Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem

doi:10.1007/s11761-019-00284-8

	Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem
	Zhao, Fuqing 2; Du, Songlin 2; Zhang, Yi 3; Ma, Weimin 1; Song, Houbin 2
刊名	Service Oriented Computing and Applications
	2020-03-01
卷号	14 期号:1 页码:65-73
关键词	Global optimization Heuristic algorithms Manufacture Scheduling Web services Biogeography-based optimizations Cloud infrastructures Diversity of populations Global optimization problems Mutation operators Non-separable problems Rotational variance Service oriented computing
ISSN号	18632386
DOI	10.1007/s11761-019-00284-8
英文摘要	In recent years, scheduling problems have attracted enormous attentions from practitioners and researches in manufacturing systems, for instance, the scheduling of computing resource in cloud infrastructure and cloud services. The scheduling problems in cloud services, big data and other service-oriented computing problems are regarded as non-separable problems. In this paper, a hybrid biogeography-based optimization with the enhanced mutation operator and CMA-ES (HBBO-CMA) is proposed to enhance the ability of exploitation on non-separable problems and alleviate the rotational variance. In the migration operator, the rotationally invariant migration operator is designed to reduce the dependence of BBO on the coordinate system and control the diversity of population. In the mutation operator, an enhanced mutation operator, which is sampled from the mean value and stand deviation of the variables of population, is employed to effectively escape the local optimum. Furthermore, the CMA-ES, which has outstanding performance on the non-separable problem, is applied to extend the exploitation of HBBO-CMA. Experimental results on CEC-2017 demonstrated the effectiveness of the proposed HBBO-CMA. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
WOS研究方向	Computer Science
语种	英语
出版者	Springer Science and Business Media Deutschland GmbH
WOS记录号	WOS:000517090200006
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/115583]
专题	国际合作处（港澳台办）经济管理学院研究生院
作者单位	1.School of Economics and Management, Tongji University, Shanghai; 200092, China 2.School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou; 730050, China; 3.School of Mechanical Engineering, Xijin University, Xi’an; 710123, China;
推荐引用方式 GB/T 7714	Zhao, Fuqing,Du, Songlin,Zhang, Yi,et al. Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem[J]. Service Oriented Computing and Applications,2020,14(1):65-73.
APA	Zhao, Fuqing,Du, Songlin,Zhang, Yi,Ma, Weimin,&Song, Houbin.(2020).Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem.Service Oriented Computing and Applications,14(1),65-73.
MLA	Zhao, Fuqing,et al."Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem".Service Oriented Computing and Applications 14.1(2020):65-73.