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基于演化博弈论的协同进化算法的研究和应用的中期报告.docx

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基于演化博弈论的协同进化算法的研究和应用的中期报告 [Abstract] Cooperativecoevolutionaryalgorithms(CCEAs)havebeenwidelyusedinsolvingcomplexoptimizationproblems.However,mostexistingCCEAsarebasedonheuristics,lackingtheoreticalanalysisandrigorousproof.Evolutionarygametheory(EGT)providesamathematicalframeworkforstudyingtheevolutionofstrategiesinapopulationofindividuals.Inthisreport,weproposeanewapproachthatcombinesEGTandCCEAs,calledevolutionarygame-basedCCEA(EG-CCEA).WepresentthebasicconceptsandtheoreticalfoundationofEG-CCEA,anddemonstrateitsapplicationinsolvingabenchmarkoptimizationproblem. [Introduction] Cooperativecoevolutionaryalgorithms(CCEAs)havebeenwidelyusedinsolvingcomplexoptimizationproblems,suchasdesignoptimization,functionoptimization,andparametercalibration.ACCEAdecomposesacomplexproblemintosub-problems,whicharesolvedbydifferentsub-populationsofindividuals.Thesub-solutionsarethencombinedtoobtainthefinaloptimalsolution.However,mostexistingCCEAsarebasedonheuristics,lackingtheoreticalanalysisandrigorousproof.Therefore,itisdifficulttounderstandthebehaviorofCCEAsandtooptimizetheirparameters. Evolutionarygametheory(EGT)providesamathematicalframeworkforstudyingtheevolutionofstrategiesinapopulationofindividuals.EGThasbeensuccessfullyappliedinvariousfields,suchasbiology,economics,andsocialsciences.InEGT,thefitnessofastrategydependsonthestrategiesofotherindividualsinthepopulation,andthebeststrategyistheonethatachievesthehighestfitnessinthepopulation.EGTcanbeusedtoanalyzetheevolutionofcooperation,competition,andcoordinationinapopulationofindividuals. Inthisreport,weproposeanewapproachthatcombinesEGTandCCEAs,calledevolutionarygame-basedCCEA(EG-CCEA).EG-CCEAusesEGTtoguidetheevolutionofsub-populationsinCCEA,andcoordinatesthesub-solutionsbasedonthegame-theoreticalanalysis.WepresentthebasicconceptsandtheoreticalfoundationofEG-CCEA,anddemonstrateitsapplicationinsolvingabenchmarkoptimizationproblem. [Methodology] TheproposedEG-CCEAconsistsofthefollowingsteps: 1.Decomposetheoriginalproblemintosub-prob