基于最大熵进化算法的高维多目标优化

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中图分类号：TP18 文献标志码：A DOI：10.3969/j. issn.1673-3819.2025.05.009

Abstract：With theincreaseindatavolume，dimensionality，andthenumberofoptimizationobjectives，theconflictsbetweenobjectives intensify，makingthesolutionofmulti-objectiveoptimizationproblems increasinglycomplex.Thisisespecially trueforhigh-dimensionalheterogeneousmulti-objectiveoptimizationproblems，wherethedificultyofsolvingthemincreases significantly.Inthispaper，weproposeamaximumentropy-basedreferencevector-guidedevolutionaryalgorithm aimedat solving many-objectiveoptimization problems.Bycombining reference point strategies with evolutionaryalgorithm search mechanisms，the proposed methodachieves complementarycooperation between the ideal and worst reference points， therebyimprovingtheeficiencyofoptimization.Thealgorithmreliesonasetofadaptivelyselectedreference vectorsandoptimizes them using Bayesian maximumentropy，focusing on balancing diversityand convergence during theoptimization processThrough comparative experimentsonseveral benchmark problems，theproposed K-RVEAalgorithmdemonstrates significant advantages，verifying the feasibility and effectiveness of the method.

Keywords：many-objective optimization；maximum entropy；diversity；convergence

在经济和工业领域，许多高维、异构的多目标优化问题日益凸显。（剩余11454字）