Exploiting gradient for kriging-based multi-objective aerodynamic optimization

Pramudita Satria Palar, Koji Shimoyama

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-8
ページ数8
ISBN(電子版)9781538627259
DOI
出版ステータスPublished - 2018 2 2
イベント2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
継続期間: 2017 11 272017 12 1

出版物シリーズ

名前2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
2018-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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