A multipopulation cooperative coevolutionary whale optimization algorithm with a two-stage orthogonal learning mechanism

doi:10.1016/j.knosys.2022.108664

	A multipopulation cooperative coevolutionary whale optimization algorithm with a two-stage orthogonal learning mechanism
	Zhao, Fuqing1 ; Bao, Haizhu 1; Wang, Ling 2; Cao, Jie1 ; Tang, Jianxin1 ; Jonrinaldi 3
	2022-06-21
发表期刊	Knowledge-Based Systems
ISSN	0950-7051
卷号	246
摘要	This paper designed a multipopulation cooperative coevolutionary framework with a two-stage orthogonal learning (OL) mechanism for the whale optimization algorithm (MCCWOA) to improve the performance of the whale optimization algorithm (WOA). In the framework, a prediction model of the neighborhood structure is established by discovering the guidance information of the following iteration process in the objective space at the first-stage OL. In the second-stage OL, an auxiliary vector pool with various features in the decision space is introduced to guide the candidates falling in the stagnant status to conduct more valuable exploration. According to the domain knowledge of the candidates, the population is divided into the elite population, the intermediate population, and the inferior population. The information of the subpopulations has interacted with the corresponding historical populations in the evolution processes to enhance the ability of cooperative coevolution among individuals. A standard set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed algorithm. The results of the statistical analysis, diversity analysis, and convergence analysis testified that the MCCWOA outperforms the 15 state-of-the-art algorithms regarding efficiency and significance. © 2022 Elsevier B.V.
关键词	Domain Knowledge Iterative methods Learning algorithms Vector spaces Co-evolutionary Historical information Learning mechanism Metaheuristic Multi population Optimization algorithms Orthogonal learning mechanism Performance Prediction modelling Whale optimization algorithm
DOI	10.1016/j.knosys.2022.108664
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000877293100001
出版者	Elsevier B.V.
EI入藏号	20221511958592
EI主题词	Optimization
EI分类号	723.4 Artificial Intelligence ; 723.4.2 Machine Learning ; 921 Mathematics ; 921.5 Optimization Techniques ; 921.6 Numerical Methods
来源库	WOS
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/157889
专题	国际合作处（港澳台办）计算机与通信学院
通讯作者	Zhao, Fuqing
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China; 2.Tsinghua Univ, Dept Automat, Beijing 10084, Peoples R China; 3.Univ Andalas, Dept Ind Engn, Padang 25163, Indonesia
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhao, Fuqing,Bao, Haizhu,Wang, Ling,et al. A multipopulation cooperative coevolutionary whale optimization algorithm with a two-stage orthogonal learning mechanism[J]. Knowledge-Based Systems,2022,246.
APA	Zhao, Fuqing,Bao, Haizhu,Wang, Ling,Cao, Jie,Tang, Jianxin,&Jonrinaldi.(2022).A multipopulation cooperative coevolutionary whale optimization algorithm with a two-stage orthogonal learning mechanism.Knowledge-Based Systems,246.
MLA	Zhao, Fuqing,et al."A multipopulation cooperative coevolutionary whale optimization algorithm with a two-stage orthogonal learning mechanism".Knowledge-Based Systems 246(2022).