Kiranyaz S.İnce, TürkerGabbouj, Moncef2023-06-162023-06-162010364212238897836421223850302-9743https://doi.org/10.1007/978-3-642-12239-2_35https://hdl.handle.net/20.500.14365/3403Centre for Emergent Computing at Edinburgh Napier University;Istanbul Technical University;Microsoft Turkey;Scientific and Technological Research Council of TurkeyEvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, EvoApplicatons 2010 -- 7 April 2010 through 9 April 2010 -- Istanbul -- 80273With an ever-growing attention Particle Swarm Optimization (PSO) has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. For this purpose we use simultaneous perturbation stochastic approximation (SPSA), which is applied only to the gbest (not to the entire swarm) for a low-cost solution. Since the problem of poor gbest update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), two distinct SA approaches are then integrated into MD-PSO and tested over a set of unsupervised data clustering applications. Experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems. © 2010 Springer-Verlag Berlin Heidelberg.eninfo:eu-repo/semantics/closedAccessDynamic data clusteringGradient descentMulti-dimensional searchParticle Swarm OptimizationStochastic approximationApproximation theoryCluster analysisClustering algorithmsGradient methodsStochastic systemsTraffic signalsDynamic dataGradient descentLow-cost solutionMulti dimensionalMulti-dimensional particle swarm optimizationsOptimization problemsSimultaneous perturbation stochastic approximationStochastic approximationsParticle swarm optimization (PSO)Conference Object10.1007/978-3-642-12239-2_352-s2.0-77952330401