论文标题
考虑输入不确定性的强大多目标贝叶斯优化框架
A Robust Multi-Objective Bayesian Optimization Framework Considering Input Uncertainty
论文作者
论文摘要
贝叶斯优化是对昂贵目标功能进行数据有效优化的流行工具。在工程设计等现实生活应用中，设计师通常希望考虑多个目标以及输入不确定性，以找到一组健壮的解决方案。虽然这是单目标贝叶斯优化中的一个活跃主题，但在多目标情况下，它较少研究。我们引入了一个新型的贝叶斯优化框架，以考虑输入不确定性有效地执行多目标优化。我们提出了一个强大的高斯工艺模型，以推断贝叶斯风险标准以量化鲁棒性，并开发了一个两阶段的贝叶斯优化过程来搜索稳健的帕累托边境。完整的框架支持输入不确定性的各种分布，并充分利用并行计算。我们通过数值基准证明了框架的有效性。
Bayesian optimization is a popular tool for data-efficient optimization of expensive objective functions. In real-life applications like engineering design, the designer often wants to take multiple objectives as well as input uncertainty into account to find a set of robust solutions. While this is an active topic in single-objective Bayesian optimization, it is less investigated in the multi-objective case. We introduce a novel Bayesian optimization framework to efficiently perform multi-objective optimization considering input uncertainty. We propose a robust Gaussian Process model to infer the Bayes risk criterion to quantify robustness, and we develop a two-stage Bayesian optimization process to search for a robust Pareto frontier. The complete framework supports various distributions of the input uncertainty and takes full advantage of parallel computing. We demonstrate the effectiveness of the framework through numerical benchmarks.
