DMPSO: Diversity-Guided Multi-Mutation Particle Swarm Optimizer

doi:10.1109/ACCESS.2019.2938063

	DMPSO: Diversity-Guided Multi-Mutation Particle Swarm Optimizer
	Tian, Dongping 1; Zhao, Xiaofei 2; Shi, Zhongzhi 3
	2019
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	7 页码:124008-124025
摘要	Particle swarm optimization (PSO) is a population based meta-heuristic search technique that has been widely applied to deal with various optimization problems. However, like other stochastic methods, PSO also encounters the problems of entrapment into local optima and premature convergence in solving complex multimodal problems. To tackle these issues, a diversity-guided multi-mutation particle swarm optimizer (abbreviated as DMPSO) is presented in this paper. To start with, the chaos opposition-based learning (OBL) is employed to yield high-quality initial particles to accelerate the convergence speed of DMPSO. Followed by, the self-regulating inertia weight is leveraged to strike a balance between the exploration and exploitation in the search space. After that, three different kinds of mutation strategies (gaussian, cauchy and chaotic mutations) are used to maintain the potential diversity of the whole swarm based on an effective diversity-guided mechanism. In particular, an auxiliary velocity-position update mechanism is exclusively applied to the global best particle that can effectively guarantee the convergence of the DMPSO. Finally, extensive experiments on a set of well-known unimodal and multimodal benchmark functions demonstrate that DMPSO outperforms most of the other tested PSO variants in terms of both the solution quality and its efficiency.
关键词	Particle swarm optimization opposition-based learning swarm diversity inertial weight premature convergence local optima mutation strategy
DOI	10.1109/ACCESS.2019.2938063
收录类别	SCI
语种	英语
资助项目	National Program on Key Basic Research Project (973 Program)[2013CB329502] ; National Natural Science Foundation of China[61971005] ; Tianchenghuizhi Fund for Innovation and Promotion of Education[2018A03036] ; Key Research and Development Program of the Shaanxi Province of China[2018GY-037]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000487837100001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4666
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Tian, Dongping
作者单位	1.Baoji Univ Arts & Sci, Inst Comp Software, Baoji 721007, Peoples R China 2.Tianjin Polytech Univ, Sch Comp Sci & Technol, Tianjin 300387, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Tian, Dongping,Zhao, Xiaofei,Shi, Zhongzhi. DMPSO: Diversity-Guided Multi-Mutation Particle Swarm Optimizer[J]. IEEE ACCESS,2019,7:124008-124025.
APA	Tian, Dongping,Zhao, Xiaofei,&Shi, Zhongzhi.(2019).DMPSO: Diversity-Guided Multi-Mutation Particle Swarm Optimizer.IEEE ACCESS,7,124008-124025.
MLA	Tian, Dongping,et al."DMPSO: Diversity-Guided Multi-Mutation Particle Swarm Optimizer".IEEE ACCESS 7(2019):124008-124025.