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一种基于分解和协同的高维多目标进化算法 Title:ADecompositionandCooperative-basedHigh-dimensionalMulti-ObjectiveEvolutionaryAlgorithm Abstract: Multi-objectiveoptimizationproblemswithhigh-dimensionaldecisionspacespresentsignificantchallengesfortraditionaloptimizationalgorithms.Thispaperproposesanovelapproach,namedtheDecompositionandCooperative-basedHigh-dimensionalMulti-ObjectiveEvolutionaryAlgorithm(DC-HMOEA),totacklethesechallenges.DC-HMOEAdecomposesthehigh-dimensionalmulti-objectiveoptimizationproblemsintolower-dimensionalsubproblemsandemploysacooperativemechanismtofacilitatetheexplorationandexploitationofthesearchspace.TheperformanceofDC-HMOEAisvalidatedthroughexperimentsonbenchmarkproblems,demonstratingitseffectivenessinsolvinghigh-dimensionalmulti-objectiveoptimizationproblems. 1.Introduction Multi-objectiveoptimizationproblemsinvolvemultipleconflictingobjectivesthatmustbeoptimizedsimultaneously.High-dimensionalmulti-objectiveoptimizationproblemsfurthercomplicatetheoptimizationprocessduetothecurseofdimensionality.ThispaperpresentstheDC-HMOEA,anovelalgorithmthatcombinesdecompositionandcooperationtoaddressthechallengesassociatedwithhigh-dimensionalmulti-objectiveoptimization. 2.RelatedWork Thissectionprovidesanoverviewofexistingevolutionaryalgorithmsformulti-objectiveoptimizationandhighlightstheirlimitationsinsolvinghigh-dimensionalproblems.Itdiscussestheadvantagesofdecompositionandcooperativestrategiesandtheirpotentialintegrationintoaneffectivealgorithm. 3.AlgorithmDescription ThissectionprovidesadetaileddescriptionoftheDC-HMOEA.Firstly,thedecompositionprocessisexplained,wherethehigh-dimensionalproblemisdecomposedintomultiplelower-dimensionalsubproblems.Thecooperativemechanismisthendescribed,whichinvolvesinformationsharingandcooperationamongthesubproblems.ThekeycomponentsofDC-HMOEA,includingthepopulationinitialization,selectionstrategies,reproduction,andterminationconditions,arepresented. 4.PerformanceEvaluation ToevaluatetheperformanceofDC-HMOEA,comprehensiveexperimentsareconducte