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Cited 6 time in webofscience Cited 7 time in scopus
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Statistical surrogate model based sampling criterion for stochastic global optimization of problems with constraints

Authors
Cho, Su-gilJang, JunyongKim, JihoonLee, MinukChoi, Jong-SuHong, SupLee, Tae Hee
Issue Date
4월-2015
Publisher
KOREAN SOC MECHANICAL ENGINEERS
Keywords
Constrained global optimization; Metamodel-based design optimization; Kriging surrogate model; Stochastic global optimization
Citation
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v.29, no.4, pp 1421 - 1427
Pages
7
Journal Title
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
Volume
29
Number
4
Start Page
1421
End Page
1427
URI
https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/776
DOI
10.1007/s12206-015-0313-9
ISSN
1738-494X
1976-3824
Abstract
Sequential surrogate model-based global optimization algorithms, such as super-EGO, have been developed to increase the efficiency of commonly used global optimization technique as well as to ensure the accuracy of optimization. However, earlier studies have drawbacks because there are three phases in the optimization loop and empirical parameters. We propose a united sampling criterion to simplify the algorithm and to achieve the global optimum of problems with constraints without any empirical parameters. It is able to select the points located in a feasible region with high model uncertainty as well as the points along the boundary of constraint at the lowest objective value. The mean squared error determines which criterion is more dominant among the infill sampling criterion and boundary sampling criterion. Also, the method guarantees the accuracy of the surrogate model because the sample points are not located within extremely small regions like super-EGO. The performance of the proposed method, such as the solvability of a problem, convergence properties, and efficiency, are validated through nonlinear numerical examples with disconnected feasible regions.
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