Local Neighborhood-Based Adaptation of Weights in Multi-Objective Evolutionary Algorithms Based on Decomposition

Multi-objective algorithms based on decomposition have become popular for the reason that a uniform distribution of weight vectors may result in a better distribution of solutions along the Pareto front. However, for more complex Pareto fronts with irregular shapes, the initial weight vectors may not be adequate. One alternative to overcome this problem, is to adapt the weight vectors during the evolutionary process. In this paper an adaptive version of Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is proposed to change the weight vectors based on the concept of local neighborhoods. The proposed method is called MOEA/D with local-neighborhood-based adaptation (MOEA/D-LNA). The proposed method is compared against a number of famous variants of MOEA/D in the literature. Initial experimental results have shown promising effectiveness on problems with irregular Pareto shapes.