基于改进多种群遗传算法的柔性作业车间调度研究  

Research on flexible job shop scheduling problem based on improved multi-population genetic algorithm

作　　者：王雷[1] 蔡劲草 

机构地区：[1]安徽工程大学机械与汽车工程学院,安徽芜湖241000

出　　处：《云南大学学报（自然科学版）》2017年第2期192-199,共8页Journal of Yunnan University(Natural Sciences Edition)

基　　金：国家自然科学基金(51305001);安徽省高校优秀青年人才支持计划重点项目(gxyqZD2016125);安徽省自然科学基金(1708085ME129)

摘　　要：传统的遗传算法在解决柔性作业车间调度问题的过程中容易出现收敛速度慢,陷入局部最优等问题.针对最大完工时间最小优化问题,对多种族遗传算法进行改进.采用横向与纵向相结合的进化机制,在遗传的过程中加入定向进化的过程,设计了定向进化概率公式,可以加快获得最优解的速度.在选择过程中,采用复活制,将被淘汰的个体与优秀基因库中的个体再次进行进化操作,可以避免优秀基因的流失.实验结果表明了改进遗传算法的有效性和可行性.The traditional genetic algorithm for solving flexible job shop scheduling problem tends to fall into slow convergence and local optimum.Aiming at minimizing the largest makespan for flexible job shop scheduling problem ,an improved multi-population genetic algorithm （IMGA） is proposed in this paper.The lateral and longitudinal evolutionary mechanism is used.The directed evolution is used for genetic algorithm, and directed evolution probability is also designed in order to accelerate the speed for getting the optimal solution. In the selection process ,the resurrection strategy is used to avoid losing some good genes by combining eliminated individual genes with outstanding individual genes during the evolving operation. The experimental results demonstrated that the proposed IMGA is feasible and effective.

关 键 词：改进遗传算法 柔性作业车间调度 定向进化 复活策略 协同进化 

分 类 号：TH166[机械工程—机械制造及自动化] TP301.6[自动化与计算机技术—计算机系统结构]