An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem

doi:10.1016/j.cor.2013.11.019

	An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem
	Zhao, Fuqing 2,3; Tang, Jianxin 3; Wang, Junbiao 2; Jonrinaldi 1
刊名	Computers and Operations Research
	2014-05-01
卷号	45 页码:38-50
关键词	Markov processes Multiobjective optimization Particle swarm optimization (PSO) Reactive power Scheduling Stochastic systems Decline disturbance Evolutional algorithm Global optimal solutions Job shop scheduling problems Local optimizations Meta-lamarckian strategies Multi-objective job shop scheduling Pre-mature convergences
ISSN号	03050548
DOI	10.1016/j.cor.2013.11.019
英文摘要	As same with many evolutional algorithms, performance of simple PSO depends on its parameters, and it often suffers the problem of being trapped in local optima so as to cause premature convergence. In this paper, an improved particle swarm optimization with decline disturbance index (DDPSO), is proposed to improve the ability of particles to explore the global and local optimization solutions, and to reduce the probability of being trapped into the local optima. The correctness of the modification, which incorporated a decline disturbance index, was proved. The key question why the proposed method can reduce the probability of being trapped in local optima was answered. The modification improves the ability of particles to explore the global and local optimization solutions, and reduces the probability of being trapped into the local optima. Theoretical analysis, which is based on stochastic processes, proves that the trajectory of particle is a Markov processes and DDPSO algorithm converges to the global optimal solution with mean square merit. After the exploration based on DDPSO, neighborhood search strategy is used in a local search and an adaptive meta-Lamarckian strategy is employed to dynamically decide which neighborhood should be selected to stress exploitation in each generation. The multi-objective combination problems with DDPSO for finding the pareto front was presented under certain performance index. Simulation results and comparisons with typical algorithms show the effectiveness and robustness of the proposed DDPSO. © 2013 Elsevier Ltd. All rights reserved.
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	Elsevier Ltd
WOS记录号	WOS:000332434600005
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/150439]
专题	国际合作处（港澳台办）计算机与通信学院
作者单位	1.School of Engineering, Computer Science and Mathematics, University of Exeter, EX4 4QF, United Kingdom 2.Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, China; 3.School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China;
推荐引用方式 GB/T 7714	Zhao, Fuqing,Tang, Jianxin,Wang, Junbiao,et al. An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem[J]. Computers and Operations Research,2014,45:38-50.
APA	Zhao, Fuqing,Tang, Jianxin,Wang, Junbiao,&Jonrinaldi.(2014).An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem.Computers and Operations Research,45,38-50.
MLA	Zhao, Fuqing,et al."An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem".Computers and Operations Research 45(2014):38-50.