文献类型：期刊文献

英文题名：A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem

作者：Gu, Jinwei[1];Gu, Manzhan[2];Cao, Cuiwen[1];Gu, Xingsheng[1]

机构：[1]E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China

年份：2010

卷号：37

期号：5

起止页码：927

外文期刊名：COMPUTERS & OPERATIONS RESEARCH

收录：;EI(收录号：20094512437213);WOS:【SCI-EXPANDED(收录号:WOS:000272652900013)】；

基金：We are very grateful to the editor and anonymous reviewers for their valuable comments and suggestions to help improve our paper. This work is supported by National Natural Science Foundation of China (Grant no. 60774078), Shanghai Commission of Science and Technology (Grant no. 08JC1408200), Shanghai Leading Academic Discipline Project (Grant no. B504), Doctor Foundation of Ministry of Education of China (Grant no: 200802510010), and China Postdoctotal Science Foundation funded project (Grant no. 20080430080). This project is National High Technology Research and Development Program of China (863 Program) (No. 2009AA04Z141).

语种：英文

外文关键词：Stochastic; Job shop scheduling; Competitive; Co-evolution algorithm; Genetic algorithm

摘要：In this paper, a novel competitive co-evolutionary quantum genetic algorithm (CCQGA) is proposed for a stochastic job shop scheduling problem (SJSSP) with the objective to minimize the expected value of makespan. Three new strategies named as competitive hunter. cooperative surviving and the big fish eating small fish are developed in population growth process. Based on improved co-evolution idea of multi-population and concepts of quantum theory, this algorithm could not only adjust population size dynamically to increase the diversity of genes and avoid premature convergence, but also accelerate the convergence speed with Q-bit representation and quantum rotation gate. FT benchmark-based problems where the processing times are subjected to independent normal distributions are solved effectively by CCQGA. The experiment results achieved by CCQGA are compared with quantum-inspired genetic algorithm (QGA) and standard genetic algorithm (GA), which shows that CCQGA has better feasibility and effectiveness. (C) 2009 Elsevier Ltd. All rights reserved.