多目标置换流水车间调度的混沌杂草优化算法

Chaos invasive weed optimization algorithm for multi-objective permutation flow shop scheduling problem

针对最小化最大完工时间,总流程时间及总延迟时间的多目标置换流水车间调度问题,提出一种改进的混沌杂草优化算法,该算法采用基于熵值权重的灰熵关联度适应值分配策略,引入快速非支配排序法生成外部档案,并将进化种群的更新和最优位置的混沌搜索相结合,用于维护外部档案,提升算法的寻优性能.通过与NSGA-Ⅱ算法进行OR-Library典型测试算例的对比实验,验证该算法的有效性.

This paper presents an improved chaos invasive weed optimization algorithm for solving the multi-objective permutation flow shop scheduling problem, minimizing the maximum completion time （makespan）, total flowtime （TFT） and total tardiness simultaneousiy. In this study, the grey entropy correlation grade based on entropies weights is adopted for adaptive value distribution strategy. Then, a fast non-dominated sorting approach is introduced to establish external archive. In addition, evolutionary population updates are combined with chaos search around the optimal location to maintain external archive, which improves the efficiency of the scheduling algorithm. Finally, typical OR-Library examples are selected to test the new method. Numerical results demonstrate the effectiveness of the designed algorithm compared with NSGA-Ⅱ.