Large-scale multiobjective optimization problems (LSMOPs) are characterized as involving hundreds or even thousands of decision variables and multiple conflicting objectives. An excellent algorithm for solving LSMOPs should find Pareto-optimal solutions with diversity and escape from local optima in the large-scale search space. Previous research has shown that these optimal solutions are uniformly distributed on the manifold structure in the low-dimensional space. However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting 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 this manifold, thereby improving the performance of evolutionary algorithms. We compare the proposed algorithm with several state-of-the-art algorithms on large-scale multiobjective benchmark functions. Experimental results have demonstrated the significant improvements achieved by this framework in solving LSMOPs.

中文翻译：

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通过生成对抗网络进行大规模多目标优化的流形插值

大规模多目标优化问题（LSMOP）的特征是涉及数百甚至数千个决策变量和多个相互冲突的目标。求解LSMOP的优秀算法应找到具有多样性的帕累托最优解，并避免大规模搜索空间中的局部最优解。先前的研究表明，这些最优解在低维空间中的流形结构上均匀分布。但是，用于解决LSMOP的传统进化算法在处理这种结构流形时存在一些缺陷，导致多样性差，局部最优和搜索效率低下。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，以学习流形并在该流形上生成高质量的解决方案，从而提高了进化算法的性能。我们将提出的算法与大型多目标基准函数上的几种最新算法进行了比较。实验结果表明，该框架在解决LSMOP方面取得了显着改善。