On the Low-Discrepancy Sequences and their Use in MOEA/D for High-Dimensional Objective Spaces


In spite of the success of the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the generation of weights for problems having many objectives, continues to be an open research problem. In this paper, we introduce a new methodology based on low-discrepancy sequences to generate the weights vectors employed by MOEA/D. We analyze and compare the proposed methodology using different low-discrepancy sequences and its impact in the search process of MOEA/D. The proposed approach is evaluated in problems having many objective functions (up to 15 objectives). We show the flexibility and ease of use of this type of sequences when adopting them to generate the weights of MOEA/D.