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Title: Sequential kriging optimization using multiple-fidelity evaluations

Abstract

When cost per evaluation on a system of interest is high, surrogate systems can provide cheaper but lower-fidelity information. In the proposed extension of the sequential kriging optimization method, surrogate systems are exploited to reduce the total evaluation cost. The method utilizes data on all systems to build a kriging metamodel that provides a global prediction of the objective function and a measure of prediction uncertainty. The location and fidelity level of the next evaluation are selected by maximizing an augmented expected improvement function, which is connected with the evaluation costs. The proposed method was applied to test functions from the literature and a metal-forming process design problem via finite element simulations. The method manifests sensible search patterns, robust performance, and appreciable reduction in total evaluation cost as compared to the original method.

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
920546
Report Number(s):
PNNL-SA-53444
TRN: US200818%%625
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Structural and Multidisciplinary Optimization, 32(5):369-382
Additional Journal Information:
Journal Volume: 32; Journal Issue: 5
Country of Publication:
United States
Language:
English
Subject:
97; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; COST; SYSTEMS ANALYSIS; FORECASTING; KRIGING; PERFORMANCE

Citation Formats

Huang, Deng, Allen, T T, Notz, W I, and Miller, R A. Sequential kriging optimization using multiple-fidelity evaluations. United States: N. p., 2006. Web. doi:10.1007/s00158-005-0587-0.
Huang, Deng, Allen, T T, Notz, W I, & Miller, R A. Sequential kriging optimization using multiple-fidelity evaluations. United States. https://doi.org/10.1007/s00158-005-0587-0
Huang, Deng, Allen, T T, Notz, W I, and Miller, R A. 2006. "Sequential kriging optimization using multiple-fidelity evaluations". United States. https://doi.org/10.1007/s00158-005-0587-0.
@article{osti_920546,
title = {Sequential kriging optimization using multiple-fidelity evaluations},
author = {Huang, Deng and Allen, T T and Notz, W I and Miller, R A},
abstractNote = {When cost per evaluation on a system of interest is high, surrogate systems can provide cheaper but lower-fidelity information. In the proposed extension of the sequential kriging optimization method, surrogate systems are exploited to reduce the total evaluation cost. The method utilizes data on all systems to build a kriging metamodel that provides a global prediction of the objective function and a measure of prediction uncertainty. The location and fidelity level of the next evaluation are selected by maximizing an augmented expected improvement function, which is connected with the evaluation costs. The proposed method was applied to test functions from the literature and a metal-forming process design problem via finite element simulations. The method manifests sensible search patterns, robust performance, and appreciable reduction in total evaluation cost as compared to the original method.},
doi = {10.1007/s00158-005-0587-0},
url = {https://www.osti.gov/biblio/920546}, journal = {Structural and Multidisciplinary Optimization, 32(5):369-382},
number = 5,
volume = 32,
place = {United States},
year = {Wed Nov 01 00:00:00 EST 2006},
month = {Wed Nov 01 00:00:00 EST 2006}
}