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具有适应性突变和惯性权重的粒子群优化(PSO)算法及其在动态系统参数估计中的应用

DOI: 10.3724/SP.J.1004.2011.00541, PP. 541-549

Keywords: Particleswarmoptimization(PSO),parameterestimation,nonlineardynamics,inertiaweight,adaptivemutation

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Abstract:

?Animportantprobleminengineeringistheunknownparametersestimationinnonlinearsystems.Inthispaper,anoveladaptiveparticleswarmoptimization(APSO)methodisproposedtosolvethisproblem.Thisworkconsiderstwonewaspects,namelyanadaptivemutationmechanismandadynamicinertiaweightintotheconventionalparticleswarmoptimization(PSO)method.Thesemechanismsareemployedtoenhanceglobalsearchabilityandtoincreaseaccuracy.First,threewell-knownbenchmarkfunctionsnamelyGriewank,RosenbrockandRastrigrinareutilizedtotesttheabilityofasearchalgorithmforidentifyingtheglobaloptimum.TheperformanceoftheproposedAPSOiscomparedwithadvancedalgorithmssuchasanonlinearlydecreasingweightPSO(NDWPSO)andareal-codedgeneticalgorithm(GA),intermsofparameteraccuracyandconvergencespeed.ItisconfirmedthattheproposedAPSOismoresuccessfulthanotheraforementionedalgorithms.Finally,thefeasibilityofthisalgorithmisdemonstratedthroughestimatingtheparametersoftwokindsofhighlynonlinearsystemsasthecasestudies.

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