Ma Lianbo, Huang Min, Yang Shengxiang, Wang Rui, Wang Xingwei
IEEE Trans Cybern. 2022 Jul;52(7):6684-6696. doi: 10.1109/TCYB.2020.3041212. Epub 2022 Jul 4.
This article proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large-scale multiobjective and many-objective optimization problems (MaOPs). Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large-scale multiobjective and MaOPs.
本文提出了一种基于分解框架的自适应局部决策变量分析方法，用于解决大规模多目标和多目标优化问题（MaOPs）。其主要思想是将参考向量的引导纳入控制变量分析，并采用自适应策略优化决策变量。特别是，在控制变量分析中，对于每个搜索方向，通过基于投影的检测方法测量每个决策变量的收敛相关度。在决策变量优化中，采用自适应标量化策略对分组决策变量进行优化，该策略能够在目标空间中自适应地平衡解的收敛性和多样性。使用一组具有2-10个目标和200-1000个变量的测试问题对所提出的算法进行了评估。实验结果验证了所提出算法在大规模多目标和MaOPs上的有效性和效率。
