A review of artificial fish swarm algorithms : recent advances and applications

Farhad POURPANAH, Ran WANG*, Chee Peng LIM, Xi-Zhao WANG, Danial YAZDANI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

54 Citations (Scopus)

Abstract

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models.

Original languageEnglish
Pages (from-to)1867-1903
Number of pages37
JournalArtificial Intelligence Review
Volume56
Issue number3
Early online date21 Jun 2022
DOIs
Publication statusPublished - Mar 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Keywords

  • Artificial fish swarm algorithm
  • Continuous optimization
  • Dynamic optimization
  • Fish schooling
  • Hybrid models
  • Multi-objective optimization
  • Swarm intelligence

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