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分解策略型多目标进化算法中的自适应机制研究.docx

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分解策略型多目标进化算法中的自适应机制研究 Title:ResearchontheAdaptiveMechanisminDecomposition-basedMulti-objectiveEvolutionaryAlgorithms Abstract: Decomposition-basedmulti-objectiveevolutionaryalgorithms(DMOEA)havegainedsignificantattentioninsolvingcomplexoptimizationproblemswithmultipleconflictingobjectives.ThesealgorithmsefficientlyexploretheParetofrontbydecomposingtheproblemintoasetofsubproblemsusingaweightvector.However,theperformanceofDMOEAsheavilyreliesonthechoiceofweightvectors,whichbecomeschallengingwhenfacedwithproblemswithdynamicorunknownParetofronts.Toaddressthischallenge,thispaperfocusesontheresearchandanalysisofadaptivemechanismsinDMOEAs,aimingtoimprovethealgorithms'convergenceanddiversityinvaryingenvironments.Variousadaptivestrategies,suchasadaptiveweightvectoradjustmentsandenvironmentalselectiontechniques,areinvestigatedanddiscussed.TheexperimentalresultsdemonstratetheeffectivenessoftheproposedadaptivemechanismsinenhancingtherobustnessandefficiencyofDMOEAs. 1.Introduction: 1.1BackgroundandSignificance: Multi-objectiveoptimizationproblems(MOPs)involveoptimizingmultipleconflictingobjectives,andoftenlackasinglegloballyoptimalsolution.DMOEAshaveemergedandproventobeeffectiveinsolvingMOPsbydecomposingtheproblemintoasetofsubproblems.However,thesealgorithmsoftenrequiremanualadjustmentofweightvectorstoachievegoodperformance. 1.2ResearchObjectives: TheobjectiveofthisresearchistoinvestigateanddevelopadaptivemechanismswithinDMOEAstoaddressthechallengesofstaticanddynamicParetofronts,andimprovetheconvergenceanddiversityofthealgorithms.Thefocusisonexploringadaptiveweightvectoradjustmentandenvironmentalselectiontechniquestoimprovethealgorithms'performance. 2.RelatedWork: Thissectionprovidesanoverviewofstate-of-the-artDMOEAsandhighlightsthechallengesarisingfromstaticanddynamicParetofronts.Additionally,itdiscussesexistingadaptivemechanismsandtheirlimitations. 3.AdaptiveWeightVectorAdjustment: 3.1StaticWeightVectorAdaptation: Thissubsectionexploresvariousapproachestoadaptstaticweightvector