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

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 settin...

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Published inThe Artificial intelligence review Vol. 56; no. 3; pp. 1867 - 1903
Main Authors Pourpanah, Farhad, Wang, Ran, Lim, Chee Peng, Wang, Xi-Zhao, Yazdani, Danial
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.03.2023
Springer
Springer Nature B.V
Subjects
Online AccessGet full text
ISSN0269-2821
1573-7462
DOI10.1007/s10462-022-10214-4

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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.
AbstractList 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.
Audience Academic
Author Wang, Ran
Pourpanah, Farhad
Lim, Chee Peng
Wang, Xi-Zhao
Yazdani, Danial
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  givenname: Farhad
  surname: Pourpanah
  fullname: Pourpanah, Farhad
  organization: College of Mathematics and Statistics, Shenzhen University, Department of Electrical and Computer Engineering, University of Windsor
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  givenname: Ran
  orcidid: 0000-0002-2586-5604
  surname: Wang
  fullname: Wang, Ran
  email: wangran@szu.edu.cn
  organization: College of Mathematics and Statistics, Shenzhen University, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University
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  givenname: Chee Peng
  surname: Lim
  fullname: Lim, Chee Peng
  organization: Institute for Intelligent Systems Research and Innovation, Deakin University
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  givenname: Xi-Zhao
  surname: Wang
  fullname: Wang, Xi-Zhao
  organization: College of Computer Science and Software Engineering, Shenzhen University, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University
– sequence: 5
  givenname: Danial
  surname: Yazdani
  fullname: Yazdani, Danial
  organization: School of Computer Science and Engineering, Southern University of Science and Technology
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Issue 3
Keywords Artificial fish swarm algorithm
Hybrid models
Continuous optimization
Dynamic optimization
Swarm intelligence
Multi-objective optimization
Fish schooling
Language English
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PublicationSubtitle An International Science and Engineering Journal
PublicationTitle The Artificial intelligence review
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SSID ssj0005243
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Snippet The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following...
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StartPage 1867
SubjectTerms Algorithms
Artificial Intelligence
Combinatorial analysis
Computer Science
Human beings
Influence on nature
Mathematical models
Optimization
Parameter modification
Swarm intelligence
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Title A review of artificial fish swarm algorithms: recent advances and applications
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