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Stability-Guided Particle Swarm Optimization

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Swarm Intelligence (ANTS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13491))

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Abstract

Particle swarm optimization (PSO) performance has been shown to be sensitive to control parameter values. To obtain best possible results, control parameter tuning or self-adaptive PSO implementations are necessary. Theoretical stability analyses have produced stability conditions on the PSO control parameters to guarantee that an equilibrium state is reached. Should control parameter values be chosen to satisfy a stability condition, divergent and cyclic search behaviour is prevented, and particles are guaranteed to stop moving. This paper proposes that control parameter values be randomly sampled to satisfy a given stability condition, removing the need for control parameter tuning. Empirical results show that the resulting stability-guided PSO performs competitively to a PSO with tuned control parameter values.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, and Computer Science Division, Stellenbosch University, Stellenbosch, South Africa

    Andries Engelbrecht

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  1. Andries Engelbrecht
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Correspondence to Andries Engelbrecht .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  3. University of Manchester, Manchester, UK

    Manuel López-Ibáñez

  4. University of Málaga, Málaga, Spain

    José García-Nieto

  5. Stellenbosch University, Stellenbosch, South Africa

    Andries Engelbrecht

  6. Worcester Polytechnic Institute, Worcester, MA, USA

    Carlo Pinciroli

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

  8. Université Libre de Bruxelles, Brussels, Belgium

    Christian Camacho-Villalón

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Engelbrecht, A. (2022). Stability-Guided Particle Swarm Optimization. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_33

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  • DOI: https://doi.org/10.1007/978-3-031-20176-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20175-2

  • Online ISBN: 978-3-031-20176-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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