A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

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 AFSA parameters modifications, procedures 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.