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A parameterized lower confidence bounding scheme for adaptive metamodel-based design optimization

Jun Zheng (Engineering Faculty, China University of Geosciences, Wuhan, China)
Zilong Li (Huazhong Institute of Electro-Optics, Wuhan, China)
Liang Gao (The State Key Laboratory of Digital Manufacturing Equipment and Technology, Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China)
Guosheng Jiang (China University of Geosciences, Wuhan, China)

Engineering Computations

ISSN: 0264-4401

Article publication date: 3 October 2016

214

Abstract

Purpose

The purpose of this paper is to efficiently use as few sample points as possible to get a sufficiently explored design space and an accurate optimum for adaptive metamodel-based design optimization (AMBDO).

Design/methodology/approach

A parameterized lower confidence bounding (PLCB) scheme is proposed in which a cooling strategy is introduced to guarantee the balance between exploitation and exploration by varying weights of the predicting error and optimum of a metamodel. The proposed scheme is investigated by a set of test functions and a structural optimization problem, in which PLCB with four kinds of cooling control functions are studied. Moreover, other infill criteria (such as expected improvement and its extension versions) are taken into comparison.

Findings

Results show that the proposed PLCB (especially PLCB with the first cooling control function) based AMBDO method can find the optimum with fewer evaluations and maintain good accuracy, which means the proposed PLCB contributes to the excellent efficiency and accuracy in finding global optimal solutions.

Originality/value

The parameterized version of the lower confidence bound metric is proposed for AMBDO, typically used in the context of adaptive sampling in efficient global optimization.

Keywords

Acknowledgements

This research was supported by the National Nature Science Foundation of China under Grant No. 51505439, the Research Fund for the Doctoral Program of Higher Education of China under Grant No. 2014M562085 and the Fundamental Research Funds for the Central Universities, CUG: Grant No. CUGL150821.

Citation

Zheng, J., Li, Z., Gao, L. and Jiang, G. (2016), "A parameterized lower confidence bounding scheme for adaptive metamodel-based design optimization", Engineering Computations, Vol. 33 No. 7, pp. 2165-2184. https://doi.org/10.1108/EC-04-2015-0088

Publisher

:

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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