Enhancing distributed differential evolution with multicultural migration for global numerical optimization

Differential evolution (DE) is a prominent stochastic optimization technique for global optimization. After its original definition in 1995, DE frameworks have been widely researched by computer scientists and practitioners. It is acknowledged that structuring a population is an efficient way to enhance the algorithmic performance of the original, single population (panmictic) DE. However, only a limited amount of work focused on Distributed DE (DDE) due to the difficulty of designing an appropriate migration strategy. Since a proper migration strategy has a major impact on the performance, there is a large margin of improvement for the DDE performance. In this paper, an enhanced DDE algorithm is proposed for global numerical optimization. The proposed algorithm, namely DDE with Multicultural Migration (DDEM) makes use of two migration selection approaches to maintain a high diversity in the subpopulations, Target Individual Based Migration Selection (TIBMS) and Representative Individual Based Migration Selection (RIBMS), respectively. In addition, the diversity amongst the individuals is controlled by means of the proposed Affinity Based Replacement Strategy (ABRS) mechanism. Numerical experiments have been performed on 34 diverse test problems. The comparisons have been made against DDE algorithms using classical migration strategies and three popular DDE variants. Experimental results show that DDEM displays a better or equal performance with respect to its competitors in terms of the quality of solutions, convergence, and statistical tests.