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Published in: Artificial Intelligence Review 5/2022

23-11-2021

A survey, taxonomy and progress evaluation of three decades of swarm optimisation

Authors: Jing Liu, Sreenatha Anavatti, Matthew Garratt, Kay Chen Tan, Hussein A. Abbass

Published in: Artificial Intelligence Review | Issue 5/2022

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Abstract

While the concept of swarm intelligence was introduced in 1980s, the first swarm optimisation algorithm was introduced a decade later, in 1992. In this paper, nineteen representative original swarm optimisation algorithms are analysed to extract their common features and design a taxonomy for swarm optimisation. We use twenty-nine benchmark problems to compare the performance of these nineteen algorithms in the form they were first introduced in the literature against five state-of-the-art swarm algorithms. This comparison reveals the advancements made in this field over three decades. It reveals that, while the state-of-the-art swarm optimisation algorithms are indeed competitive in terms of the quality of solutions they find, their complexities have evolved to be more computationally demanding when compared to the nineteen original algorithms of swarm optimisation. The investigation suggests that there is an urge to continue to design swarm optimisation algorithms that are simpler, while maintaining their current competitive performance.

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Appendix
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Metadata
Title
A survey, taxonomy and progress evaluation of three decades of swarm optimisation
Authors
Jing Liu
Sreenatha Anavatti
Matthew Garratt
Kay Chen Tan
Hussein A. Abbass
Publication date
23-11-2021
Publisher
Springer Netherlands
Published in
Artificial Intelligence Review / Issue 5/2022
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-021-10095-z

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