摘要
针对最小化最大完工时间、最大机床负荷及总机床负荷的多目标柔性作业车间调度问题,提出一种带有局部搜索策略的自适应元胞遗传算法。该算法在每完成一次种群迭代后,对种群的平均收敛速度进行计算,确定出合适的选择压。根据种群选择压的大小对种群的拓扑结构进行自适应改变。为了减小邻域搜索空间,结合车间调度问题的特点,借鉴正交设计思想设计了基于正交规则的局部搜索策略。通过4个经典多目标柔性车间调度问题的仿真实验以及与其他算法的比较,说明了该算法的有效性和可行性。
In order to minimize the makespan, the maximal workload and the total workload simultaneously in the multi-objective flexible job shop scheduling problems, an adaptive cellular genetic algorithm with local search is proposed. In this algorithm, after each iteration of a population,the average convergence rate of the population is calculated to determine the appropriate selection pressure. Then the population＇ s topology changes adaptively according to the selection pressure. To reduce search space, combined with the features of flexible job shop scheduling problem, referencing orthogonal design ideas, a local search strategy is designed based on orthogonal rules. Finally, four typical simulation results are provided to demonstrate the feasibility and effectiveness of the proposed method.
出处
《现代制造工程》
CSCD
北大核心
2016年第11期41-49,共9页
Modern Manufacturing Engineering
基金
国家自然科学基金项目(51275274,71501110)
湖北省自然科学基金项目(2014CFB665)
