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 in | The Artificial intelligence review Vol. 56; no. 3; pp. 1867 - 1903 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.03.2023
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0269-2821 1573-7462 |
| DOI | 10.1007/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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Farhad surname: Pourpanah fullname: Pourpanah, Farhad organization: College of Mathematics and Statistics, Shenzhen University, Department of Electrical and Computer Engineering, University of Windsor – sequence: 2 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 – sequence: 3 givenname: Chee Peng surname: Lim fullname: Lim, Chee Peng organization: Institute for Intelligent Systems Research and Innovation, Deakin University – sequence: 4 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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| Keywords | Artificial fish swarm algorithm Hybrid models Continuous optimization Dynamic optimization Swarm intelligence Multi-objective optimization Fish schooling |
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| SubjectTerms | Algorithms Artificial Intelligence Combinatorial analysis Computer Science Human beings Influence on nature Mathematical models Optimization Parameter modification Swarm intelligence |
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