Polynomial-chaos-kriging-assisted efficient global optimization

Pramudita Satria Palar, Koji Shimoyama

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

2 Citations (Scopus)

Abstract

In this paper, we explore the use of the recently proposed polynomial chaos-Kriging (PCK) surrogate model to assist a single-objective efficient global optimization (EGO) framework in order to solve expensive optimization problems. PCK is a form of universal Kriging (UK) that employs orthogonal polynomials and least-angle-regression (LARS) algorithm to select the proper set of polynomial basis. The use of LARS within the PCK algorithm eliminates the need for the manual selection of UK's trend function. Investigation on the capability of PCK-EGO is performed on five synthetic and one aerodynamic test problems. In light of the results, we observe that PCK-EGO performs in a similar way to standard EGO in cases with no clear polynomial-like trend. However, PCK-EGO shows a notable faster convergence in problems where the objective function exhibits a landscape trend that can be captured by polynomials. Application to the subsonic wing problem further demonstrates that PCK-EGO is more efficient than EGO in a real-world aerodynamic optimization problem.

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
ISBN (Electronic)9781538627259
DOIs
Publication statusPublished - 2018 Feb 2
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: 2017 Nov 272017 Dec 1

Publication series

Name2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
Volume2018-January

Other

Other2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period17/11/2717/12/1

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

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