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Surrogate-Based Optimization Using Parametric Response Correction

In: Simulation-Driven Design by Knowledge-Based Response Correction Techniques

Author

Listed:
  • Slawomir Koziel

    (Reykjavik University, Engineering Optimization & Modeling Center)

  • Leifur Leifsson

    (Iowa State University, Department of Aerospace Engineering)

Abstract

In this chapter, we briefly describe several parametric response correction techniques and illustrate their application for solving design optimization problems in various engineering disciplines such as electrical engineering, antenna design, hydrodynamics, and aerodynamic shape optimization. Parametric response correction is simple to implement and it boils down to determining the surrogate model parameters using available data from both the low- and high-fidelity model (normally obtained from previous iterations of the optimization algorithm) and by evaluating specific (explicit) formulas and/or solving simple (usually linear) regression problems. A simple example of a parametric response correction is the AMMO algorithm (cf. Sect. 4.5.2 ), other examples can be found in Sect. 5.2 . Here, we focus on the methods working with vector-valued responses, such as output space mapping, manifold mapping, and multi-point response correction.

Suggested Citation

  • Slawomir Koziel & Leifur Leifsson, 2016. "Surrogate-Based Optimization Using Parametric Response Correction," Springer Books, in: Simulation-Driven Design by Knowledge-Based Response Correction Techniques, chapter 0, pages 75-98, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-30115-0_6
    DOI: 10.1007/978-3-319-30115-0_6
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