重庆大学学报：自然科学版2011，Vol.34Issue(10)：61-67,7.
改进免疫克隆算法的Job Shop调度
Job-Shop scheduling based on improved immune cloning algorithm
摘要
Abstract
Parallel immune clone algorithm is proposed based on population coevolution theory and parallel computing affinity of individual at multiple compute nodes.Introducing the immune memory mechanism,the evolution processes of antibody population and memory units are conducted simultaneously,meanwhile,it improves mutual cooperation among antibodies,and ensures solution set approaching optimal solution from the inside of feasible region or infeasible region border.Clone proliferation,high frequency variation and operation of crossover operators increase the chance that better individuals gain affinity maturation by the operation of clone expansion,improve diversity of antibody population distribution,achieve the balance of optimization between depth and range,and ensure the convergence of the algorithm and the diversity of the search range.A computational study for a standard data set is carried out to test the validity of the algorithm,and the effect of algorithm parameters on the results is analyzed.The simulation results show that the global search capability,local search capability,algorithm stability and computing speed of the algorithm are all superior to conventional optimization algorithms such as normal immune clone optimization algorithm,genetic algorithm,etc.关键词
生产控制/调度算法/并行免疫克隆算法/协同优化/克隆激励Key words
production control/scheduling algorithms/parallel immunity clone algorithm/coordination optimization/cloning incentive分类
信息技术与安全科学引用本文复制引用
刘爱军,杨育,邢青松,姚豪,张煜东,周振宇..改进免疫克隆算法的Job Shop调度[J].重庆大学学报：自然科学版,2011,34(10):61-67,7.基金项目
国家自然科学基金资助项目 （）
新世纪优秀人才支持计划资助项目 （）
重庆大学“211工程”三期创新人才培养计划建设项目 （）
重庆大学研究生科技创新基金资助项目 （）
中央高校基本科研业务费科研专项自然科学类面上项目 （）
