期刊
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
卷 362, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2020.112861
关键词
Surrogate-based optimization; Infill criterion; Gradient-enhanced Kriging; Gaussian processes
资金
- National Natural Science Foundation of China [51675198]
- National Defense Pre-Research Foundation of China [41423010205]
- National Science Fund for Distinguished Young Scholars of China [51825502]
- Program for HUST Academic Frontier Youth Team, PR China [2017QYTD04]
This paper explores the use of Gradient-enhanced Kriging for optimization of expensive black-box design problems, which is not completely limited by the conventional Efficient Global Optimization algorithm framework. Specifically, we give the best linear unbiased predictor and mean squared prediction error of the partial derivatives of Gradient-enhanced Kriging and then propose a measure named Approximate Probability of Stationary Point to estimate the approximate probability of a candidate infill point be a stationary point of the underlying function. When it comes to the selection of infill point, we not only maximize the well-known Expected Improvement but also evaluate the Approximate Probability of Stationary Point as a double-check step. Then the infill decision is made according to the extent of consistency between these two quantities. Furthermore, to examine whether the optimization process will gain from sparing more costs for response evaluation, we investigate also the cases that the gradient evaluation step is conditionally skipped in some iterations. Three new infill criteria are proposed and experimented with three analytical test functions and an airfoil optimal shape design. Results show that the optimization performance can be improved by exploiting the auxiliary gradient information in the proposed way. (C) 2020 ElsevierB.V. All rights reserved.
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