粒子群優(yōu)化算法的改進(jìn)方法-研究

粒子群優(yōu)化算法的改進(jìn)方法-研究

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時間:2019-01-30

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1、AbstractParticleSwarmOptimization(PSO)iSanewmetllodusedinthesolutionofOptimizationProblems.Sinceitwasproposedin1995,theresearcharticlesaboutithaveincreasedquickly.TheconceptofPSOissimpleandeasytoimplement.Itrandomlyinitializesacertainscaleofparticleswarm,inwhicheachparticle、ⅣitIlcertai

2、nintelligenceisusedtorepresentthecandidatesolutioninthespecificoptimizationproblem,andthenusestheinformationofthegroupandindividualparticlestofindanoptimalsolutionquicklythroughtheiterativeevolution.Tmsalgorithm.basedonthetwotheories—swarmintelligenceandevolutionarycomputation,isf.a(chǎn)Vor

3、edbroadlybecauseofitsmanyadvantagessuchasitsfewparameters,simplesetupprocedureandthefastconverge.NowadaystherehavebeenalargenumberofpapersabouttheimprovedalgorithminordertomakethePSOalgorithmperformbetter.Theyhavebeensuccessfullyappliedtotheengineeringoptimizationproblems.Astheresearch

4、ismoreandmoredeeply,itsapplicationfieldsarealsoexpanding,anditsperformanceisalsogreatlyimproved.Tl:lispaperfh'stlydoesanin—depthstudyofthetheoreticalbasis,basicprincipleandrealizationprocessofthePSOalgorithm.Itanalyzestheinfluenceofrelatedparametersonthearithmeticperformance,theefficie

5、ncyofthealgorithmandtheimplementationsonthebasisofsimulationexperiments.ConsideringthedefectsofthePSO,thispaperanalyzeswhatandwhichaspectsshouldbeimprovedonthebasisoftheparticlemovementcharacteristics,andthepaperalsoexplainstheimprovementandappliedscopeofthealgorithm.Byanalyzingthebasi

6、ctheoriesandtheimprovedmethods,thispaperproposesamulti—agentparticleswarmoptimizationalgorithmbasedonthetheoryofmulti—agents,andgivesspecificproceduresofthisalgorithm.n圮authorprovestheeffectivenessofitsimprovementthroughtheMATLABsimulationexperiment,analyzesconcretelytheimprovedresults

7、,andmeanwhilepointsouttheweakpoints.Finally,thispapersummarizestheauthor’SstudyonthePSOalgorithm,andproposesafurtherresearchplan.ItisprovedthattheimprovedMAPSOismoreeffectivethanthebasicPSOwhensolvingthemorecomplexmultimodalfunctionoptimizationproblems.111eMAPSOtrulyrealizestheglobal

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