Abstract
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.
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Kundu, R., Mukherjee, R., Das, S. (2012). Modified Particle Swarm Optimization with Switching Update Strategy. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_75
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DOI: https://doi.org/10.1007/978-3-642-35380-2_75
Publisher Name: Springer, Berlin, Heidelberg
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