MF-SCBO：多保真度可扩展约束贝叶斯优化

中文摘要 AI 辅助

许多现实世界的优化问题依赖于昂贵的模拟或实验，因此有效利用可用数据至关重要。随着机器学习、工程和控制等应用中目标评估成本的持续上升，在受黑箱约束的高维黑箱函数上进行多保真度优化变得越来越重要。据我们所知，现有方法无法同时处理高维性、黑箱约束、任意数量的保真度级别以及非嵌套采样。在本工作中，我们将可扩展约束贝叶斯优化方法扩展到多保真度设置，从而提出了MF-SCBO方法。所提出的方法在标准基准函数以及具有挑战性的问题上进行了评估。实验结果表明，在高维和约束设置下，MF-SCBO通常比单保真度SCBO和所考虑的其他多保真度方法实现更好的收敛性。

英文摘要

Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity optimization of high-dimensional black-box functions subject to black-box constraints is increasingly relevant as the cost of objective evaluations continues to rise in applications such as machine learning, engineering, and control. To our knowledge, no existing method simultaneously addresses high-dimensionality, black-box constraints, an arbitrary number of fidelity levels, and non-nested sampling. In this work, we extend the Scalable Constrained Bayesian Optimization method to the multi-fidelity setting, resulting in the MF-SCBO method. The proposed approach is evaluated on standard benchmark functions as well as challenging problems. The experimental results demonstrate that MF-SCBO generally achieves better convergence than both the single-fidelity SCBO and the other multi-fidelity method considered in this high-dimensional and constrained settings.

发表机构

• Université Côte d’Azur（蔚蓝海岸大学）
• Inria（法国国家信息与自动化研究所）
• CNRS（法国国家科学研究中心）

机构由 AI 辅助整理，请以论文原文为准。