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Gaussian-Valued Particle Swarm Optimization

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Swarm Intelligence (ANTS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11172))

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Abstract

This paper examines the position update equation of the particle swarm optimization (PSO) algorithm, leading to the proposal of a simplified position update based upon a Gaussian distribution. The proposed algorithm, Gaussian-valued particle swarm optimization (GVPSO), generates probabilistic positions by retaining key elements of the canonical update procedure while also removing the need to specify values for the traditional PSO control parameters. Experimental results across a set of 60 benchmark problems indicate that GVPSO outperforms both the standard PSO and the bare bones particle swarm optimization (BBPSO) algorithm, which also employs a Gaussian distribution to generate particle positions.

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Notes

  1. 1.

    Without loss of generality, this assumes that \(c_1(y_{ij}(t) - x_{ij}(t)) > 0\) and \(c_2(\hat{y}_{ij}(t) - x_{ij}(t)) > 0\), otherwise the bounds must be flipped, i.e., 0 becomes the upper bound.

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Acknowledgments

This work is based on the research supported by the National Research Foundation (NRF) of South Africa (Grant Number 46712). The opinions, findings and conclusions or recommendations expressed in this article is that of the author(s) alone, and not that of the NRF. The NRF accepts no liability whatsoever in this regard. This work is also supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Pretoria, Pretoria, South Africa

    Kyle Robert Harrison & Andries P. Engelbrecht

  2. Department of Computer Science, Brock University, St. Catharines, Canada

    Beatrice M. Ombuki-Berman

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  1. Kyle Robert Harrison
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  2. Beatrice M. Ombuki-Berman
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  3. Andries P. Engelbrecht
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Corresponding author

Correspondence to Kyle Robert Harrison .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari

  3. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  4. University of Southern Denmark, Odense, Denmark

    Anders L. Christensen

  5. University of Sheffield, Sheffield, UK

    Andreagiovanni Reina

  6. National Research Council, Rome, Italy

    Vito Trianni

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Harrison, K.R., Ombuki-Berman, B.M., Engelbrecht, A.P. (2018). Gaussian-Valued Particle Swarm Optimization. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_31

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  • DOI: https://doi.org/10.1007/978-3-030-00533-7_31

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