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1、AModifiedParticleSwarmOptimizerYuhuiShiandRussellEberhartDepartmentofElectricalEngineeringIndianaUniversityPurdueUniversityIndianapolishdianqolis,IN46202-5160Shi.eberhart@,tech.iuEnti.eduindexofthebestparhcleamongalltheparticlesintheABSTRACTpopulationisrepresentedbythesymbolg.TherateofInthisp
2、aper,weintroduceanewparameter,calledthepositionchange(velocity)forparticleiisrepresentedinertiaweight,intotheoriginalparticleswarmasVf(vil,viz,...,VD).Theparticlesaremanipulatedoptimizer.Simulationshavebeendonetoillustratetheaccordingtothefollowingequation:signilicantandeffectiveimpactofthisn
3、ewparameterontheparticleswarmoptimizer.1.INTRODUCTIONwherec1andc2aretwopositiveconstants,rand()andRand()aretworandomfunctionsinthemge[0,1].TheEvolutionarycomputationtechniques(evolutionarysecondpartoftheequation(la)isthe“~~gnition”part,programming[4],geneticalgorithms[5],evolutionarywhichrepr
4、esentstheprivatethinkingoftheparticlestrategies[9]andgeneticprogramming[SI)areitself.Thethirdpartisthe“social”part,whichmotivatedbytheevolutionofnature.Apopulationofrepresentsthecollaborationamongtheparticles[7].Theindividuals.whichencodetheproblemsolutions.areequation(1a)isusedtocalculatethe
5、particle‘snewmanipulatedaccordingtotheruleofsurvivalofthevelocityaccordingtoitspreviousvelocityandthefittestthrough“genetic”operations,suchasmutation,distancesofitscurrentpositionfromitsownbestcrossoverandreproduction.Abestsolutionisevolvedexperience(position)andthegroup’sbestexperience.throu
6、ghthegenerations.ThesekjndsoftechniqueshaveThentheparticlefliestowardanewpositionaccordingbeensuccessfullyappliedinmanyareasandalotofnewtoequation(lb).Theperformanceofeachparticleisapplicationsareexpectedtoappear.Incontrasttomeasuredaccordingtoapredefinedfitnessfundion,evolutionarycomputation
7、techniques,Eberhartandwhichisrelatedtotheproblemtobesolved.Kennedydevelopedadifferentalgorithmthroughsimulatingsocialbehavior[2,3,6,7JAsinotherTheparticleswarmoptimizerhasbeenfoundtobealgorithms,apopulationofindividualsexists.Thisrobustandfas
