基于学习到的变量交互的高维多目标贝叶斯优化
High-dimensional Multi-objective Bayesian Optimization with Learned Variable Interactions
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中文总结 AI 辅助
本文提出ViaMOBO框架，通过变量交互分析划分高维决策空间并执行局部贝叶斯优化，经实验验证其在高维昂贵多目标问题的帕累托前沿近似上优于现有最优MOBO方法。
中文摘要 AI 辅助
多目标贝叶斯优化（MOBO）可有效针对昂贵黑盒问题识别帕累托前沿，但现有多数MOBO方法因指数级采样复杂度仅适用于低维决策空间。本文提出基于决策变量交互分析的MOBO框架ViaMOBO，用于求解高维决策空间下的昂贵多目标问题。ViaMOBO的核心思路是利用变量交互分析模型确定决策空间是否可完全或部分划分，再在划分后的决策子空间内执行局部贝叶斯优化。该模型无需强假设，可基于决策变量间的潜在独立或相互依赖关系，推导黑盒问题的目标是否可分、部分可分或不可分。我们在合成基准与真实基准上将ViaMOBO与当前最优MOBO方法对比，实验结果显示，在近似高维昂贵多目标问题的帕累托前沿时，ViaMOBO优于其他相关MOBO基线方法。
英文摘要
Multi-objective Bayesian optimization (MOBO) is effective in identifying the Pareto fronts for expensive black-box problems. However, most current MOBO approaches are limited to low-dimensional decision space due to its exponential sampling complexity. This paper presents decision variable interaction analysis-based MOBO, ViaMOBO, a generic framework for expensive multi-objective problems with high-dimensional decision space. The key idea of ViaMOBO is that it utilizes a variable interaction analysis model to determine whether the decision space can be completely or partially divided, and then performs local Bayesian optimization in the divided decision subspaces. Through the variable analysis model, it can be derived whether the objectives in black-box problems are separable, partially separable, or non-separable based on the potential independent or interdependent relationships among decision variables without any strong assumptions. We compare ViaMOBO with the state-of-the-art MOBO methods on both synthetic and real-world benchmarks. The experimental results demonstrate that ViaMOBO outperforms other related MOBO baselines in approximating the Pareto front of high-dimensional expensive multi-objective problems.
