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Self-adaptive Quantum Particle Swarm Optimization for Dynamic Environments

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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

The quantum-inspired particle swarm optimization (QPSO) algorithm has been developed to find and track an optimum for dynamic optimization problems. Though QPSO has been shown to be effective, despite its simplicity, it does introduce an additional control parameter: the radius of the quantum cloud. The performance of QPSO is sensitive to the value assigned to this problem dependent parameter, which basically limits the area of the search space wherein new, better optima can be detected. This paper proposes a strategy to dynamically adapt the quantum radius, with changes in the environment. A comparison of the adaptive radius QPSO with the static radius QPSO showed that the adaptive approach achieves desirable results, without prior tuning of the quantum radius.

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Author information

Authors and Affiliations

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

    Gary Pamparà

  2. Institute for Big Data and Data Science, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

Authors
  1. Gary Pamparà
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  2. Andries P. Engelbrecht
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Corresponding author

Correspondence to Gary Pamparà .

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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Pamparà, G., Engelbrecht, A.P. (2018). Self-adaptive Quantum Particle Swarm Optimization for Dynamic Environments. 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_13

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  • DOI: https://doi.org/10.1007/978-3-030-00533-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00532-0

  • Online ISBN: 978-3-030-00533-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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