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1、华中科技大学硕士学位论文AbstractTherearealargenumberofoptimizationproblemswhichwidelyexsitintheaerospace,industrialengineering,economicmanagement,transportationandotherfields.Theseoptimizationproblemspromptthecontinuousimprovementofoptimizationtheoryandmethods.However,thecomplexityofoptimizat
2、ionproblemsbecomemoreandmoresophisticatedwiththerapiddevelopmentofcomputertechnology.Furthermore,itisdifficultfortraditionalalgorithms,suchasthesimplexmethod,steepestdescentmethod,conjugategradientmethodandsoon,tosolvetheseproblemswiththecharacteristicsoflarge-scale,nonlinear,disc
3、ontinuous,multiextremevalues,etc.Thus,someintelligentoptimizationalgorithmscomeintobeing,suchasGeneticalgorithm,ParticleSwarmOptimizationalgorithm,AntColonyOptimizationalgorithm,etc.Nevertheless,themajorityofintelligentoptimizationalgorithmssufferfromprematureconvergenceandotheris
4、sues.Therefore,thispaperproposedtwoinprovedhybridintelligentoptimizationalgorithms,whichmayhavesomepracticalsignificance.FortheprematureproblemofParticleSwarmOptimization,anewhybridintelligentoptimizationalgorithmcalledMult-strategyParticleSwarmOptimizationalgorithmisproposed,whic
5、hcombinesParticleSwarmOptimizationalgorithmwithsteepestdescentmethod.Intheprocessofparticleswarmoptimization,theoptimalparticleperformslocalsearchbyusingthesteepestdescentstrategywithdifferencequotientandcorrectivedeclinestrategy,nonoptimalparticlesperformglobalsearchbyusingaggreg
6、ationstrategy.Whiletheentirepopulationistrappedinlocalminima,theoptimalparticleandnonoptimalparticlesuserandommobilestrategyanddiffusionstrategytoescapefromthelocalextremumpointrespectively.Intheend,theperformanceofMulti-strategyParticleSwarmOptimizationalgorithmistestedwithfourty
7、picalbenchmarkfunctionsandiscomparedwithothertwoimprovedParticleSwarmOptimizationalgorithms’.Numericalresultsindicatethattheproposedalgorithmhasbetterperformanceincludingstability,theabilityofglobalsearch,etc.Inordertosolveproblemsonparametersettingsandthecomputationalcomplexityof
8、II万方数据华中科技大学硕士学位论文operatorsinClou