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收敛因子非线性变化的鲸鱼优化算法.docx

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收敛因子非线性变化的鲸鱼优化算法 Title:WhaleOptimizationAlgorithmwithNonlinearConvergenceFactorVariation Abstract: TheWhaleOptimizationAlgorithm(WOA)isanature-inspiredmetaheuristicalgorithmthathasbeenwidelyusedforsolvingoptimizationproblems.However,despiteitseffectiveness,theWOAhassomelimitations,suchasthelackofadaptabilitytotheconvergencerate.ThispaperproposesamodifiedversionoftheWOAcalledtheWhaleOptimizationAlgorithmwithNonlinearConvergenceFactorVariation(WOA-NCF)toaddressthisissue.TheWOA-NCFintroducesanonlinearvariationintheconvergencefactortoenhancetheconvergencespeedandtheglobalsearchabilityofthealgorithm.Theeffectivenessoftheproposedalgorithmisevaluatedthroughextensiveexperiments,andtheresultsdemonstrateitssuperiorityovertheoriginalWOAandseveralotherstate-of-the-artoptimizationalgorithms. 1.Introduction Optimizationproblemsariseinvariousfields,includingengineering,finance,andcomputerscience.Solvingtheseproblemsefficientlyandeffectivelyiscrucialforachievingoptimalsolutionsandimprovingperformance.Traditionaloptimizationtechniques,suchasgradient-basedmethods,haveproventobeinadequateforsolvingcomplex,high-dimensionalproblemswithnumerouslocaloptima. Nature-inspiredmetaheuristicalgorithmshaveemergedaspowerfultoolsforsolvingoptimizationproblems.Thesealgorithmsareinspiredbynaturalphenomena,suchasthebehaviorofanimalsorphysicalprocesses.TheWhaleOptimizationAlgorithm(WOA)isonesuchalgorithmthatimitatesthesocialbehaviorofhumpbackwhales. 2.TheWhaleOptimizationAlgorithm(WOA) TheWOAisapopulation-basedalgorithmthatoptimizesasetofsolutionsiteratively.Itisbasedonthesocialbehaviorofhumpbackwhales,whereeachwhalerepresentsapotentialsolution.Thealgorithmcomprisesthreemainphases:initialization,exploration,andexploitation. However,theoriginalWOAdoesnotconsidertheconvergencerateadaptability,whichlimitsitsperformanceinsolvingcomplexoptimizationproblemswithdifferentconvergencespeeds.Therefore,weproposetheWhaleOptimizationAlgorithmwithNonlinearConvergenceFactorVariation(WOA-NCF)toenhancetheconvergencespe