A decomposition-based many-objective evolutionary algorithm with adaptive weight vector strategy

Abstract

Evolutionary multi-objective optimization methods have become increasingly popular in finding a representative set of Pareto optimal solutions for multi-objective optimization problems (MOPs). As an important multi-objective optimization method, MOEA/D has been successfully applied to resolve various of MOPs. However, there still exist two issues in MOEA/D. First, the distribution of result set is greatly limited in complex Pareto Front (PF) shapes. Second, the effectiveness is relatively poor for solving many-objective optimization problems (MaOPs). Although many researchers attempt to improve MOEA/D, these two issues have not been solved well. This paper proposes a modified MOEA/D method called MOEA/D-VW which involves three optimization strategies. First, an adaptive weight vector adjustment strategy is adopted to fine-tune the subproblems, thus changing the evolution direction of the subpopulation. Second, a weight vector initialization strategy with preference parameters is added into MOEA/D-VW. Third, a modified crossover operator is leveraged to generate new population with good diversity and overcome the problem of high computational cost. Experiments are carried out on the set of 23 continuous optimization problems. The experimental results show that the performance and the distribution of the Pareto Set (PS) of MOEA/D-VW are effectively improved. Meanwhile, the experimental results demonstrate that the modified crossover operator can effectively reduce the computational cost of MOEA/D-VW.