An Adaptive Gaussian Process-Based Search for Stochastically Constrained Optimization via Simulation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)1718-1729
Number of pages12
Journal / PublicationIEEE Transactions on Automation Science and Engineering
Issue number4
Online published26 Aug 2020
Publication statusPublished - Oct 2021


Simulation optimization (SO) techniques show a strong ability to solve large-scale problems. In this article, we concentrate on stochastically constrained SO. There are some challenges to tackle the problem: 1) the objective and constraints have no analytical forms and need to be evaluated via simulation; 2) we should make a tradeoff between exploiting around the best solution and exploring more unknown regions; and 3) both the objective value and feasibility determine the quality of a solution. Motivated by these issues, we propose an adaptive Gaussian process-based search (AGPS) to address stochastically constrained discrete SO problems. AGPS fast constructs the Gaussian process for each performance and then builds a new sampling distribution to adaptively balance exploration and exploitation considering the objective function and stochastic constraints. We show that AGPS converges to the set of globally optimal solutions with probability one. Numerical experiments demonstrate the superiority of our method compared with other advanced approaches.

Research Area(s)

  • Adaptation models, Discrete optimization via simulation (DOvS), Gaussian process (GP)-based search, Gaussian processes, Global Positioning System, Optimization, Search problems, stochastic constraint, Sun