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

• URL: http://arxiv.org/abs/2101.02932v1
• Date: Fri, 8 Jan 2021 09:38:38 GMT
• Title: Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks
• Authors: Zhenzhong Wang and Haokai Hong and Kai Ye and Min Jiang and Kay Chen Tan
• Abstract summary: Large-scale multiobjective optimization problems (LSMOPs) are characterized as involving hundreds or even thousands of decision variables and multiple conflicting objectives. Previous research has shown that these optimal solutions are uniformly distributed on the manifold structure in the low-dimensional space. In this work, a generative adversarial network (GAN)-based manifold framework is proposed to learn the manifold and generate high-quality solutions.
• Score: 12.18471608552718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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