Abstract
This paper introduces a modified particle swarm optimization (PSO) that exhibits the so-called “extreme social disagreements” among its wandering particles in order to resolve the stagnation when it occurs during search. We provide a short theoretical introduction about particle swarm optimization, then we describe and test our modified algorithms. We conclude from tests on several optimization benchmarks that our approach may help PSO escape stagnation in most of the situations in which it was tested. This work is intended to illustrate one of the benefits of using disagreements in social algorithms like PSO.
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Lihu, A., Holban, Ş., lihu, OA. (2013). Particle Swarm Optimization with Disagreements on Stagnation. In: Nguyen, N.T. (eds) Transactions on Computational Collective Intelligence XII. Lecture Notes in Computer Science, vol 8240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53878-0_9
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