COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information
COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information
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DOI：
10.1016/j.compchemeng.2022.107700
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
J. Paulson;Congwen Lu
中科院分区：
文献类型：
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作者：
J. Paulson;Congwen Lu
许多工程问题涉及计算昂贵的模型，衍生信息是不容易获得的优化。贝叶斯优化（BO）框架是解决这些问题的一种特别有前途的方法，它使用高斯过程（GP）模型和预期的效用函数来系统地权衡设计空间的开发和探索。然而，BO从根本上受到黑盒模型假设的限制，该假设没有考虑任何潜在的问题结构。在本文中，我们提出了一种新的算法，COBALT，约束灰箱优化问题，结合多元GP模型与一种新的约束预期效用函数，其结构可以利用国家的最先进的非线性规划求解器。COBALT相比，传统的BO的七个测试问题，包括校准的基因组规模的生物反应器模型的实验数据。总体而言，COBALT在无约束和约束测试问题上都表现出非常有前途的性能。
Many engineering problems involve the optimization of computationally expensive models for which derivative information is not readily available. The Bayesian optimization (BO) framework is a particularly promising approach for solving these problems, which uses Gaussian process (GP) models and an expected utility function to systematically tradeoff between exploitation and exploration of the design space. BO, however, is fundamentally limited by the black-box model assumption that does not take into account any underlying problem structure. In this paper, we propose a new algorithm, COBALT, for constrainedgrey-boxoptimization problems that combines multivariate GP models with a novel constrained expected utility function whose structure can be exploited by state-of-the-art nonlinear programming solvers. COBALT is compared to traditional BO on seven test problems including the calibration of a genome-scale bioreactor model to experimental data. Overall, COBALT shows very promising performance on both unconstrained and constrained test problems.