This article aims at improving the Particle Swarm Optimization, by uniquely reshaping its update strategy for generating new solutions with a switching strategy that transits between exploration and convergence, a time-varying inertia weight to control particles' movement and an aging mechanism to avoid stagnation in local basins of attraction. The algorithm addressed as MPSO-SUS has been compared with eight other state-of-artEAs on a standard benchmark of sixteen functions. The results of such comparison indicate that MPSO-SUS clearly and statistically outperform the other well-known approaches, justifying its distinctive feature which makes it a successful optimizer. © 2012 Springer-Verlag.
CITATION STYLE
Kundu, R., Mukherjee, R., & Das, S. (2012). Modified particle swarm optimization with switching update strategy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7677 LNCS, pp. 644–652). https://doi.org/10.1007/978-3-642-35380-2_75
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