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

Kody Kazda () and Xiang Li ()
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Kody Kazda: Queen’s University
Xiang Li: Queen’s University

A chapter in High-Dimensional Optimization and Probability, 2022, pp 341-357 from Springer

Abstract: 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.

Date: 2022
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DOI: 10.1007/978-3-031-00832-0_10

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