南方科技大学知识苑(SUSTech KC): A review of artificial fish swarm algorithms: recent advances and applications

题名	A review of artificial fish swarm algorithms: recent advances and applications
作者	Pourpanah, Farhad 1,2; Wang, Ran 1,3; Lim, Chee Peng 4; Wang, Xi-Zhao 5,7; Yazdani, Danial6
通讯作者	Wang, Ran
发表日期	2022-06-01
DOI	10.1007/s10462-022-10214-4
发表期刊	ARTIFICIAL INTELLIGENCE REVIEW 影响因子和分区
ISSN	0269-2821
EISSN	1573-7462
卷号	56 期号:3页码:1867-1903
摘要	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.
关键词	Artificial fish swarm algorithm Fish schooling Swarm intelligence Hybrid models Continuous optimization Multi-objective optimization Dynamic optimization
相关链接	[来源记录]
收录类别	SCI ; EI
语种	英语
学校署名	其他
资助项目	National Natural Science Foundation of China[62176160,61976141,61732011] ; Natural Science Foundation of Shenzhen (University Stability Support Program)[20200804193857002]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000814064800002
出版者	SPRINGER
EI入藏号	20222512256545
EI主题词	Combinatorial optimization ; Swarm intelligence
EI分类号	Artificial Intelligence:723.4 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Optimization Techniques:921.5
ESI学科分类	COMPUTER SCIENCE
Scopus记录号	2-s2.0-85132354834
来源库	Web of Science
引用统计	被引频次[WOS]：48
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/347968
专题	工学院_计算机科学与工程系
作者单位	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
推荐引用方式 GB/T 7714	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.
APA	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.
MLA	Pourpanah, Farhad,et al."A review of artificial fish swarm algorithms: recent advances and applications".ARTIFICIAL INTELLIGENCE REVIEW 56.3(2022):1867-1903.