Trust region based stochastic optimization with adaptive restart: A family of global optimization algorithms

Logan Mathesen, Giulia Pedrielli, Szu Hui Ng

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

2 Scopus citations


The field of simulation optimization has seen algorithms proposed for local optimization, drawing upon different locally convergent search methods. Similarly, there are numerous global optimization algorithms with differing strategies to achieve convergence. In this paper, we look specifically into meta-model based algorithms that stochastically drive global search through an optimal sampling criteria evaluated over a constructed meta-model of the predicted response considering the uncertainty of the response. We propose Trust Region Based Optimization with Adaptive Restart (TBOAR), a family of algorithms that dynamically restarts a trust region based search method via an optimal sampling criteria derived upon a meta-model based global search approach. Additionally, we propose a new sampling criteria to reconcile undesirable adaptive restart trajectories. This paper presents preliminary results showing the advantage of the proposed approach over the benchmark Efficient Global Optimization algorithm, focusing on a deterministic black box simulator with a d-dimensional input and a one-dimensional response.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781538634288
StatePublished - Jan 4 2018
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017


Other2017 Winter Simulation Conference, WSC 2017
Country/TerritoryUnited States
CityLas Vegas

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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