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Analysis and improvement of the binary particle swarm optimization

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Abstract

Solving binary-real problems with bio-inspired algorithms is an active research matter. However, the efficiency of the employed algorithm varies drastically by tailoring the governing equations or just by adopting “more adequate” parameter setting. Within this framework, we aim to improve the parameter setting of the binary particle swarm optimization (BPSO). We derive a Markov chain model of BPSO. The transition probabilities reveal that the acceleration coefficients control the transition speed between the exploitation and exploration phases. The transition probabilities also depict a poor exploration ratio in high-dimensional search spaces. Increasing the values of the acceleration coefficients may enhance the exploration ratio. Nevertheless, overly high values for these coefficients present some shortcomings. Numerical experiments realized on three different problem sets (e.g. multidimensional knapsack problem) further prove the need to increase the acceleration coefficients as the search space dimension rises. We recommend a set of equations governing the best setting for acceleration coefficients. Finally, a comparison with other BPSO variants reveals the merits of the suggested setting over the conventional ones.

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Notes

  1. Note that the reasoning made here is valid for other PSO topologies.

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Funding

This work has been supported by the Tunisian Ministry of Higher Education and Scientific Research (21PEJC D1P14 project).

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  1. Laboratory of Probability and Statistics, Department of Mathematics, Faculty of sciences of Sfax, University of Sfax, B.P. 1171, Sfax, Tunisia

    Sameh Kessentini

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Kessentini, S. Analysis and improvement of the binary particle swarm optimization. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-06112-3

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