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一种基于新型差分进化模型的MOEAD改进算法.docx

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一种基于新型差分进化模型的MOEAD改进算法 Title:AnImprovedMOEA/DAlgorithmbasedonaNovelDifferentialEvolutionModel Abstract: Multi-ObjectiveEvolutionaryAlgorithms(MOEAs)haveshownremarkableeffectivenessinsolvingmulti-objectiveoptimizationproblems(MOPs).AmongvariousMOEAs,MOEA/D(Multi-ObjectiveEvolutionaryAlgorithmbasedonDecomposition)isawell-knownandwidelyusedalgorithmduetoitsabilitytohandleMOPsefficiently.However,MOEA/Dsuffersfromsomelimitations,suchasslowconvergenceandalackofdiversitymaintenance.Toaddresstheseissues,thispaperproposesanimprovedMOEA/DalgorithmbasedonanovelDifferentialEvolution(DE)model.Theproposedalgorithmenhancesthesearchcapability,convergencerate,anddiversitymaintenanceofMOEA/D. 1.Introduction Multi-objectiveoptimizationproblemscommonlyexistinvariousfields,wheremultipleconflictingobjectivesneedtobeoptimizedsimultaneously.MOEAshaveemergedaseffectiveapproachestosolveMOPsbyprovidingasetofsolutionsthatrepresentsthetrade-offsbetweendifferentobjectives.MOEA/D,apopularMOEA,decomposesaMOPintoseveralscalaroptimizationsubproblems,whicharesolvedsimultaneouslyandcollaborativelybyapopulationofsolutions.However,theoriginalMOEA/Dalgorithmshowssomelimitations,includingslowconvergenceandalackofdiversitymaintenance. 2.ImprovedMOEA/DAlgorithmDesign TheproposedimprovedMOEA/DalgorithmbuildsupontheoriginalalgorithmandincorporatesanovelDifferentialEvolutionmodeltoaddressthelimitations.Thekeymodificationsinthealgorithmdesignareasfollows: 2.1InclusionofaNovelDifferentialEvolutionModel TheDEmodelisknownforitspowerfulexplorationandexploitationcapabilities.ByintegratingDEintotheframeworkofMOEA/D,thealgorithmcanbenefitfromtheenhancedsearchabilityandconvergencerateofDE. 2.2AdaptiveControlParameters Toimprovetheefficiencyandeffectivenessoftheproposedalgorithm,adaptivecontrolparametersareintroduced.Theseparametersdynamicallyadjustthemutationandcrossoverprobabilitiesduringtheevolutionprocess,leadingtoabetterbalancebetweenexplorationandexploitation. 2.3EnhancedDiversityMaintenance DiversityiscrucialinMO