Van den Bergh, FEngelbrecht, AP2007-08-232007-08-232006Van den Bergh, F and Engelbrecht, AP. 2006. Study of particle swarm optimization particle trajectories. Information Sciences, Vol. 176, pp 937–9710020-0255http://hdl.handle.net/10204/1155Copyright: 2005 Elsevier Science B.VParticle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Most of the PSO studies are empirical, with only a few theoretical analyses that concentrate on understanding particle trajectories. These theoretical studies concentrate mainly on simplified PSO systems. This paper overviews current theoretical studies, and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term. The paper also provides a formal proof that each particle converges to a stable point. An empirical analysis of multidimensional stochastic particles is also presented. Experimental results are provided to support the conclusions drawn from the theoretical findingsenParticle swarm optimizationParticle trajectoriesEquilibriumConvergenceArticleVan den Bergh, F., & Engelbrecht, A. (2006). Study of particle swarm optimization particle trajectories. http://hdl.handle.net/10204/1155Van den Bergh, F, and AP Engelbrecht "Study of particle swarm optimization particle trajectories." (2006) http://hdl.handle.net/10204/1155Van den Bergh F, Engelbrecht A. Study of particle swarm optimization particle trajectories. 2006; http://hdl.handle.net/10204/1155.TY - Article AU - Van den Bergh, F AU - Engelbrecht, AP AB - Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Most of the PSO studies are empirical, with only a few theoretical analyses that concentrate on understanding particle trajectories. These theoretical studies concentrate mainly on simplified PSO systems. This paper overviews current theoretical studies, and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term. The paper also provides a formal proof that each particle converges to a stable point. An empirical analysis of multidimensional stochastic particles is also presented. Experimental results are provided to support the conclusions drawn from the theoretical findings DA - 2006 DB - ResearchSpace DP - CSIR KW - Particle swarm optimization KW - Particle trajectories KW - Equilibrium KW - Convergence LK - https://researchspace.csir.co.za PY - 2006 SM - 0020-0255 T1 - Study of particle swarm optimization particle trajectories TI - Study of particle swarm optimization particle trajectories UR - http://hdl.handle.net/10204/1155 ER -