Combined kriging surrogate model for efficient global optimization using the optimal weighting method

Tanguy Appriou, Koji Shimoyama

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

Abstract

When solving design optimization problems using evolutionary algorithms, the optimization process can be computationally expensive. To accelerate the optimization process, ordinary Kriging (OK) surrogate models are often used with the efficient global optimization (EGO) framework. However, in some cases the EGO framework can lead to a globally inaccurate OK surrogate model when many sample points are close to each other. One way to tackle this issue is to use a regression OK model instead of an interpolation OK model. In this paper, we propose an interpolation method which solve the issue by combining a local and a global OK model fitted to different set of the sample points. This paper describes the optimal weighting method used to combine the different Kriging models and compares the performance of the new method to interpolation and regression OK for the modified Branin test function. We find that when many sample points exist close to each other, the combined Kriging method outperform both the interpolation and the regression OK.

Original languageEnglish
Title of host publicationGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages29-30
Number of pages2
ISBN (Electronic)9781450371278
DOIs
Publication statusPublished - 2020 Jul 8
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: 2020 Jul 82020 Jul 12

Publication series

NameGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020
CountryMexico
CityCancun
Period20/7/820/7/12

Keywords

  • Combined kriging
  • Efficient global optimization (EGO)
  • Kriging surrogate model
  • Optimal weighting method

ASJC Scopus subject areas

  • Computational Mathematics

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