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

doi:10.1007/s11761-019-00284-8

IR > 经济管理学院

	Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem
	Zhao, Fuqing1 ; Du, Songlin 1; Zhang, Yi 2; Ma, Weimin 3; Song, Houbin1
	2020-03
发表期刊	Service Oriented Computing and Applications
ISSN	18632386
卷号	14 期号:1 页码:65-73
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.
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
DOI	10.1007/s11761-019-00284-8
收录类别	EI ; ESCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000517090200006
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20200208028514
EI主题词	Ecology
EI分类号	454.3 Ecology and Ecosystems - 537.1 Heat Treatment Processes - 723.1 Computer Programming - 912.2 Management - 921.5 Optimization Techniques
来源库	Compendex
分类代码	454.3 Ecology and Ecosystems - 537.1 Heat Treatment Processes - 723.1 Computer Programming - 912.2 Management - 921.5 Optimization Techniques
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/115583
专题	经济管理学院国际合作处（港澳台办）研究生院
通讯作者	Zhao, Fuqing
作者单位	1.Lanzhou Univ Technol, Sch Comp, Commun Technol, Lanzhou, Peoples R China; 2.Xijin Univ, Sch Mech Engn, Xian, Peoples R China; 3.Tongji Univ, Sch Econom, Management, Shanghai, Shanghai, Peoples R China
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,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.