Many-Objective Evolutionary Algorithm Based On Decomposition With Random And Adaptive Weights

Abstract

Decomposition-based evolutionary algorithms that work with an appropriate set of weights might obtain a quality final solution set in spite of the use of uniformly distributed and fixed weights that has two important limitations: it may fail depending on the problem geometry; and the population size is not flexible when dealing with Many-objective Problems (MaOPs). Recently proposed, the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/D-URAW) deals with these limitations using uniformly randomly weights generation method and weight adaptation based on the population sparsity. This paper validates this new approach, the MOEA/D-URAW, with state-of-the-art evolutionary algorithms in MaOPs, i.e., WFGI-WFG9 and MOKP with 5, 10 and 15 objectives. The results suggest the effectiveness of this approach.