A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
Farhad Pourpanah, Ran Wang, Chee Peng Lim, Xi-Zhao Wang and, Danial Yazdani

TL;DR
This paper reviews recent advances in the Artificial Fish Swarm Algorithm (AFSA), highlighting improvements, hybrid models, and diverse applications in continuous optimization since 2013.
Contribution
It provides a comprehensive overview of AFSA developments, parameter modifications, hybrid models, and future research directions in the field.
Findings
Enhanced AFSA models show improved convergence.
Hybrid AFSA methods outperform traditional algorithms.
Applications span various real-world optimization problems.
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…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms · Vehicle Routing Optimization Methods
