Interval uncertainty propagation by a parallel Bayesian global optimization method

authored by

Chao Dang, Pengfei Wei, Matthias G.R. Faes, Marcos A. Valdebenito, Michael Beer

Abstract

This paper is concerned with approximating the scalar response of a complex computational model subjected to multiple input interval variables. Such task is formulated as finding both the global minimum and maximum of a computationally expensive black-box function over a prescribed hyper-rectangle. On this basis, a novel non-intrusive method, called ‘triple-engine parallel Bayesian global optimization’, is proposed. The method begins by assuming a Gaussian process prior (which can also be interpreted as a surrogate model) over the response function. The main contribution lies in developing a novel infill sampling criterion, i.e., triple-engine pseudo expected improvement strategy, to identify multiple promising points for minimization and/or maximization based on the past observations at each iteration. By doing so, these identified points can be evaluated on the real response function in parallel. Besides, another potential benefit is that both the lower and upper bounds of the model response can be obtained with a single run of the developed method. Four numerical examples with varying complexity are investigated to demonstrate the proposed method against some existing techniques, and results indicate that significant computational savings can be achieved by making full use of prior knowledge and parallel computing.

Organisation(s)

Institute for Risk and Reliability

External Organisation(s)

Northwestern Polytechnical University
TU Dortmund University
Universidad Adolfo Ibanez
University of Liverpool
Tongji University

Type

Article

Journal

Applied mathematical modelling

Volume

108

Pages

220-235

No. of pages

16

ISSN

0307-904X

Publication date

08.2022

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Modelling and Simulation, Applied Mathematics

Electronic version(s)

https://doi.org/10.1016/j.apm.2022.03.031 (Access: Closed)