IEEE Trans Cybern. 2022 Mar;52(3):1602-1615. doi: 10.1109/TCYB.2020.2986600. Epub 2022 Mar 11.
In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multiobjective evolutionary algorithms. Maintaining a good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a DMOEA based on decision variable classification (DMOEA-DVC) is proposed in this article. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and changes response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. The experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms.
近年来，动态多目标优化问题（DMOPs）受到了越来越多的关注。许多动态多目标进化算法（DMOEAs）已经被提出，主要通过将多样性引入或预测方法与传统的多目标进化算法相结合来解决 DMOPs。保持种群多样性和收敛性的良好平衡对于 DMOEAs 的性能至关重要。针对上述问题，本文提出了一种基于决策变量分类的 DMOEA（DMOEA-DVC）。DMOEA-DVC 在静态优化和变化响应阶段将决策变量分别分为两组和三组。在静态优化中，DMOEA-DVC 使用两种不同的交叉算子对两组决策变量进行操作，以加速收敛，同时保持良好的多样性。在变化响应中，DMOEA-DVC 通过维护、预测和多样性引入策略分别对三组决策变量进行重新初始化。DMOEA-DVC 在 33 个基准 DMOPs 上与其他六种最先进的 DMOEAs 进行了比较。实验结果表明，DMOEA-DVC 的整体性能优于或可与比较算法相媲美。
