CIS Publication Spotlight [Publication Spotlight]

Yongduan Song*, Dongrui Wu, Carlos A.Coello Coello, Georgios N. Yannakakis, Huajin Tang, Yiu Ming Cheung

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

“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.”
Original languageEnglish
Pages (from-to)24-26
Number of pages3
JournalIEEE Computational Intelligence Magazine
Volume19
Issue number1
DOIs
Publication statusPublished - 1 Feb 2024

Scopus Subject Areas

  • Theoretical Computer Science
  • Artificial Intelligence

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