Solving multi-objective optimization problems using differential evolution and a maximin selection criterion

In this paper, we propose a new selection operator (based on a maximin scheme and a clustering technique), which is incorporated into a differential evolution algorithm to solve multi-objective optimization problems. The resulting algorithm is called Maximin-Clustering Differential Evolution (MCDE) and, is validated using standard test problems and performance measures taken from the specialized literature. Our preliminary results indicate that MCDE is able to outperform NSGA-II and that is competitive with a hypervolume-based approach (SMS-EMOA), but at a significantly lower computational cost.