基于灰狼优化算法的置换流水线车间调度

Permutation Flow-shop Scheduling Based on the Grey Wolf Optimizer

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。

This paper presented a hybrid grey wolf optimizer to solve the permutation flow-shop scheduling.The standard grey wolf optimizer was utilized to solve the discrete flow shop scheduling problem,which will lead convergence speed very slow.Meanwhile,according to the characteristics of the permutation flow shop scheduling,an improved grey wolf optimizer was proposed.To avoid unfeasible solutions generated by the grey wolf optimizer,the proposed algorithm used a random key based on largest order value for coding job positions.To enhance the convergence performance of the proposed algorithm,a local search strategy was also introduced into the proposed algorithm.The search process in wolves was guided by the first best three wolves,which corresponds to the three good fitness values for updating population.The experimental simulations and comparisons of the new benchmarks demonstrated the validity of the proposed method.