Improved Solution Search Performance of Constrained MOEA/D Hybridizing Directional Mating and Local Mating

Abstract

In this study, we propose an improvement to the direct mating method, a constraint handling approach for multi-objective evolutionary algorithm with combining the direct mating and the local mating. The direct mating can maintain the information for the improvement of the design, and the local mating selects another parent from the feasible solution space around the initially selected parent. The direct mating method selects the other parent along the optimal direction in the objective space after the first parent is selected, even if it is infeasible. We evaluate the proposed method on three mathematical problems with unique Pareto fronts and one real-world application. We use the generation histories of the averages and standard deviations of the hypervolumes as the performance evaluation criteria. Our investigation results show that the proposed method can solve constraint multi-objective problems better than existing methods while maintaining high diversity.