  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.
