关键词: 自适应, 变异算子, 邻域值, 支配关系, 多目标

Abstract: When solving multiobjective problems, Multiobjective Evolutionary Algorithm based on Decomposition（MOEA/D） is simple and effective. But most MOEA/D use fixed control parameters. It will lead to poor ability of global search and make it difficult to balance convergence and diversity. To solve these problems, a multiobjective optimization algorithm based on adaptive mutation operator and neighborhood value is proposed in this paper. The algorithm adjusts the mutation operator adaptively based on the degree of dispersion or concentration of individual fitness values in the population. It can enhance the global search ability of the algorithm. The size of neighborhood is adjusted adaptively according to the stages of evolution and the degree of concentration of individual fitness values. Each individual has a neighborhood size in each generation. The number of individuals are counted, which dominate the individual corresponding to the subproblem in the neighborhood of subproblem. If this number exceeds the set upper limit, the Pareto dominance relationship will be also used as one of the criteria for judging the individual quality in the neighborhood. The proposed algorithm is compared with traditional MOEA/D in the standard test problem. The result shows that the solution set obtained by the proposed algorithm not only has better convergence and diversity, but also has a competitive performance in solving multiobjective problems.

Key words: adaptive, mutation operator, neighborhood size, domination relationship, multiobjective