发表机构
Imperial College London（伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高维昂贵黑盒函数，提出降空间多保真贝叶斯优化框架RS-MFBO，结合全局敏感性分析和保真度增强高斯过程，在工业模拟器上显著减少高保真评估次数并保持优化性能。
AI 中文摘要
优化工业过程流程图通常因严格模拟的高成本和复杂设计空间中固有的维度灾难而在计算上令人望而却步。为了解决这些挑战，我们提出了一种降空间多保真贝叶斯优化（RS-MFBO）框架，专为高维、昂贵的黑盒函数设计。该方法将全局敏感性分析（GSA）用于降维，与一个增强保真度的高斯过程相结合，该过程捕捉低成本近似与昂贵高保真评估之间的相关性。一种成本感知的采集策略，辅以冷却和晋升机制，自适应地指导样本在不同保真度之间的分配。该框架在两个不同的工业过程模拟器上得到验证：SuperPro Designer中的质粒DNA生物过程和Aspen HYSYS中的绿色燃料合成工厂。跨多种经济和物理目标的结果表明，与单保真基线相比，所提出的方法在保持竞争性优化性能的同时，大幅减少了高保真模拟器评估的次数。这些结果凸显了RS-MFBO作为一种可扩展、模拟器无关的方法，适用于成本受限的黑盒优化。
英文摘要
Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address these challenges, we present a reduced-space multi-fidelity Bayesian optimization (RS-MFBO) framework designed for high-dimensional, expensive black-box functions. The approach integrates Global Sensitivity Analysis (GSA) for dimensionality reduction with a fidelity-augmented Gaussian process that captures correlations between low-cost approximations and expensive high-fidelity evaluations. A cost-aware acquisition strategy, augmented with cooldown and promotion mechanisms, adaptively guides the allocation of samples across fidelities. The framework is validated on two distinct industrial process simulators: a plasmid DNA bioprocess in SuperPro Designer and a green fuel synthesis plant in Aspen HYSYS. Results across diverse economic and physical objectives demonstrate that the proposed method substantially reduces the number of high-fidelity simulator evaluations while maintaining competitive optimization performance compared to single-fidelity baselines. These results highlight RS-MFBO as a scalable, simulator-agnostic approach for cost-constrained black-box optimization.
CommentsAccepted at the 20th Learning and Intelligent Optimization Conference (LION 20), 2026. Corrected author version. This version corrects a typo in the mathematical description of the multi-fidelity covariance kernel in Section 3.2
