Dynamic Grouping Crowding Differential Evolution with Ensemble of Parameters for Multi-modal Optimization


In recent years, multi-modal optimization has become an important area of active research. Many algorithms have been developed in literature to tackle multi-modal optimization problems. In this work, a dynamic grouping crowding differential evolution (DGCDE) with ensemble of parameter is proposed. In this algorithm, the population is dynamically regrouped into 3 equal subpopulations every few generations. Each of the subpopulations is assigned a set of parameters. The algorithms is tested on 12 classical benchmark multi-modal optimization problems and compared with the crowding differential evolution (Crowding DE) in literature. As shown in the experimental results, the proposed algorithm outperforms the Crowding DE with all three different parameter settings on the benchmark problems.