Supply chain optimization using migration differential evolution ensemble

The optimization of a supply chain is a very hard problem for classical optimization methods. Some evolutionary algorithms have been used to deal with supply chain problem recently. In this paper, Migration Differential Evolution (MDE) is proposed by imitating nomadic migration for this supply chain problem, and an ensemble method based on different Differential Evolutions (DEs) has been presented, which not only avoids the premature convergence, but also improves its global search capability. Finally, it is applied to deal with supply chain optimization problem. Experiments show that it achieves better or comparable results as compared to the performance of approaches in the literature.