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群搜索优化算法的改进及其在结构优化设计中的应用.docx

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群搜索优化算法的改进及其在结构优化设计中的应用 Title:ImprovementsinSwarmSearchOptimizationAlgorithmsandTheirApplicationinStructuralOptimizationDesign Introduction: SwarmSearchOptimization(SSO)algorithmsareaclassofmetaheuristicalgorithmsinspiredbythecollectivebehaviorofsocialinsects,suchasants,bees,andbirds.Thesealgorithmsemulatethedecentralized,self-organizednatureofswarmstosolvecomplexoptimizationproblems.TheprimarygoalofSSOalgorithmsistofindtheoptimalornear-optimalsolutionbyiterativelyexploringthesearchspaceandsharinginformationamongthepopulationmembers. ThispaperaimstodiscusstheimprovementsmadeinSSOalgorithmsandtheirapplicationsinstructuraloptimizationdesign.Thestructuraloptimizationdesigninvolvesfindingthedesignvariablesthatminimizetheobjectivefunctionwhilesatisfyingtheconstraints,leadingtothemostefficientandlightweightstructures. 1.HistoricalOverview: ThepaperstartswithabriefhistoricaloverviewofSSOalgorithms,tracingtheoriginfromtheseminalAntColonyOptimization(ACO)algorithmtovariousswarmintelligence-basedalgorithmslikeParticleSwarmOptimization(PSO),ArtificialBeeColonyOptimization(ABC),andFireflyAlgorithm(FA).Thissectionhighlightsthekeyfeaturesandlimitationsofthesealgorithms,settingthestageforthesubsequentdiscussionsontheimprovements. 2.ImprovementsinSSOAlgorithms: ThesecondsectionfocusesontherecentadvancementsandimprovementsinSSOalgorithms.Itdiscussesthefollowingkeyenhancements: a.LocalSearchStrategies:Theinclusionoflocalsearchstrategies,suchashillclimbingorsimulatedannealing,torefinethesolutionslocally. b.AdaptiveParameters:Theadaptationofalgorithmparametersduringtheoptimizationprocesstoenhancetheexplorationandexploitationbalance. c.HybridApproaches:TheintegrationofSSOalgorithmswithotheroptimizationtechniques,suchasgeneticalgorithmsordifferentialevolution,toovercometheirrespectivelimitationsandimproveoptimizationperformance. d.DynamicEnvironmentAdaptation:ThedevelopmentofSSOalgorithmsthatcandynamicallyadapttochangesintheoptimizationproblemorenvironment. Eachimprovementisexplainedinde