This article proposes a novel differential evolution algorithm based on dynamic multi-population (DEDMP) for solving the multi-objective flexible job shop scheduling problem. In DEDMP, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of the subpopulation is dynamically adjusted based on the last search experience. Furthermore, DEDMP is adaptive based on two search strategies, one with strong exploration ability and the other with strong exploitation ability. The selection probability of each search strategy is also dynamically adjusted according to the success rate. Furthermore, the proposed algorithm adopts newly designed mutation and crossover operators and it can directly generate feasible solutions in the search space. To evaluate the performance of DEDMP, DEDMP is compared with some state-of-the-art algorithms on benchmark instances. The experimental results show that DEDMP is better than or at least competitive with other outstanding algorithms.

为解决多目标柔性作业车间调度问题，提出了一种基于动态多种群（DEDMP）的差分进化算法。在DEDMP中，在每一代中，整个族群都通过聚类划分划分为几个子种群，并且根据最近的搜索经验来动态调整子种群的大小。此外，DEDMP是基于两种搜索策略的自适应方法，一种具有强大的探索能力，另一种具有强大的利用能力。每个搜索策略的选择概率也根据成功率动态调整。此外，该算法采用了新设计的变异和交叉算子，可以直接在搜索空间中生成可行的解。要评估DEDMP的性能，将DEDMP与基准实例上的一些最新算法进行了比较。实验结果表明，DEDMP优于或至少可以与其他出色算法竞争。