提出自适应ε约束分解法，高效实现多目标优化的均匀覆盖与约束处理。
Multi-Objective Bayesian Optimization via Adaptive \varepsilon-Constraints Decomposition
- 根据代理帕累托前沿的覆盖缺口，动态设定约束条件。
- 在合成与真实数据集上实现更优的帕累托覆盖和竞争性超体积表现。
- 适合需要均匀解分布和约束处理的复杂优化场景。
多目标贝叶斯优化（MOBO）为优化多个昂贵黑箱函数提供了一个合理框架。然而，现有MOBO方法常面临覆盖不均、可扩展性差以及约束与偏好处理困难的问题。本文提出STAGE-BO：通过分析代理帕累托前沿的覆盖情况，识别出未覆盖间隙最大的帕累托点，并以其坐标定义自适应ε约束，将问题转化为一系列不等式约束子问题，利用约束期望改进获取策略高效求解。该方法无需超体积计算即可实现均匀帕累托覆盖，自然支持约束与偏好。在合成与真实世界基准上的实验表明，其覆盖性能优于现有最优方法，且超体积表现具有竞争力。代码开源地址：https://github.com/YangYaohong1/STAGE-BO。
原文摘要 · Abstract (English)
Multi-objective Bayesian optimization (MOBO) provides a principled framework for optimizing multiple expensive black-box functions. However, existing MOBO methods often struggle with coverage, scalability, and handling constraints and preferences. In this work we propose STAGE-BO, Sequential Targeting Adaptive Gap-Filling $\varepsilon$-Constraint Bayesian Optimization: by analyzing the coverage of the surrogate Pareto front, our method identifies the Pareto front point with the largest uncovered gap, and uses its coordinates to define adaptive constraints in $\varepsilon$-constraint method, which transforms the problem into a sequence of inequality-constrained subproblems, efficiently solved via constrained expected improvement acquisition. Our approach provides uniform Pareto coverage without hypervolume computation and naturally handles constraints and preferences. Experiments on synthetic and real-world benchmarks demonstrate superior coverage and competitive hypervolume performance against state-of-the-art baselines. Our code implementation can be found at https://github.com/YangYaohong1/STAGE-BO.
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