Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization

doi:10.1016/j.swevo.2019.100573

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization
	Tian, Dongping 1; Zhao, Xiaofei 2; Shi, Zhongzhi 2
刊名	SWARM AND EVOLUTIONARY COMPUTATION
	2019-12-01
卷号	51 页码:16
关键词	Particle swarm optimization Acceleration coefficients Logistic map Swarm diversity Inertial weight Premature convergence
ISSN号	2210-6502
DOI	10.1016/j.swevo.2019.100573
英文摘要	Particle swarm optimization (PSO) is a stochastic computation technique motivated by intelligent collective behavior of some animals, which has been widely used to address many hard optimization problems. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima when dealing with complex multimodal problems. In this paper, we propose a chaotic particle swarm optimization with sigmoid-based acceleration coefficients (abbreviated as CPSOS). On the one hand, the frequently used logistic map is applied to generate well-distributed initial particles. On the other hand, the sigmoid-based acceleration coefficients are formulated to balance the global search ability in the early stage and the global convergence in the latter stage. In particular, two sets of slowly varying function and regular varying function embedded update mechanism in conjunction with the chaos based re-initialization and Gaussian mutation strategies are employed at different evolution stages to update the particles during the whole search process, which can effectively keep the diversity of the swarm and get out of possible local optima to continue exploring the potential search regions of the solution space. To validate the performance of CPSOS, a series of experiments are conducted and the simulation results reveal that the proposed method can achieve better performance compared to several state-of-the-art PSO variants in terms of solution accuracy and effectiveness.
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 R&D Program of the Shaanxi Province of China[2018GY-037]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000500379000004
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/14932]
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, Shaanxi, Peoples R China 2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Tian, Dongping,Zhao, Xiaofei,Shi, Zhongzhi. Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization[J]. SWARM AND EVOLUTIONARY COMPUTATION,2019,51:16.
APA	Tian, Dongping,Zhao, Xiaofei,&Shi, Zhongzhi.(2019).Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization.SWARM AND EVOLUTIONARY COMPUTATION,51,16.
MLA	Tian, Dongping,et al."Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization".SWARM AND EVOLUTIONARY COMPUTATION 51(2019):16.