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

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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.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 62176160, 61976141 and 61732011, in part by the Natural Science Foundation of Shenzhen (University Stability Support Program) under Grant 20200804193857002, and in part by the Interdisciplinary Innovation Team of Shenzhen University.

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Authors and Affiliations

  1. College of Mathematics and Statistics, Shenzhen University, Shenzhen, China

    Farhad Pourpanah & Ran Wang

  2. Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada

    Farhad Pourpanah

  3. Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, China

    Ran Wang

  4. Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia

    Chee Peng Lim

  5. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Xi-Zhao Wang

  6. School of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Danial Yazdani

  7. Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, China

    Ran Wang & Xi-Zhao Wang

Authors
  1. Farhad Pourpanah
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  2. Ran Wang
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  3. Chee Peng Lim
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  4. Xi-Zhao Wang
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  5. Danial Yazdani
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Correspondence to Ran Wang.

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Pourpanah, F., Wang, R., Lim, C.P. et al. A review of artificial fish swarm algorithms: recent advances and applications. Artif Intell Rev 56, 1867–1903 (2023). https://doi.org/10.1007/s10462-022-10214-4

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