A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization

Abbas El Dor (), Maurice Clerc () and Patrick Siarry ()

Computational Optimization and Applications, 2012, vol. 53, issue 1, 295 pages

Abstract: Particle swarm optimization (PSO) is characterized by a fast convergence, which can lead the algorithms of this class to stagnate in local optima. In this paper, a variant of the standard PSO algorithm is presented, called PSO-2S, based on several initializations in different zones of the search space, using charged particles. This algorithm uses two kinds of swarms, a main one that gathers the best particles of auxiliary ones, initialized several times. The auxiliary swarms are initialized in different areas, then an electrostatic repulsion heuristic is applied in each area to increase its diversity. We analyse the performance of the proposed approach on a testbed made of unimodal and multimodal test functions with and without coordinate rotation and shift. The Lennard-Jones potential problem is also used. The proposed algorithm is compared to several other PSO algorithms on this benchmark. The obtained results show the efficiency of the proposed algorithm. Copyright Springer Science+Business Media, LLC 2012

Keywords: Particle swarm optimization; Tribes-PSO; Global optimization; Multi-swarm; Repulsion heuristic; Partitioned search space (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10589-011-9449-4

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