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 1,3; Tang, Jianxin 3; Wang, Junbiao 1; Jonrinaldi 2
刊名	COMPUTERS & OPERATIONS RESEARCH
	2014-05
卷号	45 页码:38-50
关键词	Particle swarm optimization Expanded job shop scheduling problem Multi-objective job shop scheduling problem Decline disturbance Adaptive meta-Lamarckian strategy
ISSN号	0305-0548
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. (C) 2013 Elsevier Ltd. All rights reserved.
资助项目	Science Foundation for Excellent Youth Scholars of Lanzhou University of Technology[1114ZTC139] ; Science Foundation for Excellent Youth Scholars of Lanzhou University of Technology[2012M521802] ; Science Foundation for Excellent Youth Scholars of Lanzhou University of Technology[2013T60889] ; Science Foundation for Excellent Youth Scholars of Lanzhou University of Technology[1014ZCX017]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000332434600005
状态	已发表
内容类型	期刊论文
源URL	[http://119.78.100.223/handle/2XXMBERH/34402]
专题	国际合作处（港澳台办）计算机与通信学院
通讯作者	Zhao, Fuqing
作者单位	1.Northwestern Polytech Univ, Minist Educ, Key Lab Contemporary Design & Integrated Mfg Tech, Xian, Peoples R China 2.Univ Exeter, Sch Engn Comp Sci & Math, Exeter EX4 4QF, Devon, England 3.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R 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 & 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 & 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 & OPERATIONS RESEARCH 45(2014):38-50.