A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism

doi:10.1016/j.eswa.2022.117444

	A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism
	Zhao, Fuqing1 ; Zhou, Gang 1; Wang, Ling 2; Xu, Tianpeng1 ; Zhu, Ningning1 ; Jonrinaldi 3
	2022-10-01
发表期刊	Expert Systems with Applications
ISSN	0957-4174
卷号	203
摘要	Scatter search (SS) is a population-based metaheuristic algorithm, which has been proved high efficiency and effective optimizer for complex continuous real value problems. A two-stage cooperative SS guided with the multi-population hierarchical learning mechanism (TCSSMH) to overcome the slow convergence speed of the original SS is proposed. Three strategies are applied to the original SS. Firstly, TCSSMH adopts an adaptive two-way selection search strategy based on the elite reference set (RefSet), which is elite-oriented and ensures the quality of the population. Secondly, the multi-group hierarchical learning mechanism is embedded in the updating process of the RefSet, and the population of the candidates is divided into three levels including excellent candidates, medium candidates, and inferior candidates according to the fitness value of the function. These three subpopulations cooperate to balance the exploration and exploitation ability of the algorithm in the process of evolution. Finally, each subpopulation adopts an interactive learning strategy to increase the diversity of the population and avoid premature convergence of solutions. The optimum of each subpopulation with high accuracy is obtained by the pattern search (PS) optimization. The stronger search ability and higher search efficiency of these additional proposed strategies are verified by extensive experiments. The TCSSMH algorithm is tested on the CEC2017 benchmark test suite and practical engineering problems. The experimental results show that the TCSSMH algorithm is superior to other state-of-the-art algorithms in global search ability and convergence on the benchmark problems. © 2022 Elsevier Ltd
关键词	Benchmarking Educational technology Efficiency Evolutionary algorithms Optimization Hierarchical learning Hierarchical learning mechanism Interactive learning Interactive learning strategy Learning mechanism Learning strategy Multi population Pattern search Reference set Scatter search
DOI	10.1016/j.eswa.2022.117444
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000804926200006
出版者	Elsevier Ltd
EI入藏号	20222112141236
EI主题词	Learning systems
EI分类号	901.2 Education ; 913.1 Production Engineering ; 921.5 Optimization Techniques
来源库	WOS
引用统计	被引频次：4[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/158503
专题	国际合作处（港澳台办）计算机与通信学院科学技术处（军民融合领导小组办公室）
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 Automation, Beijing, 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,Zhou, Gang,Wang, Ling,et al. A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism[J]. Expert Systems with Applications,2022,203.
APA	Zhao, Fuqing,Zhou, Gang,Wang, Ling,Xu, Tianpeng,Zhu, Ningning,&Jonrinaldi.(2022).A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism.Expert Systems with Applications,203.
MLA	Zhao, Fuqing,et al."A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism".Expert Systems with Applications 203(2022).