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

In: High-Dimensional Optimization and Probability

Author

Listed:
  • Kody Kazda

    (Queen’s University)

  • Xiang Li

    (Queen’s University)

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.

Suggested Citation

  • Kody Kazda & Xiang Li, 2022. "Surrogate-Based Reduced-Dimension Global Optimization in Process Systems Engineering," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 341-357, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-00832-0_10
    DOI: 10.1007/978-3-031-00832-0_10
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