Common model analysis and improvement of particle swarm optimizer-中国期刊网

摘要 Particleswarmoptimizer(PSO),anewevolutionarycomputationalgorithm,exhibitsgoodperformanceforoptimizationproblems,althoughPSOcannotguaranteeconvergenceofaglobalminimum,evenalocalminimum.However,therearesomeadjustableparametersandrestrictiveconditionswhichcanaffectperformanceofthealgorithm.Inthispaper,thealgorithmareanalyzedasatime-varyingdynamicsystem,andthesufficientconditionsforasymptoticstabilityofaccelerationfactors,incrementofaccelerationfactorsandinertiaweightarededuced.Thevalueoftheinertiaweightisenhancedto(fi1,1).Basedonthededucedprincipleofaccelerationfactors,anewadaptivePSOalgorithm-harmoniousPSO(HPSO)isproposed.FurthermoreitisprovedthatHPSOisaglobalsearchalgorithm.Intheexperiments,HPSOareusedtothemodelidentificationofalinearmotordrivingservosystem.AnAkaikeinformationcriteriabasedfitnessfunctionisdesignedandthealgorithmscannotonlyestimatetheparameters,butalsodeterminetheorderofthemodelsimultaneously.TheresultsdemonstratetheeffectivenessofHPSO.

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Common model analysis and improvement of particle swarm optimizer

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Common model analysis and improvement of particle swarm optimizer

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