This repository includes the code for the paper “Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models”, which has been accepted at AAAI 2025. The preprint is at: https://arxiv.org/abs/2405.08674
- 🎉🎉🎉 [Dec 10, 2024] Our paper is accepted at AAAI 2025! The camera ready version is coming soon.
CDM-PSL (Composite Diffusion Model-Based Pareto Set Learning) is a novel algorithm designed to address the challenges of expensive multi-objective Bayesian optimization (EMOBO). While MOBO has shown great potential in solving expensive multi-objective optimization problems (EMOPs), it struggles to model complex distributions of the Pareto optimal solutions when only a limited number of function evaluations are available.
Existing Pareto set learning methods often exhibit instability in expensive scenarios, leading to significant deviations between the obtained solution set and the true Pareto set (PS). To overcome these challenges, CDM-PSL introduces Composite Diffusion Model to enhance optimization performance.
The source code for CDM-PSL will be released after the AAAI 2025 conference presentation.
If you find our work helpful, please cite the following BibTeX entry:
@article{li2024expensive,
title={Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models},
author={Li, Bingdong and Di, Zixiang and Lu, Yongfan and Qian, Hong and Wang, Feng and Yang, Peng and Tang, Ke and Zhou, Aimin},
journal={accepted by AAAI},
year={2025}
}