改进GWO算法求解柔性作业车间调度问题

针对以最小化最大完工时间为目标的柔性作业车间调度问题，设计一种改进的邻域搜索灰狼算法。设计一种适于灰狼算法的基于工序和机器的双层编码方案，改进种群初始化策略、灰狼变异操作以及种群更新机制；通过两点交叉操作、插入操作以及PR操作，得到GWO算法的全局搜索邻域，提出设计禁忌搜索邻域以增强GWO算法的局部开发能力。最后将所提算法在已知算例上进行仿真实验，并与其他算法进行对比。实验结果验证了改进GWO算法具有一定的优越性。

Improved GWO Algorithm for Flexible Job Shop Scheduling Problem

An improved neighborhood search gray wolf algorithm was designed for a flexible job shop scheduling problem with the objective of minimizing the maximum completion time.A two-layer encoding scheme was designed based on processes and machines suitable for the gray wolf algorithm,the population initialization strategy,the gray wolf mutation operation and the population update mechanism were improved.The global search neighborhood of the GWO algorithm was obtained by two-point crossover operation,insertion operation and PR operation,and then the forbidden search neighborhood was proposed to enhance the local exploitation capability of the GWO algorithm.Finally,the proposed algorithm was simulated and experimented on known examples and compared with other algorithms.The experimental results verify the superiority of the improved GWO algorithm.