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

Jie Tian (), Hongli Bian, Yuyao Zhang, Xiaoxu Zhang and Hui Liu
Additional contact information
Jie Tian: School of Data and Computer Science, Shandong Women’s University, No. 2399 University Road, Jinan 250300, China
Hongli Bian: School of Computer Science and Engineering, Shandong Normal University, No. 1 University Road, Jinan 250014, China
Yuyao Zhang: School of Computer Science and Technology, Shandong Jianzhu University, No. 1000 Fengming Road, Jinan 250101, China
Xiaoxu Zhang: School of Information Science and Engineering, University of Jinan, No. 336 Nanxinzhuang West Road, Jinan 250022, China
Hui Liu: School of Data and Computer Science, Shandong Women’s University, No. 2399 University Road, Jinan 250300, China

Mathematics, 2025, vol. 13, issue 21, 1-22

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 L p -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.

Keywords: high-dimensional expensive many-objective problems; Bayesian optimization; co-evolution; generative adversarial networks; infill criterion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/21/3444/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/21/3444/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:21:p:3444-:d:1782118

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().