An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning

doi:10.1080/09540091.2019.1674247

	An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning
	Zhao, Fuqing1 ; Zhang, Lixin 1; Zhang, Yi 2; Ma, Weimin 3; Zhang, Chuck 4; Song, Houbin1
	2020-04-02
会议录名称	Connection Science
卷号	32
期号	2
页码	132-161
出版者	Taylor and Francis Ltd.
摘要	Water Wave Optimisation algorithm (WWO) is a new swarm-based metaheuristic inspired by shallow wave models for global optimisation. In this paper, an enhanced WWO, which combines with multiple assistant strategies (EWWO), is proposed. First, the random opposition-based learning (ROBL) mechanism is introduced to generate the initial population with high quality. Second, a new modified operation is designed and embedded into propagation operation to balance the global exploration and the local exploitation. Third, the covariance matrix self-adaptation evolution strategy (CMA-ES) is employed by the refraction operation to further strengthen the local exploitation. Furthermore, the diversity of the population is maintained in the evolution process by using a crossover operator. The experiment results based on CEC 2017 benchmarks indicate that the EWWO outperforms the state-of-the-art variant algorithms of the WWO and the standard WWO. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
关键词	Covariance matrix Global optimizationCrossover operator Evolution process Evolution strategies Global exploration Global optimisation Initial population Opposition-based learning State of the art
DOI	10.1080/09540091.2019.1674247
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000489597000001
EI入藏号	20194407600839
EI主题词	Water waves
ISSN	09540091
来源库	Compendex
分类代码	471.4 Seawater, Tides and Waves - 921 Mathematics - 921.5 Optimization Techniques
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/132663
专题	国际合作处（港澳台办）研究生院
作者单位	1.School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou, China; 2.School of Mechanical Engineering, Xijin University, Xi’an, China; 3.School of Economics and Management, Tongji University, Shanghai, China; 4.H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta; GA, United States
第一作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhao, Fuqing,Zhang, Lixin,Zhang, Yi,et al. An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning[C]:Taylor and Francis Ltd.,2020:132-161.