Summary of Expensive Multi-objective Bayesian Optimization Based on Diffusion Models, by Bingdong Li et al.
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
by Bingdong Li, Zixiang Di, Yongfan Lu, Hong Qian, Feng Wang, Peng Yang, Ke Tang, Aimin Zhou
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to multi-objective Bayesian optimization (MOBO) for expensive multi-objective optimization problems (EMOPs). The Composite Diffusion Model-based Pareto Set Learning algorithm (CDM-PSL) addresses the issue of instability in existing Pareto set learning algorithms. CDM-PSL combines unconditional and conditional diffusion models to generate high-quality samples, and incorporates an information entropy based weighting method to balance different objectives. This approach ensures that all objectives are given due consideration during optimization. Experimental results on synthetic benchmarks and real-world problems demonstrate superior performance of CDM-PSL compared to state-of-the-art MOBO algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers find the best solutions when they have many goals. They use a new way called CDM-PSL, which combines different methods to make good choices. The goal is to balance all these goals and not get stuck on one. They tested this method on fake data and real-world problems, and it did better than other ways of doing the same thing. |
Keywords
» Artificial intelligence » Diffusion model » Optimization
