
|
// std::cout << dif_ev.search(1, dimensionality) << std::endl;
|
||||||
|
//
|
||||||
|
// std::cout << "=====================" << std::endl;
|
||||||
|
// std::cout << gen_alg.search(1, dimensionality) << std::endl;
|
||||||
|
//
|
||||||
|
// std::cout << "=====================" << std::endl;
|
||||||
|
// std::cout << prtcl_src.search(1, dimensionality) << std::endl;
|
||||||
|
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
@ -0,0 +1,144 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
struct particle {
|
||||||
|
|
||||||
|
// Personal best
|
||||||
|
double pb_fitness = 999999999999999;
|
||||||
|
std::vector<double> pb_solution;
|
||||||
|
|
||||||
|
// Current solution
|
||||||
|
double fitness = 9999999999;
|
||||||
|
std::vector<double> solution;
|
||||||
|
|
||||||
|
std::vector<double> velocity;
|
||||||
|
double v_max = 4.0;
|
||||||
|
|
||||||
|
double c1 = 0.2;
|
||||||
|
double c2 = 0.2;
|
||||||
|
double weight = 0.9;
|
||||||
|
|
||||||
|
int dimensionality;
|
||||||
|
|
||||||
|
std::vector<double> *gb_solution;
|
||||||
|
double *gb_fitness;
|
||||||
|
|
||||||
|
function *func;
|
||||||
|
|
||||||
|
particle(int dimensionality, std::vector<double> *gb_solution, double *gb_fitness, function *func) : dimensionality(dimensionality){
|
||||||
|
|
||||||
|
this->gb_solution = gb_solution;
|
||||||
|
this->gb_fitness = gb_fitness;
|
||||||
|
this->func = func;
|
||||||
|
|
||||||
|
// Blank initial solution and assign it also to pb
|
||||||
|
pb_solution, solution = std::vector<double>(dimensionality, 0);
|
||||||
|
|
||||||
|
// Init the velocity
|
||||||
|
for (int i = 0; i < dimensionality; i++)
|
||||||
|
velocity.push_back(rand_between(-v_max/3, v_max/3));
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
void update_fitness(){
|
||||||
|
|
||||||
|
fitness = func->compute(solution);
|
||||||
|
if (fitness < pb_fitness){
|
||||||
|
pb_solution = solution;
|
||||||
|
pb_fitness = fitness;
|
||||||
|
}
|
||||||
|
if (fitness < *gb_fitness){
|
||||||
|
*gb_solution = solution;
|
||||||
|
*gb_fitness = fitness;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void update_velocity(){
|
||||||
|
for (int d = 0; d < dimensionality; d++){
|
||||||
|
velocity.at(d) = weight * velocity.at(d) + c1 * rand_between(0, 1) * (pb_solution.at(d) - solution.at(d)) +
|
||||||
|
c2 * rand_between(0, 1) * (gb_solution->at(d) - solution.at(d));
|
||||||
|
if (velocity.at(d) > v_max)
|
||||||
|
velocity.at(d) = v_max;
|
||||||
|
else if (velocity.at(d) < -v_max)
|
||||||
|
velocity.at(d) = -v_max;
|
||||||
|
|
||||||
|
solution.at(d) += velocity.at(d);
|
||||||
|
|
||||||
|
if (solution.at(d) < func->lower_bound)
|
||||||
|
solution.at(d) = func->lower_bound;
|
||||||
|
else if (solution.at(d) > func->upper_bound)
|
||||||
|
solution.at(d) = func->upper_bound;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
class particle_search : public search_function {
|
||||||
|
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
particle_search(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
for (int i = 0; i < number_of_particles; i++){
|
||||||
|
|
||||||
|
particle p(dimensionality, &gb_solution, &gb_fitness, &func);
|
||||||
|
p.solution = generate_solution(dimensionality);
|
||||||
|
p.update_fitness();
|
||||||
|
|
||||||
|
particles.push_back(p);
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
for (int i = 0; i < max_iterations; i++){
|
||||||
|
|
||||||
|
for (int p = 0; p < particles.size(); p++){
|
||||||
|
particles.at(p).update_fitness();
|
||||||
|
particles.at(p).update_velocity();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return gb_fitness;
|
||||||
|
};
|
||||||
|
|
||||||
|
private:
|
||||||
|
|
||||||
|
// The global best solution and fitness
|
||||||
|
double gb_fitness = 99999999999999;
|
||||||
|
std::vector<double> gb_solution;
|
||||||
|
|
||||||
|
int number_of_particles = 100;
|
||||||
|
std::vector<particle> particles;
|
||||||
|
|
||||||
|
int max_iterations = 100;
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,41 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
#include <algorithm>
|
||||||
|
|
||||||
|
class random_walk : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
random_walk(function f) : search_function(f) {
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
timer t;
|
||||||
|
t.start();
|
||||||
|
|
||||||
|
std::vector<double> r;
|
||||||
|
for (int i = 0; i < permutations; i++){
|
||||||
|
|
||||||
|
std::vector<double> dimension_vals;
|
||||||
|
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
auto val = fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound;
|
||||||
|
dimension_vals.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
r.push_back(func.compute(dimension_vals));
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
t.end();
|
||||||
|
|
||||||
|
std::sort(r.begin(), r.end(), std::less<double>());
|
||||||
|
|
||||||
|
return r[0];
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,6 @@
|
|||||||
|
The optimization functions can be either ran with the python script "run.py"
|
||||||
|
which will run all 3 search methods with all 15 search functions.
|
||||||
|
|
||||||
|
Or you can call the executable directly from the command line. Eg.
|
||||||
|
|
||||||
|
./Lab3 <0-14> <dimensionality> <seed> <1-5>
|
||||||
@ -0,0 +1,55 @@
|
|||||||
|
from subprocess import check_output
|
||||||
|
import subprocess
|
||||||
|
import random
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import time
|
||||||
|
|
||||||
|
random.seed()
|
||||||
|
|
||||||
|
loc = "../build/Lab3"
|
||||||
|
|
||||||
|
f = open('out','w')
|
||||||
|
f.write("Timer values")
|
||||||
|
|
||||||
|
for function in range(3, 5):
|
||||||
|
print("Method: " + str(function))
|
||||||
|
for q in range(0, 15):
|
||||||
|
print("Function: " + str(q))
|
||||||
|
f.write(str(q) + "\n")
|
||||||
|
start = time.time()
|
||||||
|
|
||||||
|
subprocess.call([
|
||||||
|
loc,
|
||||||
|
str(q),
|
||||||
|
str(20),
|
||||||
|
str(random.randint(0, 2147483646)),
|
||||||
|
str(function)
|
||||||
|
])
|
||||||
|
|
||||||
|
end = time.time()
|
||||||
|
f.write(str(end-start) + "\n")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
f.close()
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
@ -0,0 +1,243 @@
|
|||||||
|
\documentclass[paper=a4, fontsize=11pt]{scrartcl}
|
||||||
|
\usepackage[T1]{fontenc}
|
||||||
|
\usepackage{fourier}
|
||||||
|
\usepackage[english]{babel}
|
||||||
|
\usepackage[protrusion=true,expansion=true]{microtype}
|
||||||
|
\usepackage{amsmath,amsfonts,amsthm}
|
||||||
|
\usepackage[pdftex]{graphicx}
|
||||||
|
\usepackage{url}
|
||||||
|
\usepackage{sectsty}
|
||||||
|
\usepackage{rotating}
|
||||||
|
\allsectionsfont{\centering \normalfont\scshape}
|
||||||
|
|
||||||
|
\usepackage{fancyhdr}
|
||||||
|
\pagestyle{fancyplain}
|
||||||
|
\fancyhead{}
|
||||||
|
\fancyfoot[L]{}
|
||||||
|
\fancyfoot[C]{}
|
||||||
|
\fancyfoot[R]{\thepage}
|
||||||
|
\renewcommand{\headrulewidth}{0pt}
|
||||||
|
\renewcommand{\footrulewidth}{0pt}
|
||||||
|
\setlength{\headheight}{13.6pt}
|
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\numberwithin{equation}{section}
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\numberwithin{figure}{section}
|
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|
\numberwithin{table}{section}
|
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\newcommand{\horrule}[1]{\rule{\linewidth}{#1}}
|
||||||
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|
||||||
|
\title{
|
||||||
|
%\vspace{-1in}
|
||||||
|
\usefont{OT1}{bch}{b}{n}
|
||||||
|
\normalfont \normalsize \textsc{Central Washington University of the Computer Science Department} \\ [25pt]
|
||||||
|
\horrule{0.5pt} \\[0.4cm]
|
||||||
|
\huge Project 3 \\
|
||||||
|
\horrule{2pt} \\[0.5cm]
|
||||||
|
}
|
||||||
|
|
||||||
|
\author{\normalsize Mitchell Hansen \\[-6pt]}
|
||||||
|
|
||||||
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
|
||||||
|
\begin{document}
|
||||||
|
\maketitle
|
||||||
|
|
||||||
|
\section{Introduction}
|
||||||
|
For this lab we took our 15 optimization functions and ran them through
|
||||||
|
3 new methods of determining the global minimum. The functions being:
|
||||||
|
Differential evolution (DE) which uses a population approach with strategies
|
||||||
|
for computing new solutions, Genetic evolution (GE) which takes a genetic approach
|
||||||
|
with genes, crossover and mutation, and Particle swarm (PS) which simulates
|
||||||
|
swarm movement to find the global minimum.
|
||||||
|
|
||||||
|
\section{Methods}
|
||||||
|
Our rewrite in the previous lab allowed us to just extend three more
|
||||||
|
classes from the search function class we had implemented. These
|
||||||
|
extended classes were then called with a python script and the output
|
||||||
|
printed to the console where I was able to analyze the data. Each test
|
||||||
|
was ran 15 times using the python script and the data stored to a file.
|
||||||
|
|
||||||
|
Upon referencing multiple online sources, we also decided to use a different
|
||||||
|
method of initializing particle velocity vectors and velocity maximums. Settling
|
||||||
|
with velocityMaxium = 4.0, and the initial velocities being created between
|
||||||
|
velocityMaxium \textbackslash 3, and -velocityMaximum \textbackslash 3.
|
||||||
|
These values seemed to produce accurate results.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\section{Analysis}
|
||||||
|
This lab produced some interesting results regarding the performance of the
|
||||||
|
new functions. Overall, the three new functions (PS, GE, DE) were more
|
||||||
|
efficient and accurate than the previous 3 functions (Random Walk, Local Search,
|
||||||
|
Iterative Local Search), but there were some discrepancies with some
|
||||||
|
functions. These discrepancies showed themselves as completely inaccurate
|
||||||
|
results on some functions, while the method would then produce extremely accurate
|
||||||
|
results on other functions. For example, GE produced a 23185.53 average for DeJong,
|
||||||
|
the actual minimum being 0. Yet for the Michalewicz function GE produced an average
|
||||||
|
that was much more competitive to the the other functions.
|
||||||
|
|
||||||
|
Another interesting point on the performance of these functions can be seen when comparing
|
||||||
|
them to the values received from the previous search functions we used. Rosenbrocks saddle
|
||||||
|
is a great example of the performance difference, where Iterative Local Search's best
|
||||||
|
value was in the range of 2.51E+10. DE on the other hand was able to produce a minimum
|
||||||
|
value of 19 and PS a value of 37, massive increases in accuracy. Interestingly enough,
|
||||||
|
for rosenbrocks saddle GE produced a value similar to Iterative local search, a minimum
|
||||||
|
of 3.21E+09.
|
||||||
|
|
||||||
|
More differences between the three functions can be again found with the Rastrigin function.
|
||||||
|
ILS was able to produce a value of 83731.6, GE produced 65280, but PS and DE both had
|
||||||
|
massively more accurate results of: PS -> -6902.05, and DE -> -8000 which we believe is the
|
||||||
|
actual minimum of the Rastrigin function.
|
||||||
|
|
||||||
|
There are other examples of these new functions attaining greater accuracy than the previous
|
||||||
|
functions did, but that information can easily be seen in the results table in figure 5.1. One last point we
|
||||||
|
want to cover is the actual time performance of these algorithms. Previously Local Search and
|
||||||
|
Iterative Local Search both took an excessive amount of time to compute on solutions with
|
||||||
|
larger dimensions (20 +). Based on previous performance, it was estimated that the Griegwangk
|
||||||
|
function running with 30 dimensions would run for 8 hours using Iterative Local Search. To contrast
|
||||||
|
this, the complete computation time taken for the 15 functions, at 15 iterations, using all 3
|
||||||
|
search functions completed faster than one iteration of 20 dimensional Iterative Local Search
|
||||||
|
with the Griewagnk function.
|
||||||
|
|
||||||
|
\section{Conclusion}
|
||||||
|
|
||||||
|
Coming away from this lab we saw that these new functions have the ability to not only
|
||||||
|
improve the accuracy of our results, but also improve the running time of the search.
|
||||||
|
With this improved running time we could run more trials and get even more accurate results
|
||||||
|
than the ones that we are getting currently.
|
||||||
|
|
||||||
|
There were some difficulties and issues when running the tests for this lab. The first being
|
||||||
|
our inability to completely verify our results. We mentioned some discrepancies earlier
|
||||||
|
where GE produced values that were wildly inaccurate for some functions. It is unknown to
|
||||||
|
us whether this is simply a product of the strengths and weaknesses of this specific search
|
||||||
|
method, or if there is something wrong with out implementation. Another issue is that of the
|
||||||
|
Shekels Foxholes function. For Particle Swarm and Genetic Evolution there was no deviation from
|
||||||
|
the single value that they returned. Either the algorithm is able to deterministic
|
||||||
|
produce the apparent global minimum, or there is something wrong with the function.
|
||||||
|
|
||||||
|
|
||||||
|
\begin{figure}
|
||||||
|
\section{Results}
|
||||||
|
\caption{Computation comparison of DE, GA and PSO}
|
||||||
|
\hskip+4.0cm
|
||||||
|
\rotatebox{90.0}{
|
||||||
|
\scalebox{0.7}{
|
||||||
|
\small \centering
|
||||||
|
|
||||||
|
\label{Tab1d}
|
||||||
|
\begin{tabular}{c|lllll|lllll|lllll}
|
||||||
|
\noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
Problem & \multicolumn{5}{c}{DE}& \multicolumn{5}{|c|}{GA}
|
||||||
|
& \multicolumn{5}{c}{PSO} \\
|
||||||
|
\noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
& Avg & Median & Range & SD & T(s) & Avg & Median & Range & SD & T(s) & Avg & Median & Range & SD & T(s) \\ \noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
$f_1$ & -6112.33 & -6084.59 & 114.26 & 47.83 & 1.14 & -3276.12 & -3292.95 & 943.02 & 245.68 & 2.69 & -2871.98 & -2904.39 & 1194.77 & 322.06 & 0.12 \\
|
||||||
|
$f_2$ & 129.53 & 25.00 & 900.00 & 251.52 & 0.53 & 23185.53 & 22853.00 & 10310.00 & 3148.43 & 0.72 & 0.17 & 0.15 & 0.25 & 0.08 & 0.09 \\
|
||||||
|
$f_3$ & 26105.67 & 10019.00 & 168100.00 & 43662.88 & 0.78 & 5291234666.67 & 5017400000.00 & 5739020000.00 & 1539343402.74 & 0.68 & 421.98 & 200.19 & 1657.68 & 497.31 & 0.10 \\
|
||||||
|
$f_4$ & -7600.00 & -7960.00 & 2560.00 & 728.99 & 1.00 & 79752.00 & 81520.00 & 23240.00 & 8507.40 & 2.12 & -5206.62 & -5324.98 & 3479.78 & 1178.83 & 0.13 \\
|
||||||
|
$f_5$ & 0.00 & 0.00 & 0.00 & 0.00 & 1.08 & 145.86 & 150.55 & 51.89 & 17.68 & 2.31 & 9.17 & 8.93 & 5.88 & 1.95 & 0.13 \\
|
||||||
|
$f_6$ & 12.38 & 12.71 & 2.19 & 0.60 & 1.46 & 12.04 & 11.97 & 0.67 & 0.22 & 2.52 & 12.15 & 12.18 & 1.25 & 0.33 & 0.14 \\
|
||||||
|
$f_7$ & 19.06 & 19.01 & 0.62 & 0.16 & 1.67 & 36.69 & 36.60 & 5.76 & 1.54 & 4.20 & 20.55 & 20.45 & 2.63 & 0.68 & 0.18 \\
|
||||||
|
$f_8$ & 58.74 & 58.73 & 4.74 & 1.54 & 1.60 & 212.86 & 213.95 & 41.20 & 11.06 & 3.41 & -9.92 & -11.64 & 35.51 & 9.72 & 0.10 \\
|
||||||
|
$f_9$ & -83.30 & -80.69 & 21.87 & 6.99 & 2.09 & 276.38 & 276.83 & 14.65 & 4.35 & 4.10 & 251.53 & 288.37 & 173.05 & 64.83 & 0.14 \\
|
||||||
|
$f_{10}$ & -4959.12 & -4579.12 & 2896.23 & 966.10 & 3.02 & -4778.37 & -4822.17 & 978.82 & 327.79 & 4.72 & -4107.05 & -3830.50 & 2663.98 & 711.61 & 0.13 \\
|
||||||
|
$f_{11}$ & -8478.48 & -8821.20 & 5161.40 & 1330.20 & 3.56 & -3188.30 & -3181.83 & 1334.30 & 339.30 & 8.34 & -2899.33 & -2888.72 & 901.67 & 227.81 & 0.21 \\
|
||||||
|
$f_{12}$ & 0.00 & 0.00 & 0.00 & 0.00 & 1.48 & 8.00 & 8.01 & 0.69 & 0.17 & 2.70 & 7.02 & 7.08 & 1.30 & 0.37 & 0.15 \\
|
||||||
|
$f_{13}$ & -4.28 & -4.22 & 2.71 & 0.83 & 3.06 & -4.27 & -4.22 & 2.30 & 0.57 & 5.54 & -10.39 & -9.86 & 4.92 & 1.50 & 0.14 \\
|
||||||
|
$f_{14}$ & -18.99 & -19.00 & 0.04 & 0.01 & 1.47 & -10.88 & -10.53 & 3.70 & 1.00 & 3.65 & -16.07 & -16.15 & 5.22 & 1.59 & 0.14 \\
|
||||||
|
$f_{15}$ & -21.91 & -23.03 & 8.39 & 2.95 & 6.05 & -14.64 & -14.64 & 0.00 & 0.00 & 12.55 & -18.70 & -18.70 & 0.00 & 0.00 & 0.27 \\ \noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
& & & & & & & & & & & & & & & \\
|
||||||
|
\noalign{\smallskip}\hline\noalign{\smallskip} \multicolumn{16}{l}{\tiny $^1$ ThinkPad, 3.4GHz Intel Core i7 (3rd gen), 16 GB RAM}
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}}
|
||||||
|
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\newpage
|
||||||
|
\section{Previous Results}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\rotatebox{90}{
|
||||||
|
\begin{tabular}{l || l | l | l | l | l | l | l | l | l | l | l | l | l | l | l}
|
||||||
|
|
||||||
|
\textbf{Iterative Local Search, 20 dimensions} \\
|
||||||
|
\hline \\
|
||||||
|
Function & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
& -4549.4 & 0.0605 & 2.51E+10 & 83731.6 & 0.651206 & 14.7944 & 21.9681 & 279.695 & 321.001 & -1566.84 & -5338.46 & 9.21621 & -0.142619 & -17.715 & -11.8869 \\
|
||||||
|
& -6050.16 & 0.0605 & 4.55E+10 & 80745.8 & 0.268086 & 16.284 & 19.3337 & 343.725 & 313.969 & -1993.75 & -4148.75 & 9.46446 & -0.548652 & -18.1772 & -11.5925 \\
|
||||||
|
& -5398.23 & 0.0591995 & 5.06E+10 & 82401.3 & 0.032413 & 15.3526 & 23.9768 & 296.889 & 321.956 & -4633.19 & -4848.21 & 9.01646 & 0.218791 & -18.4282 & -11.5925 \\
|
||||||
|
& -5675.22 & 0.0605 & 4.42E+10 & 82591.3 & 0.00544314 & 14.7622 & 22.6234 & 331.276 & 323.228 & -4014.04 & -5436.05 & 9.26537 & 0.626347 & -18.2969 & -12.1455 \\
|
||||||
|
& -3976.37 & 0.0588747 & 3.25E+10 & 82036.8 & 0.0128344 & 13.9223 & 25.6647 & 325.015 & 329.982 & -1246.54 & -4823.87 & 9.12409 & -1.33997 & -17.3054 & -11.5925 \\
|
||||||
|
& -5082.38 & 0.0605 & 2.61E+10 & 87382.5 & 0.0201434 & 14.3422 & 21.3897 & 396.392 & 326.205 & -392.775 & -6280.62 & 9.13477 & -2.26781 & -18.1703 & -11.5925 \\
|
||||||
|
& -5891.86 & 0.0605 & 3.85E+10 & 89279.1 & 0.684922 & 15.2594 & 23.0754 & 329.302 & 326.381 & -337.14 & -3871.16 & 9.02972 & -0.0242582 & -18.4513 & -11.5925 \\
|
||||||
|
& -5003.93 & 0.0605 & 3.00E+10 & 85879 & 0.0151074 & 14.8268 & 19.9157 & 325.527 & 314.19 & -1212.15 & -4189.52 & 9.17302 & 0.0681864 & -18.2963 & -11.5925 \\
|
||||||
|
& -5418.57 & 0.0605 & 4.30E+10 & 82890.7 & 0.00544314 & 15.9129 & 20.2634 & 332.571 & 325.788 & -1548.21 & -5548.41 & 9.39065 & -0.906265 & -18.5396 & -11.5925 \\
|
||||||
|
& -5516.7 & 0.0599162 & 3.51E+10 & 82665.9 & 0.03008 & 16.1692 & 19.9074 & 388.651 & 327.169 & 923.714 & -563.104 & 9.20288 & -1.59829 & -18.1054 & -11.5925 \\
|
||||||
|
& -3937.92 & 0.0547374 & 3.35E+10 & 86354.3 & 0.0153078 & 15.3926 & 19.532 & 312.15 & 322.111 & -1598.94 & -4648.01 & 8.87789 & -1.49817 & -17.8506 & -11.5925 \\
|
||||||
|
& -4588.88 & 0.0605 & 3.51E+10 & 81101.4 & 0.00542657 & 15.7841 & 21.7808 & 357.263 & 330.684 & -1319.67 & -4261.9 & 9.33048 & -1.93719 & -18.2627 & -12.1791 \\
|
||||||
|
& -5082.97 & 0.0559769 & 2.42E+10 & 76218.4 & 0.00544314 & 15.1429 & 22.4065 & 346.209 & 324.783 & 1500.62 & -6032.9 & 9.18852 & -1.88437 & -18.2285 & -11.5925 \\
|
||||||
|
& -6070.01 & 0.0594556 & 3.21E+10 & 83486.2 & 0.0201913 & 14.966 & 19.342 & 393.145 & 324.682 & -404.273 & -5094.61 & 8.57302 & 0.126058 & -16.446 & -11.5925 \\
|
||||||
|
& -5043.31 & 0.0605 & 1.85E+10 & 82935.5 & 0.220955 & 14.0295 & 20.2134 & 316.233 & 325.996 & 2079.2 & -4572.43 & 9.70061 & -1.33897 & -18.2227 & -11.5925 \\
|
||||||
|
& -5161.34 & 0.0605 & 3.87E+10 & 85337.7 & 0.00541984 & 14.9478 & 19.5923 & 274.232 & 327.758 & -1727.17 & -4684.33 & 9.01989 & 0.290542 & -18.4796 & -12.1791 \\
|
||||||
|
& -4589.3 & 0.060484 & 3.08E+10 & 83765.5 & 0.01776 & 14.714 & 20.0449 & 314.196 & 325.896 & -1632.08 & -5179.06 & 8.52944 & -0.524841 & -18.3505 & -18.4163 \\
|
||||||
|
& -4332.6 & 0.0600202 & 6.77E+10 & 81498.7 & 0.0153003 & 16.5447 & 20.0096 & 314.165 & 307.423 & 1567.7 & -4461.73 & 9.43874 & -1.20997 & -18.3268 & -11.5925 \\
|
||||||
|
& -6267.41 & 0.0605 & 4.65E+10 & 81603.3 & 1.88596 & 16.1234 & 20.2482 & 316.52 & 317.494 & -5550.9 & -3693.39 & 9.4962 & 0.510028 & -18.1621 & -11.5925 \\
|
||||||
|
& -4588.98 & 0.0605 & 4.06E+10 & 85076.2 & 0.0225431 & 14.0527 & 20.8396 & 325.67 & 324.854 & -2326.43 & -5346.36 & 9.33666 & -1.32004 & -18.555 & -11.5925 \\
|
||||||
|
& -5477.69 & 0.0604347 & 5.13E+10 & 80799.5 & 0.00535823 & 14.7108 & 20.8015 & 261.241 & 319.17 & 485.619 & -4782.73 & 9.17792 & -1.22079 & -17.7232 & -18.1189 \\
|
||||||
|
& -6109.42 & 0.0472961 & 2.54E+10 & 83373.9 & 0.177322 & 15.6127 & 22.9987 & 286.368 & 321.812 & -2998.41 & -4456.01 & 8.6997 & -2.29998 & -18.4625 & -11.5925 \\
|
||||||
|
& -4885.09 & 0.0570039 & 4.39E+10 & 80163.4 & 0.255374 & 15.6927 & 23.8355 & 324.354 & 325.002 & -1578.78 & -4075.51 & 9.10547 & -0.131814 & -17.7901 & -11.5925 \\
|
||||||
|
& -4035.51 & 0.0600693 & 4.32E+10 & 81415.3 & 0.0152417 & 14.9575 & 19.5572 & 296.019 & 321.434 & 824.034 & -4011.91 & 9.31902 & -1.51993 & -17.8755 & -11.5924 \\
|
||||||
|
& -3917.22 & 0.0586818 & 4.16E+10 & 87878.4 & 0.0128369 & 14.4221 & 19.8007 & 375.301 & 300.768 & 1043.95 & -5037.25 & 9.57629 & -1.73324 & -18.2571 & -11.5925 \\
|
||||||
|
& -5754.04 & 0.0575467 & 2.76E+10 & 83622.3 & 0.272711 & 17.8043 & 20.3977 & 370.717 & 317.424 & -1133.68 & -4973.3 & 9.0252 & -1.10353 & -18.3087 & -11.5925 \\
|
||||||
|
& -4786.87 & 0.0605 & 5.54E+10 & 81190 & 0.0324796 & 14.4401 & 19.8204 & 358.965 & 296.103 & 692.475 & -4121.91 & 8.76355 & -0.860885 & -18.484 & -18.4163 \\
|
||||||
|
& -4510.47 & 0.0597792 & 3.24E+10 & 83530.5 & 4.0042 & 16.0272 & 20.4454 & 225.399 & 326.39 & -478.625 & -5370.33 & 9.32801 & -1.13941 & -18.4418 & -11.5729 \\
|
||||||
|
& -5398.26 & 0.0605 & 2.12E+10 & 85510.6 & 0.43707 & 14.8375 & 19.3774 & 332.986 & 327.903 & -985.672 & -316.499 & 8.94067 & 0.336942 & -17.9256 & -11.5925 \\
|
||||||
|
& -4253.69 & 0.0600716 & 3.11E+10 & 84551 & 0.0128391 & 15.5474 & 21.1043 & 361.928 & 322.473 & -2662.15 & -5159.38 & 7.88002 & -0.784451 & -18.5094 & -11.5924 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
Avg. & -5045.1266666667 & 0.05921826 & 3.70E+10 & 8.33E+04 & 3.06E-01 & 1.52E+01 & 2.10E+01 & 3.27E+02 & 3.21E+02 & -1.07E+03 & -4.51E+03 & 9.11E+00 & -8.39E-01 & -1.81E+01 & -1.23E+01 \\
|
||||||
|
Med. & -5062.845 & 0.06045935 & 35130150000 & 83154.7 & 0.02016735 & 15.05445 & 20.33055 & 325.5985 & 323.955 & -1229.345 & -4733.53 & 9.17547 & -1.0048975 & -18.2599 & -11.5925 \\
|
||||||
|
Std. Dev. & 698.8094252189 & 0.0026900942 & 10975111347.1528 & 2619.8872400694 & 0.791604351 & 0.8539153929 & 1.6317417953 & 39.4394076324 & 8.0216805796 & 1808.4389363295 & 1268.809305736 & 0.3628603461 & 0.8562948838 & 0.4364346365 & 2.0376857083 \\
|
||||||
|
|
||||||
|
\hline \\
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
\small{Iterative Local Search Running Times in Seconds}
|
||||||
|
|
||||||
|
\hskip+2.5cm\scalebox{0.5}{
|
||||||
|
\begin{tabular}{l || l | l | l}
|
||||||
|
|
||||||
|
\textbf{Dimensions} & 10 & 20 & 30 \\
|
||||||
|
\hline \\
|
||||||
|
Function 1 & 5.355587244 & 21.5247523785 & 47.5882720947 \\
|
||||||
|
Function 2 & 0.4999251366 & 1.151144743 & 2.4649145603 \\
|
||||||
|
Function 3 & 0.0042607784 & 0.0112228394 & 0.0144929886 \\
|
||||||
|
Function 4 & 0.0058951378 & 0.0114533901 & 0.0161828995 \\
|
||||||
|
Function 5 & 150.1059572697 & 2928.3961615563 & N/A \\
|
||||||
|
Function 6 & 0.0101454258 & 0.0255510807 & 0.0222308636 \\
|
||||||
|
Function 7 & 32.4964332581 & 41.5021996498 & 168.8056237698 \\
|
||||||
|
Function 8 & 0.3526818752 & 1.6770370007 & 3.8826031685 \\
|
||||||
|
Function 9 & 0.0110986233 & 0.0125215054 & 0.0229070187 \\
|
||||||
|
Function 10 & 0.0899729729 & 0.2644715309 & 0.7342042923 \\
|
||||||
|
Function 11 & 30.093629837 & 165.0208876133 & 384.6772966385 \\
|
||||||
|
Function 12 & 297.458874464 & 25.3617525101 & 21.2174470425 \\
|
||||||
|
Function 13 & 0.0197796822 & 0.0436241627 & 0.0463643074 \\
|
||||||
|
Function 14 & 1.5726833344 & 7.7616007328 & 9.7854065895 \\
|
||||||
|
Function 15 & 6.6486163139 & 23.9164574146 & 32.7224471569 \\
|
||||||
|
\hline \\
|
||||||
|
\end{tabular}
|
||||||
|
} \\[0.5cm]
|
||||||
|
|
||||||
|
|
||||||
|
\end{document}
|
||||||
|
|
||||||
@ -0,0 +1,46 @@
|
|||||||
|
#pragma once
|
||||||
|
|
||||||
|
class search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
function func;
|
||||||
|
|
||||||
|
search_function(function f) : func(f) {
|
||||||
|
};
|
||||||
|
|
||||||
|
virtual double search(int permutations, int dimensionality) = 0;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
|
||||||
|
std::vector<double> generate_solution(int dimensionality){
|
||||||
|
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
return tmp;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<std::vector<double>> generate_population(int dimensionality, int population_count){
|
||||||
|
|
||||||
|
std::vector<std::vector<double>> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
tmp.push_back(generate_solution(dimensionality));
|
||||||
|
}
|
||||||
|
return tmp;
|
||||||
|
}
|
||||||
|
|
||||||
|
double check_bounds(double input){
|
||||||
|
if (input > func.upper_bound)
|
||||||
|
return func.upper_bound;
|
||||||
|
else if (input < func.lower_bound)
|
||||||
|
return func.lower_bound;
|
||||||
|
else
|
||||||
|
return input;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
};
|
||||||
@ -0,0 +1,181 @@
|
|||||||
|
// http://www.math.keio.ac.jp/~matumoto/ver980409.html
|
||||||
|
|
||||||
|
// This is the ``Mersenne Twister'' random number generator MT19937, which
|
||||||
|
// generates pseudorandom integers uniformly distributed in 0..(2^32 - 1)
|
||||||
|
// starting from any odd seed in 0..(2^32 - 1). This version is a recode
|
||||||
|
// by Shawn Cokus (Cokus@math.washington.edu) on March 8, 1998 of a version by
|
||||||
|
// Takuji Nishimura (who had suggestions from Topher Cooper and Marc Rieffel in
|
||||||
|
// July-August 1997).
|
||||||
|
//
|
||||||
|
// Effectiveness of the recoding (on Goedel2.math.washington.edu, a DEC Alpha
|
||||||
|
// running OSF/1) using GCC -O3 as a compiler: before recoding: 51.6 sec. to
|
||||||
|
// generate 300 million random numbers; after recoding: 24.0 sec. for the same
|
||||||
|
// (i.e., 46.5% of original time), so speed is now about 12.5 million random
|
||||||
|
// number generations per second on this machine.
|
||||||
|
//
|
||||||
|
// According to the URL <http://www.math.keio.ac.jp/~matumoto/emt.html>
|
||||||
|
// (and paraphrasing a bit in places), the Mersenne Twister is ``designed
|
||||||
|
// with consideration of the flaws of various existing generators,'' has
|
||||||
|
// a period of 2^19937 - 1, gives a sequence that is 623-dimensionally
|
||||||
|
// equidistributed, and ``has passed many stringent tests, including the
|
||||||
|
// die-hard test of G. Marsaglia and the load test of P. Hellekalek and
|
||||||
|
// S. Wegenkittl.'' It is efficient in memory usage (typically using 2506
|
||||||
|
// to 5012 bytes of static data, depending on data type sizes, and the code
|
||||||
|
// is quite short as well). It generates random numbers in batches of 624
|
||||||
|
// at a time, so the caching and pipelining of modern systems is exploited.
|
||||||
|
// It is also divide- and mod-free.
|
||||||
|
//
|
||||||
|
// This library is free software; you can redistribute it and/or modify it
|
||||||
|
// under the terms of the GNU Library General Public License as published by
|
||||||
|
// the Free Software Foundation (either version 2 of the License or, at your
|
||||||
|
// option, any later version). This library is distributed in the hope that
|
||||||
|
// it will be useful, but WITHOUT ANY WARRANTY, without even the implied
|
||||||
|
// warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See
|
||||||
|
// the GNU Library General Public License for more details. You should have
|
||||||
|
// received a copy of the GNU Library General Public License along with this
|
||||||
|
// library; if not, write to the Free Software Foundation, Inc., 59 Temple
|
||||||
|
// Place, Suite 330, Boston, MA 02111-1307, USA.
|
||||||
|
//
|
||||||
|
// The code as Shawn received it included the following notice:
|
||||||
|
//
|
||||||
|
// Copyright (C) 1997 Makoto Matsumoto and Takuji Nishimura. When
|
||||||
|
// you use this, send an e-mail to <matumoto@math.keio.ac.jp> with
|
||||||
|
// an appropriate reference to your work.
|
||||||
|
//
|
||||||
|
// It would be nice to CC: <Cokus@math.washington.edu> when you write.
|
||||||
|
//
|
||||||
|
|
||||||
|
#include <stdio.h>
|
||||||
|
#include <stdlib.h>
|
||||||
|
|
||||||
|
//
|
||||||
|
// uint32 must be an unsigned integer type capable of holding at least 32
|
||||||
|
// bits; exactly 32 should be fastest, but 64 is better on an Alpha with
|
||||||
|
// GCC at -O3 optimization so try your options and see what's best for you
|
||||||
|
//
|
||||||
|
|
||||||
|
typedef unsigned long uint32;
|
||||||
|
|
||||||
|
#define N (624) // length of state vector
|
||||||
|
#define M (397) // a period parameter
|
||||||
|
#define K (0x9908B0DFU) // a magic constant
|
||||||
|
#define hiBit(u) ((u) & 0x80000000U) // mask all but highest bit of u
|
||||||
|
#define loBit(u) ((u) & 0x00000001U) // mask all but lowest bit of u
|
||||||
|
#define loBits(u) ((u) & 0x7FFFFFFFU) // mask the highest bit of u
|
||||||
|
#define mixBits(u, v) (hiBit(u)|loBits(v)) // move hi bit of u to hi bit of v
|
||||||
|
|
||||||
|
static uint32 state[N+1]; // state vector + 1 extra to not violate ANSI C
|
||||||
|
static uint32 *next; // next random value is computed from here
|
||||||
|
static int left = -1; // can *next++ this many times before reloading
|
||||||
|
|
||||||
|
void seedMT(uint32 seed)
|
||||||
|
{
|
||||||
|
//
|
||||||
|
// We initialize state[0..(N-1)] via the generator
|
||||||
|
//
|
||||||
|
// x_new = (69069 * x_old) mod 2^32
|
||||||
|
//
|
||||||
|
// from Line 15 of Table 1, p. 106, Sec. 3.3.4 of Knuth's
|
||||||
|
// _The Art of Computer Programming_, Volume 2, 3rd ed.
|
||||||
|
//
|
||||||
|
// Notes (SJC): I do not know what the initial state requirements
|
||||||
|
// of the Mersenne Twister are, but it seems this seeding generator
|
||||||
|
// could be better. It achieves the maximum period for its modulus
|
||||||
|
// (2^30) iff x_initial is odd (p. 20-21, Sec. 3.2.1.2, Knuth); if
|
||||||
|
// x_initial can be even, you have sequences like 0, 0, 0, ...;
|
||||||
|
// 2^31, 2^31, 2^31, ...; 2^30, 2^30, 2^30, ...; 2^29, 2^29 + 2^31,
|
||||||
|
// 2^29, 2^29 + 2^31, ..., etc. so I force seed to be odd below.
|
||||||
|
//
|
||||||
|
// Even if x_initial is odd, if x_initial is 1 mod 4 then
|
||||||
|
//
|
||||||
|
// the lowest bit of x is always 1,
|
||||||
|
// the next-to-lowest bit of x is always 0,
|
||||||
|
// the 2nd-from-lowest bit of x alternates ... 0 1 0 1 0 1 0 1 ... ,
|
||||||
|
// the 3rd-from-lowest bit of x 4-cycles ... 0 1 1 0 0 1 1 0 ... ,
|
||||||
|
// the 4th-from-lowest bit of x has the 8-cycle ... 0 0 0 1 1 1 1 0 ... ,
|
||||||
|
// ...
|
||||||
|
//
|
||||||
|
// and if x_initial is 3 mod 4 then
|
||||||
|
//
|
||||||
|
// the lowest bit of x is always 1,
|
||||||
|
// the next-to-lowest bit of x is always 1,
|
||||||
|
// the 2nd-from-lowest bit of x alternates ... 0 1 0 1 0 1 0 1 ... ,
|
||||||
|
// the 3rd-from-lowest bit of x 4-cycles ... 0 0 1 1 0 0 1 1 ... ,
|
||||||
|
// the 4th-from-lowest bit of x has the 8-cycle ... 0 0 1 1 1 1 0 0 ... ,
|
||||||
|
// ...
|
||||||
|
//
|
||||||
|
// The generator's potency (min. s>=0 with (69069-1)^s = 0 mod 2^32) is
|
||||||
|
// 16, which seems to be alright by p. 25, Sec. 3.2.1.3 of Knuth. It
|
||||||
|
// also does well in the dimension 2..5 spectral tests, but it could be
|
||||||
|
// better in dimension 6 (Line 15, Table 1, p. 106, Sec. 3.3.4, Knuth).
|
||||||
|
//
|
||||||
|
// Note that the random number user does not see the values generated
|
||||||
|
// here directly since reloadMT() will always munge them first, so maybe
|
||||||
|
// none of all of this matters. In fact, the seed values made here could
|
||||||
|
// even be extra-special desirable if the Mersenne Twister theory says
|
||||||
|
// so-- that's why the only change I made is to restrict to odd seeds.
|
||||||
|
//
|
||||||
|
|
||||||
|
register uint32 x = (seed | 1U) & 0xFFFFFFFFU, *s = state;
|
||||||
|
register int j;
|
||||||
|
|
||||||
|
for(left=0, *s++=x, j=N; --j;
|
||||||
|
*s++ = (x*=69069U) & 0xFFFFFFFFU);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
uint32 reloadMT(void)
|
||||||
|
{
|
||||||
|
register uint32 *p0=state, *p2=state+2, *pM=state+M, s0, s1;
|
||||||
|
register int j;
|
||||||
|
|
||||||
|
if(left < -1)
|
||||||
|
seedMT(4357U);
|
||||||
|
|
||||||
|
left=N-1, next=state+1;
|
||||||
|
|
||||||
|
for(s0=state[0], s1=state[1], j=N-M+1; --j; s0=s1, s1=*p2++)
|
||||||
|
*p0++ = *pM++ ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
|
||||||
|
|
||||||
|
for(pM=state, j=M; --j; s0=s1, s1=*p2++)
|
||||||
|
*p0++ = *pM++ ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
|
||||||
|
|
||||||
|
s1=state[0], *p0 = *pM ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
|
||||||
|
s1 ^= (s1 >> 11);
|
||||||
|
s1 ^= (s1 << 7) & 0x9D2C5680U;
|
||||||
|
s1 ^= (s1 << 15) & 0xEFC60000U;
|
||||||
|
return(s1 ^ (s1 >> 18));
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
uint32 randomMT(void)
|
||||||
|
{
|
||||||
|
uint32 y;
|
||||||
|
|
||||||
|
if(--left < 0)
|
||||||
|
return(reloadMT());
|
||||||
|
|
||||||
|
y = *next++;
|
||||||
|
y ^= (y >> 11);
|
||||||
|
y ^= (y << 7) & 0x9D2C5680U;
|
||||||
|
y ^= (y << 15) & 0xEFC60000U;
|
||||||
|
return(y ^ (y >> 18));
|
||||||
|
}
|
||||||
|
|
||||||
|
#ifdef NOCOMPILE
|
||||||
|
int main(void)
|
||||||
|
{
|
||||||
|
int j;
|
||||||
|
|
||||||
|
// you can seed with any uint32, but the best are odds in 0..(2^32 - 1)
|
||||||
|
|
||||||
|
seedMT(4357U);
|
||||||
|
|
||||||
|
// print the first 2,002 random numbers seven to a line as an example
|
||||||
|
|
||||||
|
for(j=0; j<2002; j++)
|
||||||
|
printf(" %10lu%s", (unsigned long) randomMT(), (j%7)==6 ? "\n" : "");
|
||||||
|
|
||||||
|
return(EXIT_SUCCESS);
|
||||||
|
}
|
||||||
|
#endif
|
||||||
@ -0,0 +1,12 @@
|
|||||||
|
|
||||||
|
struct timer{
|
||||||
|
std::chrono::high_resolution_clock::time_point t1;
|
||||||
|
std::chrono::high_resolution_clock::time_point t2;
|
||||||
|
void start(){t1 = std::chrono::high_resolution_clock::now();}
|
||||||
|
void end(){t2 = std::chrono::high_resolution_clock::now();}
|
||||||
|
double duration(){ return std::chrono::duration_cast<std::chrono::microseconds>( t2 - t1 ).count();}
|
||||||
|
};
|
||||||
|
|
||||||
|
double rand_between(double lower, double upper){
|
||||||
|
return lower + (randomMT() * 1.0 / UINT32_MAX) * (upper - lower);
|
||||||
|
}
|
||||||
@ -0,0 +1,15 @@
|
|||||||
|
cmake_minimum_required(VERSION 3.5.1)
|
||||||
|
|
||||||
|
project(Lab4)
|
||||||
|
|
||||||
|
set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR})
|
||||||
|
|
||||||
|
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
|
||||||
|
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
|
||||||
|
|
||||||
|
file(GLOB SOURCES "*.cpp")
|
||||||
|
file(GLOB HEADERS "*.h" "*.hpp")
|
||||||
|
|
||||||
|
set(SOURCE_FILES ${HEADERS} ${SOURCES} )
|
||||||
|
|
||||||
|
add_executable(Lab4 ${SOURCE_FILES})
|
||||||
Binary file not shown.
@ -0,0 +1,169 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class differential_evolution : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
differential_evolution(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
// Set up random start
|
||||||
|
std::vector<std::vector<double>> population = generate_population(50, dimensionality);
|
||||||
|
|
||||||
|
for (int g = 0; g < maximum_generation_number; g++) {
|
||||||
|
|
||||||
|
for (int i = 0; i < population.size(); i++) {
|
||||||
|
|
||||||
|
// Create the U, temp vector to hold values
|
||||||
|
std::vector<double> u(dimensionality, 0);
|
||||||
|
|
||||||
|
// select a random dimension
|
||||||
|
int j_rand = rand() % dimensionality;
|
||||||
|
|
||||||
|
for (int j = j_rand; j < dimensionality; j++){
|
||||||
|
|
||||||
|
// Accept changes if rng returns < 0.9
|
||||||
|
if (randomMT() * 1.0 / RAND_MAX < 0.9)
|
||||||
|
u.at(j) = check_bounds(compute_with_strategy(&population, j, i));
|
||||||
|
else
|
||||||
|
u.at(j) = population.at(i).at(j);
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
// If the new population has a better fitness, replace it
|
||||||
|
if (func.compute(population.at(i)) > func.compute(u))
|
||||||
|
population.at(i) = u;
|
||||||
|
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Generation is done, return the best value from the population
|
||||||
|
std::sort(population.begin(), population.end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
|
||||||
|
return func.compute(population[0]);
|
||||||
|
};
|
||||||
|
|
||||||
|
void set_strategy(int strategy){
|
||||||
|
this->strategy = strategy;
|
||||||
|
}
|
||||||
|
|
||||||
|
private:
|
||||||
|
|
||||||
|
// G
|
||||||
|
int maximum_generation_number = 30;
|
||||||
|
|
||||||
|
// Tuning variable
|
||||||
|
double tuning_variable_f = 0.8;
|
||||||
|
double tuning_variable_lambda = 1.0;
|
||||||
|
|
||||||
|
int strategy = 0;
|
||||||
|
|
||||||
|
// Compute using different strategies
|
||||||
|
double compute_with_strategy(std::vector<std::vector<double>> *population, int j, int i){
|
||||||
|
|
||||||
|
// Setup and find the best solution in the population that was passed in
|
||||||
|
std::vector<double> best_solution;
|
||||||
|
double best_fitness = 999999999999;
|
||||||
|
|
||||||
|
for (auto p: *population){
|
||||||
|
double val = func.compute(p);
|
||||||
|
|
||||||
|
if (val < best_fitness){
|
||||||
|
best_fitness = val;
|
||||||
|
best_solution = p;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Depending on the strategy, determine the new solution at J
|
||||||
|
switch(strategy){
|
||||||
|
case 1: {
|
||||||
|
std::vector<int> r = distinct_indices(2, population[0].size());
|
||||||
|
return best_solution[j] + tuning_variable_f * (population->at(r[0]).at(j) - population->at(r[1]).at(j));
|
||||||
|
}
|
||||||
|
case 2: {
|
||||||
|
std::vector<int> r = distinct_indices(3, population[0].size());
|
||||||
|
return population->at(r[0]).at(j) + tuning_variable_f * (population->at(r[1]).at(j) - population->at(r[2]).at(j));
|
||||||
|
}
|
||||||
|
case 3:{
|
||||||
|
std::vector<int> r = distinct_indices(2, population[0].size());
|
||||||
|
return population->at(i).at(j) + tuning_variable_lambda * (best_solution.at(j) - population->at(i).at(j)) + tuning_variable_f * (population->at(r[0]).at(j) - population->at(r[1]).at(j));
|
||||||
|
}
|
||||||
|
case 4:{
|
||||||
|
std::vector<int> r = distinct_indices(4, population[0].size());
|
||||||
|
return best_solution.at(j) + tuning_variable_f * (population->at(r[0]).at(j) + population->at(r[1]).at(j) - population->at(r[2]).at(j) - population->at(r[3]).at(j));
|
||||||
|
}
|
||||||
|
case 5:{
|
||||||
|
std::vector<int> r = distinct_indices(5, population[0].size());
|
||||||
|
return population->at(r[4]).at(j) + tuning_variable_f * (population->at(r[0]).at(j) + population->at(r[1]).at(j) - population->at(r[2]).at(j) - population->at(r[3]).at(j));
|
||||||
|
}
|
||||||
|
case 6:{
|
||||||
|
std::vector<int> r = distinct_indices(2, population[0].size());
|
||||||
|
return best_solution.at(j) + tuning_variable_f * (population->at(r[0]).at(j) - population->at(r[1]).at(j));
|
||||||
|
}
|
||||||
|
case 7:{
|
||||||
|
std::vector<int> r = distinct_indices(3, population[0].size());
|
||||||
|
return population->at(r[0]).at(j) + tuning_variable_f * (population->at(r[1]).at(j) - population->at(r[2]).at(j));
|
||||||
|
}
|
||||||
|
case 8:{
|
||||||
|
std::vector<int> r = distinct_indices(2, population[0].size());
|
||||||
|
return population->at(i).at(j) + tuning_variable_lambda * (best_solution.at(j) - population->at(i).at(j)) + tuning_variable_f * (population->at(r[0]).at(j) - population->at(r[1]).at(j));
|
||||||
|
}
|
||||||
|
case 9:{
|
||||||
|
std::vector<int> r = distinct_indices(4, population[0].size());
|
||||||
|
return best_solution.at(j) + tuning_variable_f * (population->at(r[0]).at(j) + population->at(r[1]).at(j) - population->at(r[2]).at(j) - population->at(r[3]).at(j));
|
||||||
|
}
|
||||||
|
case 10:{
|
||||||
|
std::vector<int> r = distinct_indices(5, population[0].size());
|
||||||
|
return population->at(r[4]).at(j) + tuning_variable_f * (population->at(r[0]).at(j) + population->at(r[1]).at(j) - population->at(r[2]).at(j) - population->at(r[3]).at(j));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<int> distinct_indices(int count, int max){
|
||||||
|
|
||||||
|
std::vector<int> indices;
|
||||||
|
for (int q = 0; q < count; q++) {
|
||||||
|
int val = 1 + (rand() % (max - 1));
|
||||||
|
while (std::count(indices.begin(), indices.end(), val) != 0)
|
||||||
|
val = randomMT() % max;
|
||||||
|
indices.push_back(val);
|
||||||
|
}
|
||||||
|
return indices;
|
||||||
|
}
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,87 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class firefly : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
firefly(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
// Set up random start population
|
||||||
|
for (int p = 0; p < population_size; p++) {
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
population.push_back(tmp);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
for (int t = 0; t < iterations; t++){
|
||||||
|
|
||||||
|
// Compare each fly to each other
|
||||||
|
for (int i = 0; i < population.size(); i++){
|
||||||
|
for (int j = 0; j < population.size(); j++){
|
||||||
|
|
||||||
|
// If it finds one brighter
|
||||||
|
if (func.compute(population.at(j)) < func.compute(population.at(i))){
|
||||||
|
|
||||||
|
// Move towards it for each dimension
|
||||||
|
for (int d = 0; d < dimensionality; d++){
|
||||||
|
|
||||||
|
population.at(i).at(d) =
|
||||||
|
population.at(i).at(d) +
|
||||||
|
attractiveness(distance(population.at(i), population.at(j))) *
|
||||||
|
(population.at(j).at(d) - population.at(i).at(d)) + alpha *
|
||||||
|
rand_between(-0.5, 0.5);
|
||||||
|
|
||||||
|
check_solution_bounds(&population.at(i));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Sort the population and return the best fitness
|
||||||
|
std::sort(population.begin(), population.end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
return func.compute(population.front());
|
||||||
|
};
|
||||||
|
|
||||||
|
private:
|
||||||
|
|
||||||
|
// Distance between two vector points
|
||||||
|
double distance(std::vector<double> a, std::vector<double> b){
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
for (int i = 0; i < a.size(); i++){
|
||||||
|
sum += pow(a.at(i) - b.at(i), 2);
|
||||||
|
}
|
||||||
|
return sqrt(sum);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Inverse square law basically
|
||||||
|
double attractiveness(double distance){
|
||||||
|
return beta * exp(-gamma * distance * distance);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Constants
|
||||||
|
double gamma = 1.0;
|
||||||
|
double alpha = 0.5;
|
||||||
|
double beta = 0.2;
|
||||||
|
|
||||||
|
int iterations = 100;
|
||||||
|
int population_size = 50;
|
||||||
|
|
||||||
|
std::vector<std::vector<double>> population;
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,319 @@
|
|||||||
|
|
||||||
|
std::vector<double> c = {0.806,0.517,0.1,0.908,0.965,0.669,0.524,0.902,0.351,0.876,0.462,
|
||||||
|
0.491,0.463,0.741,0.352,0.869,0.813,0.811,0.0828,0.964,0.789,0.360,0.369,
|
||||||
|
0.992,0.332,0.817,0.632,0.883,0.608,0.326};
|
||||||
|
|
||||||
|
double a[][10] =
|
||||||
|
{
|
||||||
|
{9.681,0.667,4.783,9.095,3.517,9.325,6.544,0.211,5.122,2.02},
|
||||||
|
{9.4,2.041,3.788,7.931,2.882,2.672,3.568,1.284,7.033,7.374},
|
||||||
|
{8.025,9.152,5.114,7.621,4.564,4.711,2.996,6.126,0.734,4.982},
|
||||||
|
{2.196,0.415,5.649,6.979,9.510,9.166,6.304,6.054,9.377,1.426},
|
||||||
|
{8.074,8.777,3.467,1.863,6.708,6.349,4.534,0.276,7.633,1.567},
|
||||||
|
{7.650,5.658,0.720,2.764,3.278,5.283,7.474,6.274,1.409,8.208},
|
||||||
|
{1.256,3.605,8.623,6.905,4.584,8.133,6.071,6.888,4.187,5.448},
|
||||||
|
{8.314,2.261,4.24,1.781,4.124,0.932,8.129,8.658,1.208,5.762},
|
||||||
|
{0.226,8.858,1.42,0.954,1.622,4.698,6.228,9.096,0.972,7.637},
|
||||||
|
{7.305,2.228,1.242,5.928,9.133,1.826,4.06,5.204,8.713,8.247},
|
||||||
|
{0.652,7.027,0.508,4.876,8.807,4.632,5.808,6.937,3.291,7.016},
|
||||||
|
{2.699,3.516,5.847,4.119,4.461,7.496,8.817,0.69,6.593,9.789},
|
||||||
|
{8.327,3.897,2.017,9.57,9.825,1.15,1.395,3.885,6.354,0.109},
|
||||||
|
{2.132,7.006,7.136,2.641,1.882,5.943,7.273,7.691,2.88,0.564},
|
||||||
|
{4.707,5.579,4.08,0.581,9.698,8.542,8.077,8.515,9.231,4.67},
|
||||||
|
{8.304,7.559,8.567,0.322,7.128,8.392,1.472,8.524,2.277,7.826},
|
||||||
|
{8.632,4.409,4.832,5.768,7.05,6.715,1.711,4.323,4.405,4.591},
|
||||||
|
{4.887,9.112,0.17,8.967,9.693,9.867,7.508,7.77,8.382,6.74},
|
||||||
|
{2.44,6.686,4.299,1.007,7.008,1.427,9.398,8.48,9.95,1.675},
|
||||||
|
{6.306,8.583,6.084,1.138,4.350,3.134,7.853,6.061,7.457,2.258},
|
||||||
|
{0.652,2.343,1.37,0.821,1.31,1.063,0.689,8.819,8.833,9.07},
|
||||||
|
{5.558,1.272,5.756,9.857,2.279,2.764,1.284,1.677,1.244,1.234},
|
||||||
|
{3.352,7.549,9.817,9.437,8.687,4.167,2.57,6.54,0.228,0.027},
|
||||||
|
{8.798,0.88,2.37,0.168,1.701,3.68,1.231,2.39,2.499,0.064},
|
||||||
|
{1.46,8.057,1.337,7.217,7.914,3.615,9.981,9.198,5.292,1.224},
|
||||||
|
{0.432,8.645,8.774,0.249,8.081,7.461,4.416,0.652,4.002,4.644},
|
||||||
|
{0.679,2.8,5.523,3.049,2.968,7.225,6.73,4.199,9.614,9.229},
|
||||||
|
{4.263,1.074,7.286,5.599,8.291,5.2,9.214,8.272,4.398,4.506},
|
||||||
|
{9.496,4.83,3.15,8.27,5.079,1.231,5.731,9.494,1.883,9.732},
|
||||||
|
{4.138,2.562,2.532,9.661,5.611,5.5,6.886,2.341,9.699,6.5}
|
||||||
|
};
|
||||||
|
|
||||||
|
double schwefel(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 512;
|
||||||
|
int lower_bound = -512;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += (-input[i]) * std::sin(std::sqrt(std::abs(input[i])));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double first_de_jong(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double rosenbrock(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 100 * std::pow((std::pow(input[i], 2) - input[i + 1]), 2) + std::pow((1 - input[i]), 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double rastrigin(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2) - 10 * std::cos(2 * M_PI * input[i]);
|
||||||
|
}
|
||||||
|
|
||||||
|
sum *= 2 * input.size();
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double griewangk(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
sum += std::pow(input[i], 2) / 4000;
|
||||||
|
}
|
||||||
|
|
||||||
|
double product = 1;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size(); i++){
|
||||||
|
product *= std::cos(input[i] / sqrt(i + 1));
|
||||||
|
}
|
||||||
|
|
||||||
|
return 1 + sum - product;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double sine_envelope_sine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 0.5 + (std::pow(std::sin(std::pow(input[i], 2) + std::pow(input[i + 1], 2) - 0.5), 2)) /
|
||||||
|
(1 + 0.001 * (std::pow(input[i], 2) + std::pow(input[i + 1], 2)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double stretched_v_sine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += std::pow(std::pow(input[i], 2) + std::pow(input[i + 1], 2), 1.0 / 4) *
|
||||||
|
std::pow(std::sin(50 * std::pow(std::pow(input[i], 2) + std::pow(input[i + 1], 2), 1.0 / 10)), 2) + 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double ackleys_one(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 32;
|
||||||
|
int lower_bound = -32;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += (1.0 / pow(M_E, 0.2)) *
|
||||||
|
std::sqrt(std::pow(input[i], 2) + std::pow(input[i + 1], 2)) +
|
||||||
|
3 * std::cos(2 * input[i]) +
|
||||||
|
std::sin(2 * input[i + 1]);
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double ackleys_two(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 32;
|
||||||
|
int lower_bound = -32;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++){
|
||||||
|
sum += 20 + M_E -
|
||||||
|
(20 / (std::pow(M_E, 0.2) * std::sqrt(((std::pow(input[i], 2) + std::pow(input[i+1], 2) + 1) / 2)))) -
|
||||||
|
std::pow(M_E, 0.5 * std::cos(2 * M_PI * input[i]) + cos(2 * M_PI * input[i + 1]));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double egg_holder(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += -input[i] * std::sin(std::sqrt(abs(input[i] - input[i + 1] - 47))) -
|
||||||
|
(input[i + 1] + 47) * std::sin(std::sqrt(std::abs(input[i + 1] + 47 + input[i] / 2)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double rana(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 500;
|
||||||
|
int lower_bound = -500;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += input[i] * std::sin(std::sqrt(std::abs(input[i + 1] - input[i] + 1))) *
|
||||||
|
std::cos(std::sqrt(std::abs(input[i + 1] + input[i] + 1))) +
|
||||||
|
(input[i + 1] + 1) *
|
||||||
|
std::cos(std::sqrt(std::abs(input[i + 1] - input[i] + 1))) *
|
||||||
|
std::sin(std::sqrt(std::abs(input[i + 1] + input[i] + 1)));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double pathological(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 100;
|
||||||
|
int lower_bound = -100;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += 0.5 +
|
||||||
|
(std::pow(std::sin(std::sqrt(100 * std::pow(input[i], 2) + std::pow(input[i + 1], 2))), 2) - 0.5) /
|
||||||
|
(1 + 0.001 * std::pow(std::pow(input[i], 2) - 2 * input[i] * input[i + 1] + std::pow(input[i + 1], 2), 2));
|
||||||
|
}
|
||||||
|
|
||||||
|
return sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double michalewicz(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = M_PI;
|
||||||
|
int lower_bound = 0;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += std::sin(input[i]) * std::pow(std::sin(i * std::pow(input[i], 2) / M_PI), 20);
|
||||||
|
}
|
||||||
|
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double masters_cosine_wave(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 30;
|
||||||
|
int lower_bound = -30;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < input.size() - 1; i++) {
|
||||||
|
sum += std::pow(M_E, -(1/8) * (std::pow(input[i], 2) + std::pow(input[i + 1], 2) + 0.5 * input[i + 1] * input[i])) *
|
||||||
|
std::cos(4 * std::sqrt(std::pow(input[i], 2) + std::pow(input[i + 1], 2) + 0.5 * input[i] * input[i + 1]));
|
||||||
|
}
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double shekels_foxholes(std::vector<double> input){
|
||||||
|
|
||||||
|
int upper_bound = 10;
|
||||||
|
int lower_bound = 0;
|
||||||
|
|
||||||
|
double sum = 0;
|
||||||
|
|
||||||
|
for (int i = 0; i < c.size() - 1; i++) {
|
||||||
|
|
||||||
|
double bottom_sum = 0;
|
||||||
|
|
||||||
|
for (int q = 0; q < input.size(); q++){
|
||||||
|
bottom_sum = std::pow(input.at(q) - a[i][q], 2);
|
||||||
|
}
|
||||||
|
|
||||||
|
sum += 1 / (bottom_sum + c[i]);
|
||||||
|
}
|
||||||
|
|
||||||
|
return -sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
double set_within(double val, double prior_upper, double prior_lower, double after_upper, double after_lower){
|
||||||
|
return ((after_upper - after_lower) * (val - prior_lower) / (prior_upper - prior_lower)) + after_lower;
|
||||||
|
}
|
||||||
|
|
||||||
|
struct function {
|
||||||
|
|
||||||
|
double (*function_pointer)(std::vector<double>);
|
||||||
|
|
||||||
|
double range = 0;
|
||||||
|
double upper_bound = 0;
|
||||||
|
double lower_bound = 0;
|
||||||
|
|
||||||
|
timer t;
|
||||||
|
|
||||||
|
function(){};
|
||||||
|
|
||||||
|
function(double (*func)(std::vector<double>), double upper_bound, double lower_bound) {
|
||||||
|
function_pointer = func;
|
||||||
|
this->upper_bound = upper_bound;
|
||||||
|
this->lower_bound = lower_bound;
|
||||||
|
}
|
||||||
|
|
||||||
|
double compute(std::vector<double> input) {
|
||||||
|
|
||||||
|
for (auto v: input) {
|
||||||
|
if (v <= lower_bound && v >= upper_bound) {
|
||||||
|
std::cout << "Function exceeded bounds";
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
double res = function_pointer(input);
|
||||||
|
return res;
|
||||||
|
|
||||||
|
};
|
||||||
|
};
|
||||||
@ -0,0 +1,167 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class genetic_algorithm : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
genetic_algorithm(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
elitism = elitism_rate * number_of_solutions;
|
||||||
|
|
||||||
|
// Set up random start population
|
||||||
|
std::vector<std::vector<double>> population;
|
||||||
|
for (int p = 0; p < number_of_solutions; p++) {
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
population.push_back(tmp);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
for (int i = 0; i < max_iterations; i++){
|
||||||
|
|
||||||
|
// Setup the random new population
|
||||||
|
std::vector<std::vector<double>> new_population;
|
||||||
|
for (int p = 0; p < number_of_solutions; p++) {
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
new_population.push_back(tmp);
|
||||||
|
}
|
||||||
|
|
||||||
|
for (int s = 0; s < number_of_solutions; s += 2){
|
||||||
|
|
||||||
|
auto p1p2 = select(&population);
|
||||||
|
|
||||||
|
crossover(&std::get<0>(p1p2), &std::get<1>(p1p2));
|
||||||
|
|
||||||
|
mutate(&std::get<0>(p1p2));
|
||||||
|
mutate(&std::get<1>(p1p2));
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
reduce(&population, &new_population);
|
||||||
|
|
||||||
|
for (auto q: population){
|
||||||
|
double val = func.compute(q);
|
||||||
|
if (val < best_fitness)
|
||||||
|
best_fitness = val;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return best_fitness;
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
private:
|
||||||
|
|
||||||
|
double crossover_rate = 0.90;
|
||||||
|
double elitism_rate = 0.2;
|
||||||
|
int elitism = 10;
|
||||||
|
double mutation_rate = 0.008;
|
||||||
|
double mutation_range = 0.1;
|
||||||
|
double muration_precision = 3.5;
|
||||||
|
double best_fitness = 99999999999999999;
|
||||||
|
int number_of_solutions = 50;
|
||||||
|
int max_iterations = 100;
|
||||||
|
|
||||||
|
|
||||||
|
double get_fitness(std::vector<std::vector<double>> *population){
|
||||||
|
|
||||||
|
double fitness_sum = 0;
|
||||||
|
|
||||||
|
for (auto p: *population){
|
||||||
|
double fitness = func.compute(p);
|
||||||
|
if (fitness >= 0)
|
||||||
|
fitness_sum += 1 / (1 + fitness);
|
||||||
|
else
|
||||||
|
fitness_sum += 1 + abs(fitness);
|
||||||
|
}
|
||||||
|
|
||||||
|
return fitness_sum;
|
||||||
|
}
|
||||||
|
|
||||||
|
int select_parent(std::vector<std::vector<double>> *population){
|
||||||
|
|
||||||
|
double r = fmod(randomMT(), total_fitness(population));
|
||||||
|
|
||||||
|
int s = 0;
|
||||||
|
|
||||||
|
while (s < population->size()-1 && (r - func.compute(population->at(s)) > 0)) {
|
||||||
|
r -= func.compute(population->at(s++));
|
||||||
|
}
|
||||||
|
|
||||||
|
return s;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::tuple<std::vector<double>, std::vector<double>> select(std::vector<std::vector<double>> *population){
|
||||||
|
|
||||||
|
auto p1 = population->at(select_parent(population));
|
||||||
|
auto p2 = population->at(select_parent(population));
|
||||||
|
|
||||||
|
return std::make_tuple(p1, p2);
|
||||||
|
};
|
||||||
|
|
||||||
|
void mutate(std::vector<double> *solution){
|
||||||
|
for (auto i: *solution){
|
||||||
|
if ((randomMT() * 1.0 / UINT32_MAX) < mutation_rate){
|
||||||
|
i += ((randomMT() * 1.0 / UINT32_MAX) * 2 - 1) * (func.upper_bound - func.lower_bound) *
|
||||||
|
mutation_range * pow(2, (-1 * (randomMT() * 1.0 / UINT32_MAX) * muration_precision));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void crossover(std::vector<double> *parent1, std::vector<double> *parent2){
|
||||||
|
|
||||||
|
if ((randomMT() * 1.0 / UINT32_MAX) < crossover_rate){
|
||||||
|
|
||||||
|
int d = randomMT() % (parent1->size() - 1) + 1;
|
||||||
|
int dim = parent1->size();
|
||||||
|
|
||||||
|
std::vector<double> temp;
|
||||||
|
temp.insert(temp.begin(), parent1->begin(), parent1->begin() + d);
|
||||||
|
|
||||||
|
parent1->erase(parent1->begin(), parent1->begin() + d);
|
||||||
|
parent1->insert(parent1->end(), parent2->begin() + dim - d, parent2->end());
|
||||||
|
|
||||||
|
parent2->erase(parent2->begin() + dim - d, parent2->end());
|
||||||
|
parent2->insert(parent2->end(), temp.begin(), temp.end());
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void reduce(std::vector<std::vector<double>> *population, std::vector<std::vector<double>> *new_population){
|
||||||
|
|
||||||
|
std::sort(population->begin(), population->end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
|
||||||
|
std::sort(new_population->begin(), new_population->end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
|
||||||
|
for (int s = 0; s < elitism; s++) {
|
||||||
|
new_population->at(elitism + 1 - s) = population->at(s);
|
||||||
|
}
|
||||||
|
|
||||||
|
*population = *new_population;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double total_fitness(std::vector<std::vector<double>> *population){
|
||||||
|
|
||||||
|
double val = 0;
|
||||||
|
|
||||||
|
for (auto i: *population)
|
||||||
|
val += func.compute(i);
|
||||||
|
|
||||||
|
return val;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,138 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class harmony : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
harmony(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
this->dimensionality = dimensionality;
|
||||||
|
|
||||||
|
// Set up random start population
|
||||||
|
for (int p = 0; p < population_size; p++) {
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
population.push_back(tmp);
|
||||||
|
}
|
||||||
|
|
||||||
|
double best_fitness = func.compute(population.at(0));
|
||||||
|
|
||||||
|
// Do a thousand iterations
|
||||||
|
int r = 0;
|
||||||
|
while (r < 1000){
|
||||||
|
|
||||||
|
// The rng to determine which of the 3 we chose
|
||||||
|
double rng = rand_between(0, 1);
|
||||||
|
|
||||||
|
// Accept up the the specified percentage, re init the rest
|
||||||
|
if (rng > r_accept) {
|
||||||
|
accept_and_generate();
|
||||||
|
}
|
||||||
|
|
||||||
|
// Harmonize
|
||||||
|
else if (rng > r_pa) {
|
||||||
|
for (int p = 0; p < population.size(); p++) {
|
||||||
|
for (int d = 0; d < dimensionality; d++) {
|
||||||
|
population.at(p).at(d) = population.at(p).at(d) + rand_between(1, 2) * rand_between(-1, 1);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Introduce some stochasticity
|
||||||
|
else{
|
||||||
|
population.clear();
|
||||||
|
for (int p = 0; p < population_size; p++) {
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
population.push_back(tmp);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Sort the array and find the best fitness, record
|
||||||
|
std::sort(population.begin(), population.end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
|
||||||
|
if (func.compute(population.at(0)) < best_fitness)
|
||||||
|
best_fitness = func.compute(population.at(0));
|
||||||
|
|
||||||
|
r++;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
return func.compute(population.at(0));
|
||||||
|
};
|
||||||
|
|
||||||
|
private:
|
||||||
|
|
||||||
|
double r_accept = 0.8;
|
||||||
|
double r_pa = 0.1;
|
||||||
|
|
||||||
|
int population_size = 50;
|
||||||
|
int dimensionality = 0;
|
||||||
|
|
||||||
|
void accept_and_generate(){
|
||||||
|
|
||||||
|
// Resort the population so the best will be at(0)
|
||||||
|
std::sort(population.begin(), population.end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
|
||||||
|
// Keep up to the accept rate
|
||||||
|
std::vector<std::vector<double>> tmp_pop;
|
||||||
|
for (int i = 0; (1.0 * i / population.size()) < r_accept; i++){
|
||||||
|
tmp_pop.push_back(population.at(i));
|
||||||
|
}
|
||||||
|
|
||||||
|
// Repopulate with random values
|
||||||
|
for (int p = 0; p < population_size - tmp_pop.size(); p++) {
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
tmp_pop.push_back(tmp);
|
||||||
|
}
|
||||||
|
|
||||||
|
population = tmp_pop;
|
||||||
|
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
std::vector<std::vector<double>> population;
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,65 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class iterative_local : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
iterative_local(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
// Set up random start
|
||||||
|
std::vector<double> global_best_solution;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
global_best_solution.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
// 30 iteration max
|
||||||
|
int iteration_max = 30;
|
||||||
|
for (int i = 0; i < iteration_max; i++){
|
||||||
|
|
||||||
|
// Random new solution
|
||||||
|
std::vector<double> best_solution;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
best_solution.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<double> temp_solution = best_solution;
|
||||||
|
|
||||||
|
// While a better solution is still being found
|
||||||
|
bool better_solution_found = true;
|
||||||
|
while (better_solution_found) {
|
||||||
|
|
||||||
|
better_solution_found = false;
|
||||||
|
double delta = 0.11;
|
||||||
|
|
||||||
|
temp_solution = best_solution;
|
||||||
|
std::vector<double> new_solution(dimensionality);
|
||||||
|
|
||||||
|
// Set up the new solution
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
|
||||||
|
temp_solution[i] += delta;
|
||||||
|
new_solution[i] = best_solution[i] - delta * (func.compute(temp_solution) - func.compute(best_solution));
|
||||||
|
temp_solution[i] = best_solution[i];
|
||||||
|
// temp[i] - delta * new with delta, and the old without
|
||||||
|
}
|
||||||
|
|
||||||
|
// test it
|
||||||
|
if (func.compute(new_solution) < func.compute(best_solution)) {
|
||||||
|
best_solution = new_solution;
|
||||||
|
better_solution_found = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Check to see if we found a better global solution
|
||||||
|
if (func.compute(best_solution) < func.compute(global_best_solution)){
|
||||||
|
global_best_solution = best_solution;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,53 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class local : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
local(function f) : search_function(f) {
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
// Set up the initial soution
|
||||||
|
std::vector<double> best_solution;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
best_solution.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<double> temp_solution = best_solution;
|
||||||
|
|
||||||
|
// While a better solution is being found
|
||||||
|
bool better_solution_found = true;
|
||||||
|
while (better_solution_found) {
|
||||||
|
|
||||||
|
better_solution_found = false;
|
||||||
|
double delta = 0.11;
|
||||||
|
|
||||||
|
temp_solution = best_solution;
|
||||||
|
std::vector<double> new_solution(dimensionality);
|
||||||
|
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
|
||||||
|
temp_solution[i] += delta;
|
||||||
|
new_solution[i] = best_solution[i] - delta * (func.compute(temp_solution) - func.compute(best_solution));
|
||||||
|
temp_solution[i] = best_solution[i];
|
||||||
|
// temp[i] - delta * new with delta, and the old without
|
||||||
|
}
|
||||||
|
|
||||||
|
// Check to see if we found a better solution
|
||||||
|
if (func.compute(new_solution) < func.compute(best_solution)) {
|
||||||
|
best_solution = new_solution;
|
||||||
|
better_solution_found = true;
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
return func.compute(best_solution);
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,105 @@
|
|||||||
|
#include <iostream>
|
||||||
|
#include <vector>
|
||||||
|
#include <cmath>
|
||||||
|
#include <map>
|
||||||
|
#include <chrono>
|
||||||
|
#include <cstring>
|
||||||
|
#include "twister.c"
|
||||||
|
#include "util.hpp"
|
||||||
|
#include "functions.hpp"
|
||||||
|
#include "random_walk.hpp"
|
||||||
|
#include "local.hpp"
|
||||||
|
#include "iterative_local.hpp"
|
||||||
|
#include "differential_evolution.hpp"
|
||||||
|
#include "genetic_algorithm.hpp"
|
||||||
|
#include "particle_swrm.hpp"
|
||||||
|
#include "soma.hpp"
|
||||||
|
#include "harmony.hpp"
|
||||||
|
#include "firefly.hpp"
|
||||||
|
|
||||||
|
int main(int argc, char* args[]) {
|
||||||
|
|
||||||
|
std::map<int, function> function_lookup;
|
||||||
|
|
||||||
|
function_lookup.emplace(std::make_pair(0, function(&schwefel, 512, -512)));
|
||||||
|
function_lookup.emplace(std::make_pair(1, function(&first_de_jong, 100, -100)));
|
||||||
|
function_lookup.emplace(std::make_pair(2, function(&rosenbrock, 100, -100)));
|
||||||
|
function_lookup.emplace(std::make_pair(3, function(&rastrigin, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(4, function(&griewangk, 500, -500)));
|
||||||
|
function_lookup.emplace(std::make_pair(5, function(&sine_envelope_sine_wave, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(6, function(&stretched_v_sine_wave, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(7, function(&ackleys_one, 32, -32)));
|
||||||
|
function_lookup.emplace(std::make_pair(8, function(&ackleys_two, 32, -32)));
|
||||||
|
function_lookup.emplace(std::make_pair(9, function(&egg_holder, 500, -500)));
|
||||||
|
function_lookup.emplace(std::make_pair(10, function(&rana, 500, -500)));
|
||||||
|
function_lookup.emplace(std::make_pair(11, function(&pathological, 100, -100)));
|
||||||
|
function_lookup.emplace(std::make_pair(12, function(&michalewicz, M_PI, 0)));
|
||||||
|
function_lookup.emplace(std::make_pair(13, function(&masters_cosine_wave, 30, -30)));
|
||||||
|
function_lookup.emplace(std::make_pair(14, function(&shekels_foxholes, 10, 0)));
|
||||||
|
|
||||||
|
function f;
|
||||||
|
int dimensionality = 0;
|
||||||
|
double seed = 0;
|
||||||
|
int search_function = 0;
|
||||||
|
|
||||||
|
// // Get the command line args
|
||||||
|
// if (argc == 1){
|
||||||
|
// char arg_str[200];
|
||||||
|
// std::cin.get(arg_str, 200);
|
||||||
|
// char t = ' ';
|
||||||
|
//
|
||||||
|
// f = function_lookup[atoi(strtok(arg_str, &t))];
|
||||||
|
// dimensionality = atoi(strtok(NULL, &t));
|
||||||
|
// seed = atoi(strtok(NULL, &t));
|
||||||
|
// search_function = atoi(strtok(NULL, &t));
|
||||||
|
//
|
||||||
|
// } else {
|
||||||
|
//
|
||||||
|
// f = function_lookup[atoi(args[1])];
|
||||||
|
// dimensionality = atoi(args[2]);
|
||||||
|
// seed = atoi(args[3]);
|
||||||
|
// search_function = atoi(args[4]);
|
||||||
|
// }
|
||||||
|
|
||||||
|
srand(time(nullptr));
|
||||||
|
f = function_lookup[12];
|
||||||
|
dimensionality = 20;
|
||||||
|
seed = rand();
|
||||||
|
search_function = 7;
|
||||||
|
|
||||||
|
// Set up the search functions
|
||||||
|
seedMT(seed);
|
||||||
|
random_walk random_walk_search(f);
|
||||||
|
local local_search(f);
|
||||||
|
iterative_local iterative_local_search(f);
|
||||||
|
differential_evolution differential_evolution_search(f);
|
||||||
|
genetic_algorithm genetic_algorithm_search(f);
|
||||||
|
particle_swarm particle_swarm_search(f);
|
||||||
|
soma soma_search(f);
|
||||||
|
firefly firefly_search(f);
|
||||||
|
harmony harmony_search(f);
|
||||||
|
|
||||||
|
// return the results of the search
|
||||||
|
if (search_function == 0)
|
||||||
|
std::cout << random_walk_search.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 1)
|
||||||
|
std::cout << local_search.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 2)
|
||||||
|
std::cout << iterative_local_search.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 3)
|
||||||
|
std::cout << differential_evolution_search.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 4)
|
||||||
|
std::cout << genetic_algorithm_search.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 5)
|
||||||
|
std::cout << particle_swarm_search.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 6)
|
||||||
|
std::cout << soma_search.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 7)
|
||||||
|
std::cout << firefly_search.search(1, dimensionality) << std::endl;
|
||||||
|
else if (search_function == 8)
|
||||||
|
std::cout << harmony_search.search(1, dimensionality) << std::endl;
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
@ -0,0 +1,145 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
struct particle {
|
||||||
|
|
||||||
|
// Personal best
|
||||||
|
double pb_fitness = 999999999999999;
|
||||||
|
std::vector<double> pb_solution;
|
||||||
|
|
||||||
|
// Current solution
|
||||||
|
double fitness = 9999999999;
|
||||||
|
std::vector<double> solution;
|
||||||
|
|
||||||
|
std::vector<double> velocity;
|
||||||
|
double v_max = 4.0;
|
||||||
|
|
||||||
|
double c1 = 0.2;
|
||||||
|
double c2 = 0.2;
|
||||||
|
double weight = 0.9;
|
||||||
|
|
||||||
|
int dimensionality;
|
||||||
|
|
||||||
|
std::vector<double> *gb_solution;
|
||||||
|
double *gb_fitness;
|
||||||
|
|
||||||
|
function *func;
|
||||||
|
|
||||||
|
particle(int dimensionality, std::vector<double> *gb_solution, double *gb_fitness, function *func) : dimensionality(dimensionality){
|
||||||
|
|
||||||
|
this->gb_solution = gb_solution;
|
||||||
|
this->gb_fitness = gb_fitness;
|
||||||
|
this->func = func;
|
||||||
|
|
||||||
|
// Blank initial solution and assign it also to pb
|
||||||
|
pb_solution, solution = std::vector<double>(dimensionality, 0);
|
||||||
|
|
||||||
|
// Init the velocity
|
||||||
|
for (int i = 0; i < dimensionality; i++)
|
||||||
|
velocity.push_back(rand_between(-v_max/3, v_max/3));
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
void update_fitness(){
|
||||||
|
|
||||||
|
fitness = func->compute(solution);
|
||||||
|
if (fitness < pb_fitness){
|
||||||
|
pb_solution = solution;
|
||||||
|
pb_fitness = fitness;
|
||||||
|
}
|
||||||
|
if (fitness < *gb_fitness){
|
||||||
|
*gb_solution = solution;
|
||||||
|
*gb_fitness = fitness;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void update_velocity(){
|
||||||
|
for (int d = 0; d < dimensionality; d++){
|
||||||
|
velocity.at(d) = weight * velocity.at(d) + c1 * rand_between(0, 1) * (pb_solution.at(d) - solution.at(d)) +
|
||||||
|
c2 * rand_between(0, 1) * (gb_solution->at(d) - solution.at(d));
|
||||||
|
if (velocity.at(d) > v_max)
|
||||||
|
velocity.at(d) = v_max;
|
||||||
|
else if (velocity.at(d) < -v_max)
|
||||||
|
velocity.at(d) = -v_max;
|
||||||
|
|
||||||
|
solution.at(d) += velocity.at(d);
|
||||||
|
|
||||||
|
if (solution.at(d) < func->lower_bound)
|
||||||
|
solution.at(d) = func->lower_bound;
|
||||||
|
else if (solution.at(d) > func->upper_bound)
|
||||||
|
solution.at(d) = func->upper_bound;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
class particle_swarm : public search_function {
|
||||||
|
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
particle_swarm(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
for (int i = 0; i < number_of_particles; i++){
|
||||||
|
|
||||||
|
particle p(dimensionality, &gb_solution, &gb_fitness, &func);
|
||||||
|
p.solution = generate_solution(dimensionality);
|
||||||
|
p.update_fitness();
|
||||||
|
|
||||||
|
particles.push_back(p);
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
for (int i = 0; i < max_iterations; i++){
|
||||||
|
|
||||||
|
for (int p = 0; p < particles.size(); p++){
|
||||||
|
particles.at(p).update_fitness();
|
||||||
|
particles.at(p).update_velocity();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return gb_fitness;
|
||||||
|
};
|
||||||
|
|
||||||
|
private:
|
||||||
|
|
||||||
|
// The global best solution and fitness
|
||||||
|
double gb_fitness = 99999999999999;
|
||||||
|
std::vector<double> gb_solution;
|
||||||
|
|
||||||
|
int number_of_particles = 100;
|
||||||
|
std::vector<particle> particles;
|
||||||
|
|
||||||
|
int max_iterations = 100;
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,41 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
#include <algorithm>
|
||||||
|
|
||||||
|
class random_walk : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
random_walk(function f) : search_function(f) {
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
timer t;
|
||||||
|
t.start();
|
||||||
|
|
||||||
|
std::vector<double> r;
|
||||||
|
for (int i = 0; i < permutations; i++){
|
||||||
|
|
||||||
|
std::vector<double> dimension_vals;
|
||||||
|
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
auto val = fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound;
|
||||||
|
dimension_vals.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
|
||||||
|
r.push_back(func.compute(dimension_vals));
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
t.end();
|
||||||
|
|
||||||
|
std::sort(r.begin(), r.end(), std::less<double>());
|
||||||
|
|
||||||
|
return r[0];
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
@ -0,0 +1,6 @@
|
|||||||
|
The optimization functions can be either ran with the python script "run.py"
|
||||||
|
which will run all 3 search methods with all 15 search functions.
|
||||||
|
|
||||||
|
Or you can call the executable directly from the command line. Eg.
|
||||||
|
|
||||||
|
./Lab4 <0-14> <dimensionality> <seed> <1-8>
|
||||||
@ -0,0 +1,6 @@
|
|||||||
|
Timer values
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,55 @@
|
|||||||
|
from subprocess import check_output
|
||||||
|
import subprocess
|
||||||
|
import random
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import time
|
||||||
|
|
||||||
|
random.seed()
|
||||||
|
|
||||||
|
loc = "../build/Lab4"
|
||||||
|
|
||||||
|
f = open('out','w')
|
||||||
|
f.write("Timer values")
|
||||||
|
|
||||||
|
for function in range(6, 9):
|
||||||
|
print("Method: " + str(function))
|
||||||
|
for q in range(0, 15):
|
||||||
|
print("Function: " + str(q))
|
||||||
|
f.write(str(q) + "\n")
|
||||||
|
start = time.time()
|
||||||
|
|
||||||
|
subprocess.call([
|
||||||
|
loc,
|
||||||
|
str(q),
|
||||||
|
str(20),
|
||||||
|
str(random.randint(0, 2147483646)),
|
||||||
|
str(function)
|
||||||
|
])
|
||||||
|
|
||||||
|
end = time.time()
|
||||||
|
f.write(str(end-start) + "\n")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
f.close()
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,39 @@
|
|||||||
|
from subprocess import check_output
|
||||||
|
import subprocess
|
||||||
|
import random
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import time
|
||||||
|
|
||||||
|
random.seed()
|
||||||
|
|
||||||
|
loc = "../build/Lab4"
|
||||||
|
|
||||||
|
search = 8
|
||||||
|
|
||||||
|
for function in range(0, 15):
|
||||||
|
|
||||||
|
print("Function: " + str(function))
|
||||||
|
for q in range(0, 10):
|
||||||
|
subprocess.call([
|
||||||
|
loc,
|
||||||
|
str(function),
|
||||||
|
str(20),
|
||||||
|
str(random.randint(0, 2147483646)),
|
||||||
|
str(search)
|
||||||
|
])
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
@ -0,0 +1,193 @@
|
|||||||
|
\documentclass[paper=a4, fontsize=11pt]{scrartcl}
|
||||||
|
\usepackage[T1]{fontenc}
|
||||||
|
\usepackage{fourier}
|
||||||
|
\usepackage[english]{babel}
|
||||||
|
\usepackage[protrusion=true,expansion=true]{microtype}
|
||||||
|
\usepackage{amsmath,amsfonts,amsthm}
|
||||||
|
\usepackage[pdftex]{graphicx}
|
||||||
|
\usepackage{url}
|
||||||
|
\usepackage{sectsty}
|
||||||
|
\usepackage{rotating}
|
||||||
|
\allsectionsfont{\centering \normalfont\scshape}
|
||||||
|
|
||||||
|
\usepackage{fancyhdr}
|
||||||
|
\pagestyle{fancyplain}
|
||||||
|
\fancyhead{}
|
||||||
|
\fancyfoot[L]{}
|
||||||
|
\fancyfoot[C]{}
|
||||||
|
\fancyfoot[R]{\thepage}
|
||||||
|
\renewcommand{\headrulewidth}{0pt}
|
||||||
|
\renewcommand{\footrulewidth}{0pt}
|
||||||
|
\setlength{\headheight}{13.6pt}
|
||||||
|
\numberwithin{equation}{section}
|
||||||
|
\numberwithin{figure}{section}
|
||||||
|
\numberwithin{table}{section}
|
||||||
|
\newcommand{\horrule}[1]{\rule{\linewidth}{#1}}
|
||||||
|
|
||||||
|
\title{
|
||||||
|
%\vspace{-1in}
|
||||||
|
\usefont{OT1}{bch}{b}{n}
|
||||||
|
\normalfont \normalsize \textsc{Central Washington University of the Computer Science Department} \\ [25pt]
|
||||||
|
\horrule{0.5pt} \\[0.4cm]
|
||||||
|
\huge Project 4 \\
|
||||||
|
\horrule{2pt} \\[0.5cm]
|
||||||
|
}
|
||||||
|
|
||||||
|
\author{\normalsize Mitchell Hansen \\[-6pt]}
|
||||||
|
|
||||||
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
|
||||||
|
\begin{document}
|
||||||
|
\maketitle
|
||||||
|
|
||||||
|
\section{Introduction}
|
||||||
|
For this lab we again took our 15 optimization functions and ran them through
|
||||||
|
3 new methods of determining the global minimum. The functions being:
|
||||||
|
The Self Organizing Migrating Algorithm (SOMA) which uses an evolutional approach
|
||||||
|
, The Firefly Algorithm (FA) which uses an evolutional swarm approach
|
||||||
|
similar to Particle Swarm, and the Harmony Search Algorithm (HS) which uses
|
||||||
|
another evolutional style approach.
|
||||||
|
|
||||||
|
\section{Methods}
|
||||||
|
For each of the 3 search methods and all 15 of the search functions we ran
|
||||||
|
tests for a set amount of iterations using the python scripts we wrote
|
||||||
|
for the previous lab. These results were then written out to a file from which
|
||||||
|
we calculated the min, max, range, etc.
|
||||||
|
|
||||||
|
We also slightly modified the way FA calculated the step when changin it's direction.
|
||||||
|
Instead of using a random distribution when calculating the FA random step
|
||||||
|
we used a suggested regular distribution that we found when researching the
|
||||||
|
problem online.
|
||||||
|
|
||||||
|
\section{Analysis}
|
||||||
|
This lab produced both surprising and disappointing results when evaluating the search functions.
|
||||||
|
The most surprising results are that of SOMA, where it regularly equaled or beat the most accurate
|
||||||
|
search function so far which has been Differential Evolution (DE). Of the 15 functions tested, SOMA
|
||||||
|
had better results than DE for 6 of the functions. The rest of the 9 functions tested were very close
|
||||||
|
to the results found by the DE search function.
|
||||||
|
|
||||||
|
The disappointing results produced by this lab lay in the other two functions tested. HS and FA produced
|
||||||
|
very similar results overall, most possibly because they are not all that much better than particle
|
||||||
|
swarm of which they are in the same class. Between the two search functions, there was very seldom
|
||||||
|
a difference of more than 20 to 30 percent in their fitnesses. We think it's best to compare these
|
||||||
|
two functions to their cousin Particle Swarm (PSO) as it is the closest related method.
|
||||||
|
|
||||||
|
In relation to PSO the functions did very similarly for the later functions (>4). The lower functions (<4)
|
||||||
|
all produced results which were much more inaccurate than traditional particle swarm. In regards to the
|
||||||
|
later functions, HS was only able to beat a PSO search method under function 6 with 11.28 determined as
|
||||||
|
the minimum, with PSO returning 12.15. FA wasn't able to beat PSO in function 6 but it did come close
|
||||||
|
with 13.32. Another notable point to look at is function 15 where HS was able to find the minimum at
|
||||||
|
-18.70, but FA was unable to capture the minimum with 16.40.
|
||||||
|
|
||||||
|
|
||||||
|
\section{Conclusion}
|
||||||
|
|
||||||
|
The conclusion for this lab is a pessimistic one, SOMA was a great success and a valuable
|
||||||
|
addition to our growing library of search functions. But are overshadowed by the poor performance
|
||||||
|
not only in the accuracy of answers, but also the run times of FA and HS. PSO was able to
|
||||||
|
outperform HS and FA in almost every aspect, and that is even telling as PSO performs
|
||||||
|
poorly when compared to other search functions we have used for some specific functions.
|
||||||
|
Walking away from this lab, I believe that we will add SOMA to our list of highly accurate
|
||||||
|
and performance search methods, while leaving FA and HS for problem sets which they are most
|
||||||
|
suited to.
|
||||||
|
|
||||||
|
Although we scrutinized our code to an appropriate degree we can't rule out the poor performance
|
||||||
|
of the FA and HS algorithms being caused by incorrect implementations. We're reasonably
|
||||||
|
sure that this is not the case though as the comparable performance to PSO was expected.
|
||||||
|
Additionally SOMA was not entirely without faults either. Function 10 for example shows an almost 250
|
||||||
|
percent increase in the minimum value over it's DE an PSO counterparts. This is worrisome as
|
||||||
|
all of the other results produced by SOMA were within 20 to 30 percent of PSO and DE.
|
||||||
|
|
||||||
|
|
||||||
|
\begin{figure}
|
||||||
|
\section{Results}
|
||||||
|
\caption{Computation comparison of SOMA, HS and FA}
|
||||||
|
\hskip+4.0cm
|
||||||
|
\rotatebox{90.0}{
|
||||||
|
\scalebox{0.7}{
|
||||||
|
\small \centering
|
||||||
|
|
||||||
|
\label{Tab1d}
|
||||||
|
\begin{tabular}{c|lllll|lllll|lllll}
|
||||||
|
\noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
Problem & \multicolumn{5}{c}{SOMA}
|
||||||
|
& \multicolumn{5}{|c|}{HS}
|
||||||
|
& \multicolumn{5}{c}{FA} \\
|
||||||
|
\noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
& Avg & Median & Range & SD & T(s) & Avg & Median & Range & SD & T(s) & Avg & Median & Range & SD & T(s) \\ \noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
$f_1$ & -7299.02 & -7386.83 & 1503.58 & 452.97 & 0.08 & -1736.64 & -1762.09 & 901.78 & 340.48 & 4.73 & -2078.10 & -2121.93 & 1297.24 & 956.34 & 2.00 \\
|
||||||
|
$f_2$ & 73.06 & 33.94 & 332.07 & 100.36 & 0.05 & 38678.19 & 39044.00 & 14740.90 & 5545.68 & 3.63 & 39611.35 & 41038.00 & 15876.60 & 8095.76 & 0.87 \\
|
||||||
|
$f_3$ & 149.76 & 119.57 & 338.84 & 99.33 & 0.10 & 15223266000.00 & 15499350000.00 & 9746940000.00 & 3077498860.01 & 3.87 & 13940456000.00 & 13970750000.00 & 11865890000.00 & 3765178464.71 & 1.06 \\
|
||||||
|
$f_4$ & -7758.64 & -7737.15 & 362.37 & 131.58 & 0.20 & 132487.90 & 138380.00 & 75080.00 & 22337.69 & 4.71 & 135166.40 & 136884.00 & 22462.00 & 30028.16 & 2.09 \\
|
||||||
|
$f_5$ & 45.26 & 35.50 & 124.00 & 36.68 & 0.07 & 249.51 & 249.76 & 66.18 & 22.99 & 4.75 & 219.01 & 229.32 & 130.89 & 46.52 & 2.18 \\
|
||||||
|
$f_6$ & 13.70 & 13.84 & 2.91 & 0.99 & 0.00 & 11.28 & 11.24 & 0.82 & 0.24 & 5.90 & 13.32 & 13.25 & 1.35 & 3.11 & 2.89 \\
|
||||||
|
$f_7$ & 36.00 & 34.30 & 24.57 & 8.03 & 0.04 & 34.10 & 34.92 & 5.20 & 1.82 & 6.54 & 45.15 & 44.93 & 8.66 & 9.88 & 4.59 \\
|
||||||
|
$f_8$ & 14.49 & 15.01 & 72.42 & 28.43 & 0.10 & 279.82 & 282.06 & 57.12 & 17.79 & 5.95 & 273.29 & 278.48 & 37.49 & 62.42 & 3.66 \\
|
||||||
|
$f_9$ & 141.72 & 212.47 & 376.52 & 154.36 & 0.09 & 305.15 & 306.60 & 19.46 & 6.85 & 7.10 & 342.88 & 354.12 & 71.60 & 74.94 & 4.89 \\
|
||||||
|
$f_{10}$ & -13566.43 & -14173.65 & 3948.20 & 1297.42 & 0.55 & -3194.74 & -2846.66 & 2290.95 & 832.19 & 6.07 & -3332.62 & -3378.12 & 2053.65 & 1555.81 & 3.43 \\
|
||||||
|
$f_{11}$ & -8428.54 & -8806.80 & 3527.50 & 1076.29 & 0.58 & -2037.88 & -1976.69 & 1404.16 & 421.07 & 8.39 & -2009.29 & -1998.01 & 1467.36 & 1004.39 & 6.10 \\
|
||||||
|
$f_{12}$ & 8.91 & 8.88 & 0.87 & 0.27 & 0.01 & 7.78 & 7.74 & 0.59 & 0.19 & 5.76 & 8.71 & 8.77 & 0.44 & 2.14 & 3.10 \\
|
||||||
|
$f_{13}$ & -2.36 & -2.38 & 2.90 & 0.81 & 0.01 & -5.46 & -5.55 & 1.53 & 0.48 & 7.33 & -2.53 & -2.52 & 2.33 & 1.43 & 4.73 \\
|
||||||
|
$f_{14}$ & -7.39 & -7.74 & 8.78 & 3.13 & 0.01 & -12.86 & -12.81 & 2.15 & 0.67 & 5.41 & -7.47 & -7.21 & 6.35 & 4.04 & 3.20 \\
|
||||||
|
$f_{15}$ & -17.46 & -18.45 & 6.22 & 2.14 & 0.03 & -18.70 & -18.70 & 0.00 & 0.00 & 11.09 & -16.40 & -17.37 & 6.14 & 6.23 & 9.65 \\ \noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
& & & & & & & & & & & & & & & \\
|
||||||
|
\noalign{\smallskip}\hline\noalign{\smallskip} \multicolumn{16}{l}{\tiny $^1$ ThinkPad, 3.4GHz Intel Core i7 (3rd gen), 16 GB RAM}
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}}
|
||||||
|
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\newpage
|
||||||
|
|
||||||
|
\begin{figure}
|
||||||
|
\section{Previous Results}
|
||||||
|
\caption{Computation comparison of DE, GA and PSO}
|
||||||
|
\hskip+4.0cm
|
||||||
|
\rotatebox{90.0}{
|
||||||
|
\scalebox{0.7}{
|
||||||
|
\small \centering
|
||||||
|
|
||||||
|
\label{Tab1d}
|
||||||
|
\begin{tabular}{c|lllll|lllll|lllll}
|
||||||
|
\noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
Problem & \multicolumn{5}{c}{DE}& \multicolumn{5}{|c|}{GA}
|
||||||
|
& \multicolumn{5}{c}{PSO} \\
|
||||||
|
\noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
& Avg & Median & Range & SD & T(s) & Avg & Median & Range & SD & T(s) & Avg & Median & Range & SD & T(s) \\ \noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
$f_1$ & -6112.33 & -6084.59 & 114.26 & 47.83 & 1.14 & -3276.12 & -3292.95 & 943.02 & 245.68 & 2.69 & -2871.98 & -2904.39 & 1194.77 & 322.06 & 0.12 \\
|
||||||
|
$f_2$ & 129.53 & 25.00 & 900.00 & 251.52 & 0.53 & 23185.53 & 22853.00 & 10310.00 & 3148.43 & 0.72 & 0.17 & 0.15 & 0.25 & 0.08 & 0.09 \\
|
||||||
|
$f_3$ & 26105.67 & 10019.00 & 168100.00 & 43662.88 & 0.78 & 5291234666.67 & 5017400000.00 & 5739020000.00 & 1539343402.74 & 0.68 & 421.98 & 200.19 & 1657.68 & 497.31 & 0.10 \\
|
||||||
|
$f_4$ & -7600.00 & -7960.00 & 2560.00 & 728.99 & 1.00 & 79752.00 & 81520.00 & 23240.00 & 8507.40 & 2.12 & -5206.62 & -5324.98 & 3479.78 & 1178.83 & 0.13 \\
|
||||||
|
$f_5$ & 0.00 & 0.00 & 0.00 & 0.00 & 1.08 & 145.86 & 150.55 & 51.89 & 17.68 & 2.31 & 9.17 & 8.93 & 5.88 & 1.95 & 0.13 \\
|
||||||
|
$f_6$ & 12.38 & 12.71 & 2.19 & 0.60 & 1.46 & 12.04 & 11.97 & 0.67 & 0.22 & 2.52 & 12.15 & 12.18 & 1.25 & 0.33 & 0.14 \\
|
||||||
|
$f_7$ & 19.06 & 19.01 & 0.62 & 0.16 & 1.67 & 36.69 & 36.60 & 5.76 & 1.54 & 4.20 & 20.55 & 20.45 & 2.63 & 0.68 & 0.18 \\
|
||||||
|
$f_8$ & 58.74 & 58.73 & 4.74 & 1.54 & 1.60 & 212.86 & 213.95 & 41.20 & 11.06 & 3.41 & -9.92 & -11.64 & 35.51 & 9.72 & 0.10 \\
|
||||||
|
$f_9$ & -83.30 & -80.69 & 21.87 & 6.99 & 2.09 & 276.38 & 276.83 & 14.65 & 4.35 & 4.10 & 251.53 & 288.37 & 173.05 & 64.83 & 0.14 \\
|
||||||
|
$f_{10}$ & -4959.12 & -4579.12 & 2896.23 & 966.10 & 3.02 & -4778.37 & -4822.17 & 978.82 & 327.79 & 4.72 & -4107.05 & -3830.50 & 2663.98 & 711.61 & 0.13 \\
|
||||||
|
$f_{11}$ & -8478.48 & -8821.20 & 5161.40 & 1330.20 & 3.56 & -3188.30 & -3181.83 & 1334.30 & 339.30 & 8.34 & -2899.33 & -2888.72 & 901.67 & 227.81 & 0.21 \\
|
||||||
|
$f_{12}$ & 0.00 & 0.00 & 0.00 & 0.00 & 1.48 & 8.00 & 8.01 & 0.69 & 0.17 & 2.70 & 7.02 & 7.08 & 1.30 & 0.37 & 0.15 \\
|
||||||
|
$f_{13}$ & -4.28 & -4.22 & 2.71 & 0.83 & 3.06 & -4.27 & -4.22 & 2.30 & 0.57 & 5.54 & -10.39 & -9.86 & 4.92 & 1.50 & 0.14 \\
|
||||||
|
$f_{14}$ & -18.99 & -19.00 & 0.04 & 0.01 & 1.47 & -10.88 & -10.53 & 3.70 & 1.00 & 3.65 & -16.07 & -16.15 & 5.22 & 1.59 & 0.14 \\
|
||||||
|
$f_{15}$ & -21.91 & -23.03 & 8.39 & 2.95 & 6.05 & -14.64 & -14.64 & 0.00 & 0.00 & 12.55 & -18.70 & -18.70 & 0.00 & 0.00 & 0.27 \\ \noalign{\smallskip}\hline\noalign{\smallskip}
|
||||||
|
& & & & & & & & & & & & & & & \\
|
||||||
|
\noalign{\smallskip}\hline\noalign{\smallskip} \multicolumn{16}{l}{\tiny $^1$ ThinkPad, 3.4GHz Intel Core i7 (3rd gen), 16 GB RAM}
|
||||||
|
|
||||||
|
\end{tabular}
|
||||||
|
}}
|
||||||
|
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\end{document}
|
||||||
|
|
||||||
@ -0,0 +1,56 @@
|
|||||||
|
#pragma once
|
||||||
|
|
||||||
|
class search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
function func;
|
||||||
|
|
||||||
|
search_function(function f) : func(f) {
|
||||||
|
};
|
||||||
|
|
||||||
|
virtual double search(int permutations, int dimensionality) = 0;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
|
||||||
|
std::vector<double> generate_solution(int dimensionality){
|
||||||
|
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
return tmp;
|
||||||
|
}
|
||||||
|
|
||||||
|
std::vector<std::vector<double>> generate_population(int dimensionality, int population_count){
|
||||||
|
|
||||||
|
std::vector<std::vector<double>> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++) {
|
||||||
|
tmp.push_back(generate_solution(dimensionality));
|
||||||
|
}
|
||||||
|
return tmp;
|
||||||
|
}
|
||||||
|
|
||||||
|
double check_bounds(double input){
|
||||||
|
if (input > func.upper_bound)
|
||||||
|
return func.upper_bound;
|
||||||
|
else if (input < func.lower_bound)
|
||||||
|
return func.lower_bound;
|
||||||
|
else
|
||||||
|
return input;
|
||||||
|
}
|
||||||
|
|
||||||
|
void check_solution_bounds(std::vector<double> *input){
|
||||||
|
|
||||||
|
for (auto &i: *input){
|
||||||
|
if (i > func.upper_bound)
|
||||||
|
i = func.upper_bound;
|
||||||
|
else if (i < func.lower_bound)
|
||||||
|
i = func.lower_bound;
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
};
|
||||||
@ -0,0 +1,149 @@
|
|||||||
|
#pragma once
|
||||||
|
#include "search_function.h"
|
||||||
|
|
||||||
|
class soma : public search_function {
|
||||||
|
|
||||||
|
public:
|
||||||
|
|
||||||
|
soma(function f) : search_function(f) {};
|
||||||
|
|
||||||
|
double search(int permutations, int dimensionality) {
|
||||||
|
|
||||||
|
// Set the parameters defined on page 8
|
||||||
|
path_length = 3.5;
|
||||||
|
specimen_step = 0.11;
|
||||||
|
perturbation = 0.11;
|
||||||
|
migrations = 1000;
|
||||||
|
min_div = 0.1;
|
||||||
|
|
||||||
|
// Set the population size
|
||||||
|
population_size = dimensionality * 0.5;
|
||||||
|
if (population_size < 10)
|
||||||
|
population_size = 10;
|
||||||
|
|
||||||
|
// Set up random start population
|
||||||
|
for (int p = 0; p < population_size; p++) {
|
||||||
|
std::vector<double> tmp;
|
||||||
|
for (int i = 0; i < dimensionality; i++){
|
||||||
|
tmp.push_back(fmod(randomMT(), (func.upper_bound * 2)) + func.lower_bound);
|
||||||
|
}
|
||||||
|
population.push_back(tmp);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Sort the population so the leader is at(0)
|
||||||
|
std::sort(population.begin(), population.end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
|
||||||
|
for (int m = 0; m < migrations; m++){
|
||||||
|
|
||||||
|
std::vector<double> leader_solution = population.at(0);
|
||||||
|
double leader_fitness = func.compute(leader_solution);
|
||||||
|
|
||||||
|
for (int i = 1; i < population.size(); i++){
|
||||||
|
|
||||||
|
// We need the best fitness and the starting point for each
|
||||||
|
// individual in the population. start_solution is not mutated,
|
||||||
|
// while best_* and population.at(i) both are
|
||||||
|
double best_fitness = func.compute(population.at(i));
|
||||||
|
std::vector<double> best_solution = population.at(i);
|
||||||
|
std::vector<double> start_solution = population.at(i);
|
||||||
|
|
||||||
|
for (double p = 0; p < path_length; p += specimen_step){
|
||||||
|
|
||||||
|
// Generate the perturbation vector for each step,
|
||||||
|
// seems to give better results than per migration
|
||||||
|
std::vector<double> perturbation_vector(dimensionality, 0);
|
||||||
|
for (auto &q: perturbation_vector){
|
||||||
|
double val = rand_between(0, 1);
|
||||||
|
if (val < perturbation)
|
||||||
|
q = 1;
|
||||||
|
else
|
||||||
|
q = 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Mutate the individual
|
||||||
|
for (int j = 0; j < dimensionality; j++){
|
||||||
|
population.at(i).at(j) = start_solution.at(j) + (leader_solution.at(j) - start_solution.at(j)) * p * perturbation_vector.at(j);
|
||||||
|
}
|
||||||
|
|
||||||
|
check_solution_bounds(&population.at(i));
|
||||||
|
|
||||||
|
|
||||||
|
// If this step beat the individuals best, update it
|
||||||
|
if (func.compute(population.at(i)) < best_fitness){
|
||||||
|
best_fitness = func.compute(population.at(i));
|
||||||
|
best_solution = population.at(i);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// population.at(i) is now at the end of the step.
|
||||||
|
// set it to the best solution it found along the way
|
||||||
|
population.at(i) = best_solution;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
// Early exit if the different between the leader and any others are less than
|
||||||
|
// a defined constant. 1 is a little to lenient.
|
||||||
|
for (int r = 1; r < population.size(); r++){
|
||||||
|
if (std::abs(func.compute(population.at(r)) - leader_fitness) < min_div){
|
||||||
|
return leader_fitness;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Resort the population so the leader will be at(0)
|
||||||
|
std::sort(population.begin(), population.end(), [this](std::vector<double> a, std::vector<double> b){
|
||||||
|
return this->func.compute(a) < this->func.compute(b);
|
||||||
|
});
|
||||||
|
|
||||||
|
// Test the front position. If the leader was usurped then replace it.
|
||||||
|
if (func.compute(population.front()) < leader_fitness) {
|
||||||
|
leader_solution = population.front();
|
||||||
|
leader_fitness = func.compute(leader_solution);
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
// Return the best solution found
|
||||||
|
return func.compute(population.front());
|
||||||
|
};
|
||||||
|
|
||||||
|
private:
|
||||||
|
|
||||||
|
double specimen_step;
|
||||||
|
double path_length;
|
||||||
|
int population_size;
|
||||||
|
double perturbation;
|
||||||
|
double min_div;
|
||||||
|
int migrations;
|
||||||
|
|
||||||
|
std::vector<std::vector<double>> population;
|
||||||
|
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,181 @@
|
|||||||
|
// http://www.math.keio.ac.jp/~matumoto/ver980409.html
|
||||||
|
|
||||||
|
// This is the ``Mersenne Twister'' random number generator MT19937, which
|
||||||
|
// generates pseudorandom integers uniformly distributed in 0..(2^32 - 1)
|
||||||
|
// starting from any odd seed in 0..(2^32 - 1). This version is a recode
|
||||||
|
// by Shawn Cokus (Cokus@math.washington.edu) on March 8, 1998 of a version by
|
||||||
|
// Takuji Nishimura (who had suggestions from Topher Cooper and Marc Rieffel in
|
||||||
|
// July-August 1997).
|
||||||
|
//
|
||||||
|
// Effectiveness of the recoding (on Goedel2.math.washington.edu, a DEC Alpha
|
||||||
|
// running OSF/1) using GCC -O3 as a compiler: before recoding: 51.6 sec. to
|
||||||
|
// generate 300 million random numbers; after recoding: 24.0 sec. for the same
|
||||||
|
// (i.e., 46.5% of original time), so speed is now about 12.5 million random
|
||||||
|
// number generations per second on this machine.
|
||||||
|
//
|
||||||
|
// According to the URL <http://www.math.keio.ac.jp/~matumoto/emt.html>
|
||||||
|
// (and paraphrasing a bit in places), the Mersenne Twister is ``designed
|
||||||
|
// with consideration of the flaws of various existing generators,'' has
|
||||||
|
// a period of 2^19937 - 1, gives a sequence that is 623-dimensionally
|
||||||
|
// equidistributed, and ``has passed many stringent tests, including the
|
||||||
|
// die-hard test of G. Marsaglia and the load test of P. Hellekalek and
|
||||||
|
// S. Wegenkittl.'' It is efficient in memory usage (typically using 2506
|
||||||
|
// to 5012 bytes of static data, depending on data type sizes, and the code
|
||||||
|
// is quite short as well). It generates random numbers in batches of 624
|
||||||
|
// at a time, so the caching and pipelining of modern systems is exploited.
|
||||||
|
// It is also divide- and mod-free.
|
||||||
|
//
|
||||||
|
// This library is free software; you can redistribute it and/or modify it
|
||||||
|
// under the terms of the GNU Library General Public License as published by
|
||||||
|
// the Free Software Foundation (either version 2 of the License or, at your
|
||||||
|
// option, any later version). This library is distributed in the hope that
|
||||||
|
// it will be useful, but WITHOUT ANY WARRANTY, without even the implied
|
||||||
|
// warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See
|
||||||
|
// the GNU Library General Public License for more details. You should have
|
||||||
|
// received a copy of the GNU Library General Public License along with this
|
||||||
|
// library; if not, write to the Free Software Foundation, Inc., 59 Temple
|
||||||
|
// Place, Suite 330, Boston, MA 02111-1307, USA.
|
||||||
|
//
|
||||||
|
// The code as Shawn received it included the following notice:
|
||||||
|
//
|
||||||
|
// Copyright (C) 1997 Makoto Matsumoto and Takuji Nishimura. When
|
||||||
|
// you use this, send an e-mail to <matumoto@math.keio.ac.jp> with
|
||||||
|
// an appropriate reference to your work.
|
||||||
|
//
|
||||||
|
// It would be nice to CC: <Cokus@math.washington.edu> when you write.
|
||||||
|
//
|
||||||
|
|
||||||
|
#include <stdio.h>
|
||||||
|
#include <stdlib.h>
|
||||||
|
|
||||||
|
//
|
||||||
|
// uint32 must be an unsigned integer type capable of holding at least 32
|
||||||
|
// bits; exactly 32 should be fastest, but 64 is better on an Alpha with
|
||||||
|
// GCC at -O3 optimization so try your options and see what's best for you
|
||||||
|
//
|
||||||
|
|
||||||
|
typedef unsigned long uint32;
|
||||||
|
|
||||||
|
#define N (624) // length of state vector
|
||||||
|
#define M (397) // a period parameter
|
||||||
|
#define K (0x9908B0DFU) // a magic constant
|
||||||
|
#define hiBit(u) ((u) & 0x80000000U) // mask all but highest bit of u
|
||||||
|
#define loBit(u) ((u) & 0x00000001U) // mask all but lowest bit of u
|
||||||
|
#define loBits(u) ((u) & 0x7FFFFFFFU) // mask the highest bit of u
|
||||||
|
#define mixBits(u, v) (hiBit(u)|loBits(v)) // move hi bit of u to hi bit of v
|
||||||
|
|
||||||
|
static uint32 state[N+1]; // state vector + 1 extra to not violate ANSI C
|
||||||
|
static uint32 *next; // next random value is computed from here
|
||||||
|
static int left = -1; // can *next++ this many times before reloading
|
||||||
|
|
||||||
|
void seedMT(uint32 seed)
|
||||||
|
{
|
||||||
|
//
|
||||||
|
// We initialize state[0..(N-1)] via the generator
|
||||||
|
//
|
||||||
|
// x_new = (69069 * x_old) mod 2^32
|
||||||
|
//
|
||||||
|
// from Line 15 of Table 1, p. 106, Sec. 3.3.4 of Knuth's
|
||||||
|
// _The Art of Computer Programming_, Volume 2, 3rd ed.
|
||||||
|
//
|
||||||
|
// Notes (SJC): I do not know what the initial state requirements
|
||||||
|
// of the Mersenne Twister are, but it seems this seeding generator
|
||||||
|
// could be better. It achieves the maximum period for its modulus
|
||||||
|
// (2^30) iff x_initial is odd (p. 20-21, Sec. 3.2.1.2, Knuth); if
|
||||||
|
// x_initial can be even, you have sequences like 0, 0, 0, ...;
|
||||||
|
// 2^31, 2^31, 2^31, ...; 2^30, 2^30, 2^30, ...; 2^29, 2^29 + 2^31,
|
||||||
|
// 2^29, 2^29 + 2^31, ..., etc. so I force seed to be odd below.
|
||||||
|
//
|
||||||
|
// Even if x_initial is odd, if x_initial is 1 mod 4 then
|
||||||
|
//
|
||||||
|
// the lowest bit of x is always 1,
|
||||||
|
// the next-to-lowest bit of x is always 0,
|
||||||
|
// the 2nd-from-lowest bit of x alternates ... 0 1 0 1 0 1 0 1 ... ,
|
||||||
|
// the 3rd-from-lowest bit of x 4-cycles ... 0 1 1 0 0 1 1 0 ... ,
|
||||||
|
// the 4th-from-lowest bit of x has the 8-cycle ... 0 0 0 1 1 1 1 0 ... ,
|
||||||
|
// ...
|
||||||
|
//
|
||||||
|
// and if x_initial is 3 mod 4 then
|
||||||
|
//
|
||||||
|
// the lowest bit of x is always 1,
|
||||||
|
// the next-to-lowest bit of x is always 1,
|
||||||
|
// the 2nd-from-lowest bit of x alternates ... 0 1 0 1 0 1 0 1 ... ,
|
||||||
|
// the 3rd-from-lowest bit of x 4-cycles ... 0 0 1 1 0 0 1 1 ... ,
|
||||||
|
// the 4th-from-lowest bit of x has the 8-cycle ... 0 0 1 1 1 1 0 0 ... ,
|
||||||
|
// ...
|
||||||
|
//
|
||||||
|
// The generator's potency (min. s>=0 with (69069-1)^s = 0 mod 2^32) is
|
||||||
|
// 16, which seems to be alright by p. 25, Sec. 3.2.1.3 of Knuth. It
|
||||||
|
// also does well in the dimension 2..5 spectral tests, but it could be
|
||||||
|
// better in dimension 6 (Line 15, Table 1, p. 106, Sec. 3.3.4, Knuth).
|
||||||
|
//
|
||||||
|
// Note that the random number user does not see the values generated
|
||||||
|
// here directly since reloadMT() will always munge them first, so maybe
|
||||||
|
// none of all of this matters. In fact, the seed values made here could
|
||||||
|
// even be extra-special desirable if the Mersenne Twister theory says
|
||||||
|
// so-- that's why the only change I made is to restrict to odd seeds.
|
||||||
|
//
|
||||||
|
|
||||||
|
register uint32 x = (seed | 1U) & 0xFFFFFFFFU, *s = state;
|
||||||
|
register int j;
|
||||||
|
|
||||||
|
for(left=0, *s++=x, j=N; --j;
|
||||||
|
*s++ = (x*=69069U) & 0xFFFFFFFFU);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
uint32 reloadMT(void)
|
||||||
|
{
|
||||||
|
register uint32 *p0=state, *p2=state+2, *pM=state+M, s0, s1;
|
||||||
|
register int j;
|
||||||
|
|
||||||
|
if(left < -1)
|
||||||
|
seedMT(4357U);
|
||||||
|
|
||||||
|
left=N-1, next=state+1;
|
||||||
|
|
||||||
|
for(s0=state[0], s1=state[1], j=N-M+1; --j; s0=s1, s1=*p2++)
|
||||||
|
*p0++ = *pM++ ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
|
||||||
|
|
||||||
|
for(pM=state, j=M; --j; s0=s1, s1=*p2++)
|
||||||
|
*p0++ = *pM++ ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
|
||||||
|
|
||||||
|
s1=state[0], *p0 = *pM ^ (mixBits(s0, s1) >> 1) ^ (loBit(s1) ? K : 0U);
|
||||||
|
s1 ^= (s1 >> 11);
|
||||||
|
s1 ^= (s1 << 7) & 0x9D2C5680U;
|
||||||
|
s1 ^= (s1 << 15) & 0xEFC60000U;
|
||||||
|
return(s1 ^ (s1 >> 18));
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
uint32 randomMT(void)
|
||||||
|
{
|
||||||
|
uint32 y;
|
||||||
|
|
||||||
|
if(--left < 0)
|
||||||
|
return(reloadMT());
|
||||||
|
|
||||||
|
y = *next++;
|
||||||
|
y ^= (y >> 11);
|
||||||
|
y ^= (y << 7) & 0x9D2C5680U;
|
||||||
|
y ^= (y << 15) & 0xEFC60000U;
|
||||||
|
return(y ^ (y >> 18));
|
||||||
|
}
|
||||||
|
|
||||||
|
#ifdef NOCOMPILE
|
||||||
|
int main(void)
|
||||||
|
{
|
||||||
|
int j;
|
||||||
|
|
||||||
|
// you can seed with any uint32, but the best are odds in 0..(2^32 - 1)
|
||||||
|
|
||||||
|
seedMT(4357U);
|
||||||
|
|
||||||
|
// print the first 2,002 random numbers seven to a line as an example
|
||||||
|
|
||||||
|
for(j=0; j<2002; j++)
|
||||||
|
printf(" %10lu%s", (unsigned long) randomMT(), (j%7)==6 ? "\n" : "");
|
||||||
|
|
||||||
|
return(EXIT_SUCCESS);
|
||||||
|
}
|
||||||
|
#endif
|
||||||
@ -0,0 +1,12 @@
|
|||||||
|
|
||||||
|
struct timer{
|
||||||
|
std::chrono::high_resolution_clock::time_point t1;
|
||||||
|
std::chrono::high_resolution_clock::time_point t2;
|
||||||
|
void start(){t1 = std::chrono::high_resolution_clock::now();}
|
||||||
|
void end(){t2 = std::chrono::high_resolution_clock::now();}
|
||||||
|
double duration(){ return std::chrono::duration_cast<std::chrono::microseconds>( t2 - t1 ).count();}
|
||||||
|
};
|
||||||
|
|
||||||
|
double rand_between(double lower, double upper){
|
||||||
|
return lower + (randomMT() * 1.0 / UINT32_MAX) * (upper - lower);
|
||||||
|
}
|
||||||
Loading…
Reference in new issue