Abstract: A discrete wolf pack algorithm is proposed to simulate the specific characteristics of the permutation flow shop scheduling problem and simulate the hunting behavior of wolves in nature. Using the coding method based on the workpiece sequence, the opposition learning initializes the population to improve the convergence speed of the algorithm. The redesign of the wandering behavior, summoning behavior and siege behavior in the original wolf group algorithm makes the algorithm not easy to fall into local optimum. At the same time, the Taguchi experimental design method is used to analyze the sensitivity of the algorithm parameter settings, and the optimal parameter combination is determined. Finally, the discrete wolf pack algorithm is used to simulate and test the standard test sets of Car, Reeves and Taillard. Compared with other intelligent optimization algorithms, the feasibility of the proposed algorithm is verified. It provides a more effective method for solving the permutation flow shop scheduling problem.

Key words: Discrete wolf pack algorithm, swarm intelligence optimization algorithms, permutation flow shop scheduling problem, makespan

摘要： 针对置换流水车间调度问题的具体特性，模拟自然界中狼群捕猎行为设计了一种离散狼群算法。采用基于工件序列的编码方式，反向学习初始化种群提高算法收敛速度。对原始狼群算法中游走行为、召唤行为、围攻行为进行重新设计，使得算法不易陷入局部最优。同时，运用Taguchi试验设计方法对算法参数设置进行灵敏度分析，并确定出最优的参数组合。最后，运用离散狼群算法对Car、Reeves以及Taillard标准测试集进行仿真测试，与其他智能优化算法进行比较，验证了所提出算法的可行性，为求解置换流水车间调度问题提供了更加有效的一种方法。

关键词: 离散狼群算法, 群智能优化算法, 置换流水车间调度, 最小化最大完工时间