Use of Reference Point Sets in a Decomposition-Based Multi-Objective Evolutionary Algorithm

In recent years, decomposition-based multi-objective evolutionary algorithms (MOEAs) have gained increasing popularity. However, these MOEAs depend on the consistency between the Pareto front shape and the distribution of the reference weight vectors. In this paper, we propose a decomposition-based MOEA, which uses the modified Euclidean distance (d(+)) as a scalar aggregation function. The proposed approach adopts a novel method for approximating the reference set, based on an hypercube-based method, in order to adapt the reference set for leading the evolutionary process. Our preliminary results indicate that our proposed approach is able to obtain solutions of a similar quality to those obtained by state-of-the-art MOEAs such as MOMBIII, NSGA-III, RVEA and MOEA/DD in several MOPs, and is able to outperform them in problems with complicated Pareto fronts.