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Dynamic Spatial Guided Multi-Guide Particle Swarm Optimization Algorithm for Many-Objective 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

The multi-guide particle swarm optimization (MGPSO) algorithm utilizes random tournament selection in determining the archive guide for the velocity update of a particle, choosing the least crowded solution of a static number of solutions in the external archive. This report aims to determine the feasibility of utilizing a linearly decreasing tournament size with the aim of improving initial exploration and final exploitation of the search space by the particle swarms. The archive guide for a given particle is determined from the nearest archive solutions with the aim of increasing swarm exploration efficiency. The proposed dynamic spatial MGPSO algorithm is compared with the original MGPSO algorithm and state-of-the-art algorithms specifically designed to solve many-objective optimization problems. The results show that the dynamic soatial guided MGPSO (DSG-MGPSO) scales well to many-objective problems, with performance very competitive to that of other many-objective optimization algorithms.

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Acknowledgements

The authors thank the National Research Foundation (NRF) for providing funding and the Centre for High Performance Computing (CHPC) for providing computational resources.

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Authors and Affiliations

  1. Department of Industrial Engineering, Stellenbosch University, Stellenbosch, South Africa

    Weka Steyn

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

    Andries Engelbrecht

Authors
  1. Weka Steyn
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  2. Andries Engelbrecht
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Corresponding author

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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Steyn, W., Engelbrecht, A. (2022). Dynamic Spatial Guided Multi-Guide Particle Swarm Optimization Algorithm for Many-Objective 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_11

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  • DOI: https://doi.org/10.1007/978-3-031-20176-9_11

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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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