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Simulation Optimization Through Regression or Kriging Metamodels

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High-Performance Simulation-Based Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 833))

Abstract

This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These methods use either linear regression or Kriging (Gaussian processes) metamodels. The metamodel guides the design of the experiment; this design fixes the input combinations of the simulation model. The linear-regression metamodel uses a sequence of local first-order and second-order polynomials—known as response surface methodology (RSM). Kriging models are global, but are re-estimated through sequential designs. “Robust” optimization may use RSM or Kriging, to account for uncertainty in simulation inputs.

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Acknowledgements

I thank Thomas Bartz-Beielstein for his very useful comments on the first version of this chapter.

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Authors and Affiliations

  1. Tilburg University, 90153, Tilburg, The Netherlands

    Jack P. C. Kleijnen

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  1. Jack P. C. Kleijnen
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Correspondence to Jack P. C. Kleijnen .

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Editors and Affiliations

  1. TH Köln, Cologne, Germany

    Thomas Bartz-Beielstein

  2. Jožef Stefan Institute, Ljubljana, Slovenia

    Bogdan Filipič

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Peter Korošec

  4. University Lille, Lille, France

    El-Ghazali Talbi

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Kleijnen, J.P.C. (2020). Simulation Optimization Through Regression or Kriging Metamodels. In: Bartz-Beielstein, T., Filipič, B., Korošec, P., Talbi, EG. (eds) High-Performance Simulation-Based Optimization. Studies in Computational Intelligence, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-18764-4_6

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