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Large-scale multiobjective optimization problems (LSMOPs) are characterizedas involving hundreds or even thousands of decision variables and multipleconflicting objectives. An excellent algorithm for solving LSMOPs should findPareto-optimal solutions with diversity and escape from local optima in thelarge-scale search space. Previous research has shown that these optimalsolutions are uniformly distributed on the manifold structure in thelow-dimensional space. However, traditional evolutionary algorithms for solvingLSMOPs have some deficiencies in dealing with this structural manifold,resulting in poor diversity, local optima, and inefficient searches. In thiswork, a generative adversarial network (GAN)-based manifold interpolationframework is proposed to learn the manifold and generate high-quality solutionson this manifold, thereby improving the performance of evolutionary algorithms.We compare the proposed algorithm with several state-of-the-art algorithms onlarge-scale multiobjective benchmark functions. Experimental results havedemonstrated the significant improvements achieved by this framework in solvingLSMOPs.

Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks