Abstract
Particle Swarm Algorithm has demonstrated to be a powerful optimizer in multitude of optimization problems. The use of multipopulation technique with periodic interchange of individuals has proved to increase the convergence toward good solutions in many other EvolutionaryAlgorithms.However, the policy of interchange of individuals ought to be careful studied and selected, otherwise, pernicious effects could be introduced in the optimization process. The main focus of this study is on when, how and what individuals should be exchanged between populations in order to improve the convergence. In this paper, a deep study of diverse interchange policies for multipopulation applied to Particle Swarm Optimizer is presented.
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Cárdenas-Montes, M., Vega-Rodríguez, M.A., Gómez-Iglesias, A. (2010). Performance Improvement in Multipopulation Particle Swarm Algorithm. In: de Leon F. de Carvalho, A.P., Rodríguez-González, S., De Paz Santana, J.F., Rodríguez, J.M.C. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14883-5_68
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DOI: https://doi.org/10.1007/978-3-642-14883-5_68
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