Work

CoEGAN-BO: Synergistic Co-Evolution of GANs and Bayesian Optimization for High-Dimensional Expensive Many-Objective Problems

Year: 2025

Type: article

Abstract: Bayesian optimization (BO) struggles with data scarcity and poor scalability in high-dimensional many-objective optimization problems. To address this, we propose Co-Evolutionary GAN–Bayesian Optimization (CoEGAN-BO), a novel framework that synergizes generative adversarial networks (GANs) with Bayesian co-evolutionary search for data-driven optimization. The GAN module generates synthetic samples conditioned on promising regions identified by BO, while a co-evolutionary mechanism maintains two interacting populations: one explores the GAN’s latent space for diversity, and the other exploits BO’s probabilistic model for convergence. A bi-stage infilling strategy further enhances efficiency: early iterations prioritize exploration via Lp-norm-based candidate selection, later switching to a max–min distance criterion for Pareto refinement. Experiments on expensive multi/many-objective benchmarks show that CoEGAN-BO outperforms four state-of-the-art surrogate-assisted algorithms, achieving superior convergence and diversity under limited evaluation budgets.

Source: Mathematics

DOI: 10.3390/math13213444

Authors: Jie Tian, Hongli Bian, Yuyao Zhang, Xiaoxu Zhang, Hui Liu

Institutions: Shandong Women’s University, Shandong Normal University, Shandong Jianzhu University, University of Jinan

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province