G-STAR: A new kriging-based trust region method for global optimization

Giulia Pedrielli, Szu Hui Ng

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

7 Scopus citations


Trust region methods are an efficient technique to identify good solutions when the sampling effort needs to be controlled due to the cost of running simulation. Meta-model based applications of trust region methods have already been proposed and their convergence has been characterized. Nevertheless, these approaches keep the strongly local characteristic of the original trust region method. This is not desirable in that information generated at local level are "lost" as the search progresses. A first consequence is that the search technique cannot guarantee global convergence. We propose a global version of the trust region method, the Global Stochastic Trust Augmented Region (G-STAR). The trust region is used to focus the simulation effort and balance between exploration and exploitation. Such an algorithm focuses the sampling effort in trust regions sequentially generated by adopting an extended Expected Improvement criterion. This paper presents the algorithm and the preliminary numerical results.

Original languageEnglish (US)
Title of host publication2016 Winter Simulation Conference
Subtitle of host publicationSimulating Complex Service Systems, WSC 2016
EditorsTheresa M. Roeder, Peter I. Frazier, Robert Szechtman, Enlu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781509044863
StatePublished - Jul 2 2016
Externally publishedYes
Event2016 Winter Simulation Conference, WSC 2016 - Arlington, United States
Duration: Dec 11 2016Dec 14 2016

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2016 Winter Simulation Conference, WSC 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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