Expected improvement based infill sampling for global robust optimization of constrained problems

Samee Rehman, Matthijs Langelaar*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
62 Downloads (Pure)

Abstract

A novel adaptive sampling scheme for efficient global robust optimization of constrained problems is proposed. The method addresses expensive to simulate black-box constrained problems affected by uncertainties for which only the bounds are known, while the probability distribution is not available. An iterative strategy for global robust optimization that adaptively samples the Kriging metamodel of the computationally expensive problem is proposed. The presented approach is tested on several benchmark problems and the average performance based on 100 runs is evaluated. The applicability of the method to engineering problems is also illustrated by applying robust optimization on an integrated photonic device affected by manufacturing uncertainties. The numerical results show consistent convergence to the global robust optimum using a limited number of expensive simulations.

Original languageEnglish
Pages (from-to)723-753
JournalOptimization and Engineering
Volume18
Issue number3
DOIs
Publication statusPublished - 2017

Keywords

  • Efficient global optimization
  • Expected improvement
  • Kriging
  • Robust optimization

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