Particle swarm optimization with probabilistic inertia weight

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Abstract

Particle swarm optimization (PSO) is a stochastic swarm-based algorithm inspired by the intelligent collective behavior of some animals. There are very few parameters to adjust in PSO which makes PSO easy to implement. One of the important parameter is inertia weight (ω) which balances the exploration and exploitation properties of PSO in a search space. In this paper, a new variation of PSO has been proposed, which utilizes a novel adaptive inertia weight strategy based on the binomial probability distribution for global optimization. This new technique improves final accuracy and the convergence speed of PSO with better performance. This new strategy has been tested against a set of ten benchmark functions and compared with four other PSO variants. The result shows that this new strategy is better and very competitive in most of the cases than other PSO variants.

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Agrawal, A., & Tripathi, S. (2019). Particle swarm optimization with probabilistic inertia weight. In Advances in Intelligent Systems and Computing (Vol. 741, pp. 239–248). Springer Verlag. https://doi.org/10.1007/978-981-13-0761-4_24

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