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采用了一种新颖的混合灰狼优化算法来求解置换流水线调度问题。针对标准灰狼优化算法在求解离散流水线车间调度问题时收敛速度慢的现象,并结合问题的特点,提出了改进的灰狼优化算法。为了避免非可行解的产生,在该改进算法中采用了随机键编码机制对工件位置进行编码,同时引入局部搜索策略以提高算法收敛能力,基于灰狼个体间的社会等级信息以最优3个狼指引其它个体到达最优解区域从而更新种群。通过最新标准测试集的仿真结果和算法比较验证了所提算法的有效性。
A novel hybrid gray wolf optimization algorithm is used to solve the displacement pipeline scheduling problem. Aiming at the problem that the standard gray wolf optimization algorithm converges slowly when solving the discrete-time shop scheduling problem, and based on the characteristics of the problem, an improved gray wolf optimization algorithm is proposed. In order to avoid the generation of non-feasible solutions, a random key coding mechanism is adopted in this improved algorithm to encode the position of the workpiece. At the same time, a local search strategy is introduced to improve the convergence ability of the algorithm. Based on the social hierarchy information of gray wolf individuals, The wolf guides other individuals to reach the optimal solution area to update the population. The validity of the proposed algorithm is verified by comparing the simulation results with the latest standard test set.