A knowledge-based differential covariance matrix adaptation cooperative algorithm

doi:10.1016/j.eswa.2021.115495

	A knowledge-based differential covariance matrix adaptation cooperative algorithm
	Zuo, Yang 3; Zhao, Fuqing 1; Li, Zekai 2
刊名	EXPERT SYSTEMS WITH APPLICATIONS
	2021-12-01
卷号	184
关键词	Cooperation Differential evolution Covariance matrix adaptation Niching population size Learning mechanism Design of experiment
ISSN号	0957-4174
DOI	10.1016/j.eswa.2021.115495
英文摘要	In this paper, a knowledge-based differential covariance matrix adaptation cooperative algorithm (DCMAC) is proposed for continuous problems. On the basis of combining successful history-based adaptive DE variants with linear population size reduction (LSHADE) and covariance matrix adaptation evolutionary strategy (CMA-ES), DCMAC proposes a strategy based on knowledge reward and punishment to achieve the purpose of collaborative optimization. Afterward, an adaptive learning mechanism is introduced to optimize the parameters to balance the exploitation and exploration of DCMAC. This process enables the algorithm to have global search capability. Finally, the niching-based population size reduction mechanism is introduced to improve the local search ability of the DCMAC. A weighted mutation strategy with dynamic greedy p value and covariance matrix adaptation (CMA) sampled based on differential vector are presented. Meanwhile, the knowledge acquired in the previous iteration process is adopted in the algorithm to select a mutation strategy for generating the new candidate solutions in the next iteration. The niching population size reduction mechanism is introduced to maintain the diversity of the population and compared with the other classical population size reduction methods. The optimal combination of parameters in the DCMAC algorithm is testified by the design of the experiment. Furthermore, the DCMAC is testified on the CEC2017 benchmark functions. The effectiveness and efficiency of the DCMAC are demonstrated by the experimental results in solving complex continuous problems.
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000697001800001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/148926]
专题	国际合作处（港澳台办）
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun Technol, Lanzhou 730050, Peoples R China; 2.Beijing Hollysys Ind Software Co Ltd, Xian 710077, Peoples R China 3.Henan Inst Technol, Elect & Informat Engn Coll, Xinxiang 453000, Henan, Peoples R China;
推荐引用方式 GB/T 7714	Zuo, Yang,Zhao, Fuqing,Li, Zekai. A knowledge-based differential covariance matrix adaptation cooperative algorithm[J]. EXPERT SYSTEMS WITH APPLICATIONS,2021,184.
APA	Zuo, Yang,Zhao, Fuqing,&Li, Zekai.(2021).A knowledge-based differential covariance matrix adaptation cooperative algorithm.EXPERT SYSTEMS WITH APPLICATIONS,184.
MLA	Zuo, Yang,et al."A knowledge-based differential covariance matrix adaptation cooperative algorithm".EXPERT SYSTEMS WITH APPLICATIONS 184(2021).