基于多目标优化的粒子群算法-研究与其应用

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1、西南交通大学硕士研究生学位论文第1I页AbstractOptimizationproblems，especiallymulti-objectiveoptimizationproblemusingintelligentalgorithmisoneofthefocusareasofevolutionarycomputation．Particleswarmoptimizationalgorithmasarelativelynewoptimizationtechnique，theconceptissimple，lesscontrolparameters，theoptimizationresulth

2、asnothingtodowiththeinitialvalue，withacertainparallelism，andithasbeenwidespreadandhasgoodprospectsfordevelopment．However,thetraditionalparticleswarmalgorithmstillhassomedeficienciesinglobalsearchandconvergence，thealgorithmhasattractedtheinterestofmanyresearchersandscholarsandalmostallofthemaredevo

3、tedtothestudyoftheperformanceimprovementalgorithm．Itisconfirmedthattheparticleswarmalgorithmhasaverygoodapplicationforsingleobjectiveoptimizationproblems，butthemulti-objectiveparticleswarmoptimizationalgorithmanditsapplicationremainstohefurtherstudied．Basedonthestandardparticleswarmalgorithmandmul

4、ti-·objectiveoptimizationtheory,themulti——objectiveparticleSWarmoptimizationalgorithm(Multi-ObjectiveParticleSwarmOptimizationalgorithmreferredtoas”MOPSOalgorithm”)isstudiedintensivelyinthispaper,theconcretecontentandinnovativepointsCanhesumrnarizedasfollows：(1)Thebasicconceptsandthestandardpartic

5、leofmulti-objectiveoptimizationalgorithmisanalyzedandsummarizedtoestablishthetheoreticalbasisforthefurtherstudyofmulti-objectiveparticleswarmoptimizationalgorithm．(2)Byusingthenichetechnologyforfitness，adoptingtheroulettemethod，theglobalbestpositionisselectedaccordingtothefitnessofeliteofeachparti

6、cle，andaadjustmenthasmadeintherunningprocessofthealgorithm··-·joinedthesmallprobabilityofmutationstrategy,proposedamulti-objectiveparticleswarmalgorithmwithanichetechnologyandtheelitesetstrategy,thisimprovedalgorithmnotonlyimprovestheefficiencyofthealgorithm,butalsokeepstheconvergenceofthealgorith

7、mandtheuniformityofthesolutiondistribution．(3)Anovelmulti-objectiveparticleswarnloptimizationalgorithmisproposedinthispaper,improvingthespeedoftheparticlepopulationsupdateformula，combiningwith西南交通大学硕士研究生学位论文第1II页