An Algorithm Based on Differential Evolution for Multiobjective Problems

This paper presents a new multi-objective evolutionary algorithm based on differential evolution. The proposed approach adopts a secondary population in order to retain the non-dominated solutions found during the evolutionary process. Additionally, the approach also incorporates the concept of ε-dominance to get a good distribution of the solutions retained. The main goal of this work was to keep the fast convergence exhibited by Differential Evolution in global optimization when extending this heuristic to multiobjective optimization. We adopted standard test functions and performance measures reported in the specialized literature to validate our proposal. Our results are compared with respect to another multi-objective evolutionary algorithm based on differential evolution (Pareto Differential Evolution) and with respect to two approaches that are representative of the state-of-the-art in the area: the NSGA-II and ε-MOEA.