简介：这篇论文主要被奉献给在变化环境与固定拓扑学群的一个班结队。第一，为每个代理人的控制器被采用在代理人的状态和它的邻居的平均状态之间的错误术语建议。第二，为群的一个足够的条件在环境的坡度被围住，起始的位置图被连接的假设下面被介绍完成结队。第三，因为环境是一架飞机，尽管不一个代理人知道这个组的中心在哪儿，每个代理人的速度最后收敛到群中心的速度，这进一步被证明。最后，数字例子被包括说明获得的结果。

简介：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.

简介：TherearesomeadjustableparameterswhichdirecdyinfluencetheperformanceandstabilityofParticleSwarmOp-ttimizationalgorithm.Inthispaper,stabilitiesofPSOwithconstantparametersandtime-varyingparametersareanalyzedwithoutLipschitzconstraint.Necessaryandsufficientstabilityconditionsforaccelerationfactorψandinertiaweightwarepresented.Exper-imentsonbenchmarkfunctionsshowthegoodperfomanceofPSOsatisfyingthestabilitycondition,evenwithoutLipschitzcon-straint.Andtheinertiaweightwvalueisenhancedto(-1,1).

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