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Set-Based Particle Swarm Optimisation: A Review

Author

Listed:
  • Jean-Pierre van Zyl

    (Division of Computer Science, Stellenbosch University, Stellenbosch 7600, South Africa)

  • Andries Petrus Engelbrecht

    (Division of Computer Science, Stellenbosch University, Stellenbosch 7600, South Africa
    Department of Industrial Engineering, Stellenbosch University, Stellenbosch 7600, South Africa
    Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Mubarak Al-Abdullah 7207, Kuwait)

Abstract

The set-based particle swarm optimisation algorithm is a swarm-based meta-heuristic that has gained popularity in recent years. In contrast to the original particle swarm optimisation algorithm, the set-based particle swarm optimisation algorithm is used to solve discrete and combinatorial optimisation problems. The main objective of this paper is to review the set-based particle swarm optimisation algorithm and to provide an overview of the problems to which the algorithm has been applied. This paper starts with an examination of previous attempts to create a set-based particle swarm optimisation algorithm and discusses the shortcomings of the existing attempts. The set-based particle swarm optimisation algorithm is established as the only suitable particle swarm variant that is both based on true set theory and does not require problem-specific modifications. In-depth explanations are given regarding the general position and velocity update equations, the mechanisms used to control the exploration–exploitation trade-off, and the quantifiers of swarm diversity. After the various existing applications of set-based particle swarm optimisation are presented, this paper concludes with a discussion on potential future research.

Suggested Citation

  • Jean-Pierre van Zyl & Andries Petrus Engelbrecht, 2023. "Set-Based Particle Swarm Optimisation: A Review," Mathematics, MDPI, vol. 11(13), pages 1-36, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2980-:d:1186413
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    References listed on IDEAS

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    3. Matthias Ehrgott, 2005. "Multicriteria Optimization," Springer Books, Springer, edition 0, number 978-3-540-27659-3, January.
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