The successful application of multi-objective evolutionary algorithms (MOEAs) in many kinds of multiobjective problems have attracted considerable attention in recent years. In this paper, an adaptive multi-objective evolutionary algorithm is proposed by incorporating the concepts of the grid system (denoted as AGMOEA). Based on grid, the objective space is divided into subspaces. Based on the quality and dominance relationship between subspaces, the evolutionary opportunities are dynamically allocated to different subspaces with an adaptive selection strategy. To improve the evolutionary efficiency, the evolutionary scheme and an external archive mechanism considering representative individuals are proposed. The experimental results on 21 benchmark problems demonstrate that the proposed algorithm is competitive or superior to the rival algorithms.

近年来，多目标进化算法（MOEA）在许多多目标问题中的成功应用引起了相当大的关注。本文通过结合网格系统（AGMOEA）的概念，提出了一种自适应的多目标进化算法。基于网格，目标空间分为子空间。基于子空间之间的质量和优势关系，通过自适应选择策略将进化机会动态分配给不同的子空间。为了提高进化效率，提出了具有代表性的进化方案和外部存档机制。在21个基准测试问题上的实验结果表明，该算法具有竞争优势或优于竞争对手的算法。