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.

针对多目标柔性作业车间调度问题，本文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，整个种群被聚类划分为几个子种群，并根据最后的搜索经验动态调整子种群的大小。此外，DEDMP 基于两种搜索策略进行自适应，一种具有很强的探索能力，另一种具有很强的利用能力。每个搜索策略的选择概率也是根据成功率动态调整的。此外，该算法采用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解。为了评估 DEDMP 的性能，DEDMP 在基准实例上与一些最先进的算法进行了比较。实验结果表明，DEDMP 优于或至少与其他优秀算法具有竞争力。