“Large-scale multiobjective optimization problems (LSMOPs) are characterized as optimization problems involving hundreds or even thousands of decision variables and multiple conflicting objectives. To solve LSMOPs, some algorithms designed a variety of strategies to track Pareto-optimal solutions (POSs) by assuming that the distribution of POSs follows a low-dimensional manifold. However, traditional genetic operators for solving LSMOPs have some deficiencies in dealing with the manifold, which often results in poor diversity, local optima, and inefficient searches. In this work, a generative adversarial network (GAN)-based manifold interpolation framework is proposed to learn the manifold and generate high-quality solutions on the manifold, thereby improving the optimization performance of evolutionary algorithms. We compare the proposed approach with several state-of-the-art algorithms on various large-scale multiobjective benchmark functions. The experimental results demonstrate that significant improvements have been achieved by the proposed framework in solving LSMOPs.”

中文翻译：

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CIS 出版物聚焦 [出版物聚焦]


“大规模多目标优化问题（LSMOP）的特点是涉及数百甚至数千个决策变量和多个相互冲突的目标的优化问题。为了解决 LSMOP，一些算法设计了多种策略来跟踪帕累托最优解 (POS)，假设 POS 的分布遵循低维流形。然而，用于求解LSMOP的传统遗传算子在处理流形方面存​​在一些缺陷，这常常导致多样性差、局部最优和低效搜索。在这项工作中，提出了一种基于生成对抗网络（GAN）的流形插值框架来学习流形并在流形上生成高质量的解决方案，从而提高进化算法的优化性能。我们将所提出的方法与各种大规模多目标基准函数上的几种最先进的算法进行比较。实验结果表明，所提出的框架在解决 LSMOP 方面取得了显着的改进。”