改进灰狼优化算法求解模糊车间调度问题.

Fuzzy Job-Shop scheduling problem is a classic embodiment of complex scheduling, and an excellent scheduling scheme designed for this problem can improve production efficiency. At present, the research on fuzzy Job-Shop scheduling problem mainly focused on single objective, this paper proposed an improved grey wolf optimization（IGWO） algorithm to solve the bi-objective fuzzy flexible Job-Shop scheduling problem to minimize the fuzzy completion time and the total load of fuzzy machines. This algorithm proposed a two-layer coding method to make IGWO discretization, designed a strategy based on HV contribution degree to improve population diversity. Then it used reinforcement learning method to determine global and local search parameters, improved two crossover operators to help individuals evolve in different update modes, and used two-level variable neighborhood and four replacement strategies to improve local search capability. Finally, this paper carried out several groups of experiments on several examples to verify the effectiveness of the improved strategy. In most test cases, the performance of IGWO algorithm is better than the comparison algorithm, with good convergence and distribution. [ABSTRACT FROM AUTHOR]