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Opposition-based particle swarm optimization with adaptive mutation strategy
Dong, Wenyong1; Kang, Lanlan1,2; Zhang, Wensheng3
Source PublicationSOFT COMPUTING
2017-09-01
Volume21Issue:17Pages:5081-5090
SubtypeArticle
AbstractTo solve the problem of premature convergence in traditional particle swarm optimization (PSO), an opposition-based particle swarm optimization with adaptive mutation strategy (AMOPSO) is proposed in this paper. In all the variants of PSO, the generalized opposition-based PSO (GOPSO), which introduces the generalized opposition-based learning (GOBL), is a prominent one. However, GOPSO may increase probability of being trapped into local optimum. Thus we introduce two complementary strategies to improve the performance of GOPSO: (1) a kind of adaptive mutation selection strategy (AMS) is used to strengthen its exploratory ability, and (2) an adaptive nonlinear inertia weight (ANIW) is introduced to enhance its exploitative ability. The rational principles are as follows: (1) AMS aims to perform local search around the global optimal particle in current population by adaptive disturbed mutation, so it can be beneficial to improve its exploratory ability and accelerate its convergence speed; (2) because it makes the PSO become rigid to keep fixed constant for the inertia weight, ANIW is used to adaptively tune the inertia weight to balance the contradiction between exploration and exploitation during its iteration process. Compared with several opposition-based PSOs on 14 benchmark functions, the experimental results show that the performance of the proposed AMOPSO algorithm is better or competitive to compared algorithms referred in this paper.
KeywordParticle Swarm Optimization Adaptive Mutation Generalized Opposition-based Learning Adaptive Nonlinear Inertia Weight
WOS HeadingsScience & Technology ; Technology
DOI10.1007/s00500-016-2102-5
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61170305 ; Natural Science Foundation of Guangdong Province of China(2014A030313454) ; Foundation of science, technology bureau of Liuzhou city of Guangxi Province of China(2014J020401) ; 61573157 ; 61562025)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000408231900018
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20721
Collection精密感知与控制研究中心_人工智能与机器学习
Affiliation1.Wuhan Univ, Comp Sch, Wuhan 430072, Hubei, Peoples R China
2.Jiangxi Univ Sci & Technol, Sch Apply Sci, Ganzhou 341000, Peoples R China
3.Chinese Acad Sci, State Key Lab Intelligent Control & Management Co, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Dong, Wenyong,Kang, Lanlan,Zhang, Wensheng. Opposition-based particle swarm optimization with adaptive mutation strategy[J]. SOFT COMPUTING,2017,21(17):5081-5090.
APA Dong, Wenyong,Kang, Lanlan,&Zhang, Wensheng.(2017).Opposition-based particle swarm optimization with adaptive mutation strategy.SOFT COMPUTING,21(17),5081-5090.
MLA Dong, Wenyong,et al."Opposition-based particle swarm optimization with adaptive mutation strategy".SOFT COMPUTING 21.17(2017):5081-5090.
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