简介：AnExtendedParticleSwarmOptimizer(EPSO)isproposedinthispaper.Inthisnewalgorithm,notonlythelocalbutalsotheglobalbestpositionwillimpacttheparticle'svelocityupdatingprocess.EPSOisanintegrationofLocalBestparadigm(LBEST)andGlobalBestparadigm(GBEST)anditsignificantlyenhancestheperformanceoftheconventionalparticleswarmoptimizers.TheexperimentresultshaveprovedthatEPSOdeservestobeinvestigated.

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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：一个改进图象登记方法与混合优化器基于相互的信息被建议。第一，相互的信息措施与词法坡度信息被相结合。坡度信息的本质是有大坡度大小的地点应该被排列，而且在那些地点的坡度的取向应该是类似的。第二，一个混合优化器把PSO与鲍威尔相结合算法被建议制止相互的信息功能的本地最大值并且改进登记精确性到亚象素水平。最后，multiresolution数据结构不能仅仅基于Mallat分解改进登记功能的行为，而且改进算法的速度。试验性的结果证明新方法能产出好登记结果，比关于光滑和吸引力盆以及集中速度的传统的优化器优异。

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