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

Pourpanah, Farhad1,2; Wang, Ran1,3; Lim, Chee Peng4; Wang, Xi-Zhao5,7; Yazdani, Danial6

2022-06-01

10.1007/s10462-022-10214-4

ARTIFICIAL INTELLIGENCE REVIEW   Impact Factor & Quartile

0269-2821

1573-7462

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.

Artificial fish swarm algorithm Fish schooling Swarm intelligence Hybrid models Continuous optimization Multi-objective optimization Dynamic optimization

SCI ; EI

English

Others

National Natural Science Foundation of China[62176160,61976141,61732011] ; Natural Science Foundation of Shenzhen (University Stability Support Program)[20200804193857002]

Computer Science

Computer Science, Artificial Intelligence

WOS:000814064800002

SPRINGER

20222512256545

Combinatorial optimization ; Swarm intelligence

Artificial Intelligence:723.4 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Optimization Techniques:921.5

COMPUTER SCIENCE

2-s2.0-85132354834

Web of Science

1.Shenzhen Univ, Coll Math & Stat, Shenzhen, Peoples R China
2.Univ Windsor, Dept Elect & Comp Engn, Windsor, ON, Canada
3.Shenzhen Univ, Shenzhen Key Lab Adv Machine Learning & Applicat, Shenzhen, Peoples R China
4.Deakin Univ, Inst Intelligent Syst Res & Innovat, Geelong, Vic, Australia
5.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China
6.Southern Univ Sci & Technol, Sch Comp Sci & Engn, Shenzhen, Peoples R China
7.Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen, Peoples R China

Pourpanah, Farhad,Wang, Ran,Lim, Chee Peng,et al. A review of artificial fish swarm algorithms: recent advances and applications[J]. ARTIFICIAL INTELLIGENCE REVIEW,2022,56(3):1867-1903.

Pourpanah, Farhad,Wang, Ran,Lim, Chee Peng,Wang, Xi-Zhao,&Yazdani, Danial.(2022).A review of artificial fish swarm algorithms: recent advances and applications.ARTIFICIAL INTELLIGENCE REVIEW,56(3),1867-1903.

Pourpanah, Farhad,et al."A review of artificial fish swarm algorithms: recent advances and applications".ARTIFICIAL INTELLIGENCE REVIEW 56.3(2022):1867-1903.