  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.
