求解作业车间调度问题的改进混合灰狼优化算法*

灰狼优化算法(GWO)是目前一种比较新颖的群智能优化算法,具有收敛速度快,寻优能力强等优点。本文将灰狼优化算法用于求解复杂的作业车间调度问题,与布谷鸟搜索算法进行比较研究,验证了标准GWO算法求解经典作业车间调度问题的可行性和有效性。在此基础上,针对复杂作业车间调度问题难以求解的特点,对标准GWO算法进行改进,通过进化种群动态、反向学习初始化种群,以及最优个体变异等三个方面的改进操作,测试结果表明改进后的混合灰狼优化算法能够有效跳出局部最优值,找到更好的解,并且结果鲁棒性更强。

Solving job-shop scheduling problem using an improved hybrid grey wolf optimizer

Grey wolf optimizer is currently one of the latest proposed Swarm Intelligence algorithms with the advantages of fast convergence rate and better optimization performance. Firstly the original GWO algorithm is benchmarked on 11 well-known job-shop scheduling test instances, and the results are verified by a comparative study with Cuckoo Search (CS). The results show that the GWO algorithm is able to provide very competitive results compared to CS. In order to solve complex and large scale job-shop scheduling problems, an improved hybrid grey wolf optimizer (IGWO) is proposed by using evolutionary population dynamics (EPD) method, opposition-based learning strategy and mutation operator. The proposed IGWO algorithm is then benchmarked on seven large scale test instances. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm for solving production scheduling problem in terms of exploration, local optima avoidance, exploitation and robustness.