混合离散变量优化设计的复合遗传算法-豆柴文库

您所在位置：网站首页 / 混合离散变量优化设计的复合遗传算法.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

混合离散变量优化设计的复合遗传算法
Title:HybridGeneticAlgorithmforOptimizationDesignofMixedDiscreteVariables
Abstract:
Inthefieldofengineeringdesignandoptimization,thereareoftenproblemsthatinvolvemixeddiscretevariables.Theseproblemsrequiremakingdecisionsonbothcontinuousanddiscretevariablessimultaneously,whichaddscomplexitytotheoptimizationprocess.Itiscrucialtodevelopefficientoptimizationalgorithmstosolvesuchproblemsefficiently.Thispaperproposesahybridgeneticalgorithmfortheoptimizationdesignofmixeddiscretevariables.Thehybridalgorithmcombinesthestrengthsofgeneticalgorithmsandlocalsearchtechniquestoenhancetheconvergencespeedandsolutionquality.Thealgorithmisevaluatedonasetofbenchmarkproblems,andtheresultsdemonstrateitseffectivenessandsuperioritycomparedtotraditionaloptimizationapproaches.
1.Introduction
Inengineeringdesign,optimizationproblemsofteninvolvedecisionvariablesofmixedtypes,wheresomevariablescanonlytakealimitednumberofdiscretevalues,whileothersarecontinuous.Traditionaloptimizationtechniques,suchasgradient-basedorheuristicsearchmethods,lacktheabilitytoeffectivelyhandlemixeddiscretevariables.Geneticalgorithms(GAs)havebeenwidelyusedfordiscreteoptimizationproblems,buttheycanbecomputationallyexpensiveandhavedifficultyconvergingtotheglobaloptimum.ThispaperproposesahybridgeneticalgorithmthatcombinesthestrengthsofGAsandlocalsearchtechniquestoefficientlysolveoptimizationproblemswithmixeddiscretevariables.
2.LiteratureReview
Theoptimizationofmixeddiscretevariableshasbeenstudiedextensivelyinvariousfields.Differentapproaches,suchasmathematicalprogramming,evolutionaryalgorithms,andheuristics,havebeenproposed.Geneticalgorithmshaveshownpromiseinsolvingsuchproblems,buttheirefficiencyandconvergencespeedcanbeimproved.Localsearchtechniques,suchashillclimbingandsimulatedannealing,havebeenappliedtoenhancethequalityofsolutionsobtainedfromgeneticalgorithms.Thesetechniquesaimtoexploitlocalknowledgetoguidethesearchtowardsbettersolutions.Inthispaper,ahybridgeneticalgorithmthatcombinesgeneticoperatorsan