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混合策略改进鲸鱼优化算法.docx

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混合策略改进鲸鱼优化算法 Title:HybridStrategyforEnhancingWhaleOptimizationAlgorithm Abstract: Nature-inspiredalgorithmshavegainedsubstantialattentioninsolvingcomplexoptimizationproblems.WhaleOptimizationAlgorithm(WOA)isarecentmetaheuristicalgorithminspiredbythebehaviorofhumpbackwhales.WOAexhibitsefficientexplorationandexploitationcapabilitiesbutsuffersfromprematureconvergenceandslowconvergencespeed.Toovercometheselimitations,thispaperproposesahybridstrategytoenhancetheperformanceoftheWOA.ThehybridstrategycombinestheWOAwithotheroptimizationalgorithms,aimingtoleveragetheirstrengthsandmitigatetheirweaknesses.ExperimentalresultsonbenchmarkfunctionsdemonstratethattheproposedhybridstrategyoutperformstheoriginalWOAintermsofconvergencespeed,searchaccuracy,androbustness. 1.Introduction Optimizationproblemsappearinvariousdomains,suchasengineering,operationsresearch,economics,andmachinelearning.Inspiredbythebehaviorofanimals,metaheuristicalgorithmshavebeenwidelyappliedtosolvecomplexoptimizationproblems.TheWhaleOptimizationAlgorithm(WOA)isanovelalgorithmthatmimicsthehuntingbehaviorofhumpbackwhales.WOAhasshownpromisingresultsinvariousoptimizationtasks,butitalsopossessescertainlimitations.TheaimofthispaperistoproposeahybridstrategytoimprovetheperformanceofWOA. 2.OverviewofWhaleOptimizationAlgorithm(WOA) ThissectionprovidesabriefintroductiontotheWhaleOptimizationAlgorithm.ItexplainsthekeystepsinvolvedintheWOA,includingtheinitialization,encirclingprey,updatingpositions,andupdatingpreyvalues.ThestrengthsandweaknessesoftheWOAarediscussed,highlightingtheneedforimprovement. 3.HybridStrategyforEnhancingWOA TheproposedhybridstrategyaimstoenhancetheexplorationandexploitationcapabilitiesoftheWOAbyintegratingitwithotheroptimizationalgorithms.Severalwell-knownalgorithms,suchasParticleSwarmOptimization(PSO),GeneticAlgorithm(GA),andDifferentialEvolution(DE),areconsideredasthepotentialcandidatesforhybridization.Thehybridstrategyconstitutestwomaincomponents:acooperativestrategyandacompetitivestrategy.Thecoo