On the Use of Random Weights in MOEA/D


MOEA/D is a decomposition-based multiobjective evolutionary algorithm that has attracted much attention in recent years. Its performance depends on the setting of weight vectors which are used for defining subproblems. In the case of irregular Pareto fronts (e.g, disconnected or degenerated), fixed setting of weight vectors in MOEA/D may not work well. In this paper, we propose an improved MOEA/D with both random and fixed weight vectors. Moreover, an external archive based on a modified ε-dominance strategy is used for storing nondominated solutions found by the proposed algorithm and assisting the generation of random weight vectors. Some experiments have been conducted to verify the efficiency and effectiveness of the improved MOEA/D on benchmark multiobjective test problems with irregular Pareto fronts. The experimental results show that the overall performance of the proposed algorithm is better than baseline MOEA/D and NSGA-II.