On multi-objective efficient global optimization via universal Kriging surrogate model

Pramudita Satria Palar, Koji Shimoyama

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

This paper investigates the capability of universal Kriging (UK), or Kriging with a trend, approximator enhanced with the efficient global optimization (EGO) method to solve expensive multi-objective design optimization problem. Engineering optimization problems typically can be well described with smooth and polynomial-like behavior, which is the main rationale to apply UK over the ordinary Kriging (OK) as the approximator. The UK with orthogonal polynomials and basis selection based on least-angle-regression is utilized for this purpose. Results and demonstration on three synthetic functions using expected hypervolume improvement (EHVI) and Euclidean-based expected improvement (EEI) criterions show the increased quality of the optimized non-dominated solutions when UK is coupled with EHVI criterion. On the other hand, the coupling of UK with EEI does not lead to any improvement and might produce an adverse effect. We also observed that the use of UK mainly improves the proximity to the true Pareto front, with smaller but notable effect on the diversity of the solutions when EHVI is applied as the criterion. As expected, optimization using the UK shows the greatest improvement if all objective functions can be sufficiently approximated by the UK. Based on the results, we suggest that coupling of UK and EHVI criterion is a potential approach to solve the expensive real-world multi-objective optimization problem.

本文言語English
ホスト出版物のタイトル2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ621-628
ページ数8
ISBN(電子版)9781509046010
DOI
出版ステータスPublished - 2017 7月 5
イベント2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain
継続期間: 2017 6月 52017 6月 8

出版物シリーズ

名前2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings

Other

Other2017 IEEE Congress on Evolutionary Computation, CEC 2017
国/地域Spain
CityDonostia-San Sebastian
Period17/6/517/6/8

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 信号処理

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