Kriging surrogate model enhanced by coordinate transformation of design space based on eigenvalue decomposition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

The Kriging surrogate model, which is frequently employed to apply evolutionary computation to real-world problems, with coordinate transformation of design space is proposed to improve the approximation accuracy of objective functions with correlated design variables. Eigenvalue decomposition is used to extract significant trends in the objective function from its gradients and identify suitable coordinates. Comparing with the ordinary Kriging model, the proposed method shows higher accuracy in the approximation of twodimensional test functions and reduces the computational cost to achieve the global optimization. In the application to an airfoil design problem with spline curves as correlated design variables, the proposed method achieves better performances not only in the approximation accuracy but also the ability to explore the optimal solution.

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 8th International Conference, EMO 2015, Proceedings
EditorsAntónio Gaspar-Cunha, Carlos Henggeler Antunes, Carlos A. Coello Coello
PublisherSpringer-Verlag
Pages321-335
Number of pages15
ISBN (Electronic)9783319159331
DOIs
Publication statusPublished - 2015 Jan 1
Event8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015 - Guimarães, Portugal
Duration: 2015 Mar 292015 Apr 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9018
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015
CountryPortugal
CityGuimarães
Period15/3/2915/4/1

Keywords

  • Airfoil design
  • Efficient global optimization
  • Eigenvalue decomposition
  • Kriging model
  • Spline curve

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Namura, N., Shimoyama, K., & Obayashi, S. (2015). Kriging surrogate model enhanced by coordinate transformation of design space based on eigenvalue decomposition. In A. Gaspar-Cunha, C. H. Antunes, & C. A. C. Coello (Eds.), Evolutionary Multi-Criterion Optimization - 8th International Conference, EMO 2015, Proceedings (pp. 321-335). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9018). Springer-Verlag. https://doi.org/10.1007/978-3-319-15934-8_22