Exploiting gradient for kriging-based multi-objective aerodynamic optimization

Pramudita Satria Palar, Koji Shimoyama

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

Abstract

This paper introduces a multi-objective optimization framework for engineering design based on Kriging surrogate model when gradient information is available. Specifically, our interest here is to solve airfoil optimization problem via the use of computational fluid dynamics method. Here, the gradient is obtained via the adjoint method that computes the gradient for an arbitrary number of variables with a cost equal to or even less than one function evaluation. We applied the expected hypervolume improvement technique to add more solutions toward the discovery of Pareto front. The use of gradient-enhanced Kriging and expected hypervolume improvement eliminates the need for manual tuning of the weight as in the case of previous works regarding multi-objective airfoil optimization. The framework is demonstrated on the inviscid transonic airfoil optimization case with 2 and 12 design variables. The results show that the framework is highly efficient, outperforming non-gradient-based method in terms of optimization iteration. However, one has to be more careful when the actual gradient evaluation cost is counted. In our applications, the combination of gradient-enhanced Kriging and expected hypervolume improvement shows an advantage over the one with ordinary Kriging when the adjoint cost for two objectives is less than 20% of the function evaluation cost.

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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