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A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms

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Abstract

Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms.

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Authors and Affiliations

  1. Faculty of IT, Monash University, Clayton, 3800, Australia

    Han Duy Phan, Kirsten Ellis & Alan Dorin

  2. School of IT, Faculty of Science, Engineering and Built Environment, Deakin University, Burwood, 3125, Australia

    Jan Carlo Barca

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Correspondence to Han Duy Phan.

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The authors Han Phan, Kirsten Ellis, Jan Carlo Barca declare that they have no conflict of interest. The author Alan Dorin is a member of the editorial board for the journal Neural Computing and Applications.

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Phan, H.D., Ellis, K., Barca, J.C. et al. A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms. Neural Comput & Applic 32, 567–588 (2020). https://doi.org/10.1007/s00521-019-04229-2

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