关键词: 飞蛾火焰算法, 作业车间调度问题, 拟反向学习策略, 排序配对学习策略, 邻域搜索策略

Abstract:

In order to decrease the probability of local optima stagnation and improve the population diversity, a hybrid moth flame algorithm with learning strategy and neighborhood search is proposed. The quasi-oppositional learning strategy is embedded into the flame updating process, which is helpful for the flame to jump out of the local optima and has a higher chance to be closer to the unknown optimal solution. Then, moths are divided into two subgroups according to their fitness values. One of the groups is updated by ranking paired learning strategy to realize the information exchange between individuals, and another group is updated by neighboring search strategy to increase the diversity of the population. This kind of parallel computing can improve the quality of the whole population more quickly. The CEC2017 test function set is selected for numerical experiment, and the test results and statistical analysis show that the proposed algorithm has higher accuracy and stronger robustness. Finally, the proposed algorithm is applied to solve the benchmark instances in OR-Library, the experimental results verify that the proposed algorithm is effective while solving the job shop scheduling problem.

Key words: moth flame algorithm, job shop scheduling problem, quasi-oppositional learning, ranking paired learning, neighboring search