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DMPSO: Diversity-Guided Multi-Mutation Particle Swarm Optimizer

文献类型：期刊论文

作者Tian, Dongping1; Zhao, Xiaofei3; Shi, Zhongzhi2
刊名IEEE ACCESS
出版日期2019
卷号7页码:124008-124025
关键词Particle swarm optimization opposition-based learning swarm diversity inertial weight premature convergence local optima mutation strategy
ISSN号2169-3536
DOI10.1109/ACCESS.2019.2938063
英文摘要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.
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
语种英语
WOS记录号WOS:000487837100001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/4665]  
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.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Tianjin Polytech Univ, Sch Comp Sci & Technol, Tianjin 300387, 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.

入库方式： OAI收割

来源：计算技术研究所

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