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International Conference on Swarm Intelligence

ANTS 2020: Swarm Intelligence pp 96–106Cite as

Control Parameter Importance and Sensitivity Analysis of the Multi-Guide Particle Swarm Optimization Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12421))

Abstract

The multi-guide particle swarm optimization (MGPSO) algorithm is a multi-objective optimization algorithm that uses multiple swarms, each swarm focusing on an individual objective. This paper conducts an importance and sensitivity analysis on the MGPSO control parameters 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 has the greatest sensitivity and is the most important control parameter to tune when optimizing the MGPSO.

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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. Computer Science Division, 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 Timothy G. Carolus .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  4. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  5. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  6. Université Libre de Bruxelles, Brussels, Belgium

    Mary Katherine Heinrich

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

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Carolus, T.G., Engelbrecht, A.P. (2020). Control Parameter Importance and Sensitivity Analysis of the Multi-Guide Particle Swarm Optimization Algorithm. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-60376-2_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60375-5

  • Online ISBN: 978-3-030-60376-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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