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International Conference on Swarm Intelligence

ICSI 2021: Advances in Swarm Intelligence pp 185–198Cite as

Multi-guide Particle Swarm Optimisation Control Parameter Importance in High Dimensional Spaces

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12689))

Abstract

This article presents an investigation into the effects of the search space dimension on the control parameter importance of the multi-guide particle swarm optimization (MGPSO) algorithm over time. The MGPSO algorithm is a multi-objective optimization algorithm that uses multiple swarms, each swarm focusing on an individual objective. This relative control parameter importance of the MGPSO is identified using functional analysis of variance (fANOVA). The fANOVA process quantifies the control parameter importance through analysing variance in the objective function values associated with a change in control parameter values. The results indicate that the inertia component value is the most influential control parameter to tune when optimizing the MGPSO throughout the run time. The relative importance of the inertia weight remains dominant with an increase in the search space dimensions.

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Notes

  1. 1.

    The nadir vector is a vector with components consisting of the worst objective values in the Pareto-optimal set.

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Author information

Authors and Affiliations

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

    Timothy G. Carolus & Andries P. Engelbrecht

  2. Division of Computer Science, Stellenbosch University, Stellenbosch, South Africa

    Andries P. Engelbrecht

Authors
  1. Timothy G. Carolus
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  2. Andries P. Engelbrecht
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Corresponding author

Correspondence to Andries P. Engelbrecht .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Carolus, T.G., Engelbrecht, A.P. (2021). Multi-guide Particle Swarm Optimisation Control Parameter Importance in High Dimensional Spaces. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_17

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  • DOI: https://doi.org/10.1007/978-3-030-78743-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78742-4

  • Online ISBN: 978-3-030-78743-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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