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2022 | OriginalPaper | Buchkapitel

Surrogate-Based Reduced-Dimension Global Optimization in Process Systems Engineering

verfasst von: Kody Kazda, Xiang Li

Erschienen in: High-Dimensional Optimization and Probability

Verlag: Springer International Publishing

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Abstract

High dimensional global optimization problems arise frequently in process systems engineering. This is a result of the complex mechanistic relationships that describe process systems, and/or their large-scale nature. High dimensional optimization problems can often be more easily solved by instead solving a sequence of reduced-dimension subproblems. Surrogate models can allow the formulation of reduced-dimension subproblems by approximating the key features of the original model. Surrogate-based optimization (SBO) is to use surrogate modeling to solve a sequence of approximate reduced-dimension subproblems, in order to converge to a high quality solution to the original high dimensional problem. Here we review the key characteristics of SBO frameworks and their application to process systems optimization problems.
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Metadaten
Titel
Surrogate-Based Reduced-Dimension Global Optimization in Process Systems Engineering
verfasst von
Kody Kazda
Xiang Li
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-031-00832-0_10

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