Opposition-based particle swarm optimization with adaptive mutation strategy

doi:10.1007/s00500-016-2102-5

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Opposition-based particle swarm optimization with adaptive mutation strategy
	Dong, Wenyong 1; Kang, Lanlan 1,2; Zhang, Wensheng3
发表期刊	SOFT COMPUTING
	2017-09-01
卷号	21 期号:17 页码:5081-5090
文章类型	Article
摘要	To 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.
关键词	Particle Swarm Optimization Adaptive Mutation Generalized Opposition-based Learning Adaptive Nonlinear Inertia Weight
WOS标题词	Science & Technology ; Technology
DOI	10.1007/s00500-016-2102-5
收录类别	SCI
语种	英语
项目资助者	National 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研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000408231900018
引用统计	被引频次：53[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20721
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
作者单位	1.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
推荐引用方式 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.