A lower confidence bounding approach based on the coefficient of variation for expensive global design optimization
ISSN: 0264-4401
Article publication date: 25 March 2019
Issue publication date: 8 May 2019
Abstract
Purpose
Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.
Design/methodology/approach
In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.
Findings
Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.
Practical implications
The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.
Originality/value
CV-LCB approach can balance the exploration and exploitation objectively.
Keywords
Acknowledgements
This research has been supported by the National Natural Science Foundation of China (NSFC) under Grant No. 51805179, No. 51775203, No. 51505163 and No. 51721092, and the Fundamental Research Funds for the Central Universities, HUST: Grant No. 2016YXMS272. The authors also would like to thank the anonymous referees for their valuable comments.
Citation
Cheng, J., Jiang, P., Zhou, Q., Hu, J., Yu, T., Shu, L. and Shao, X. (2019), "A lower confidence bounding approach based on the coefficient of variation for expensive global design optimization", Engineering Computations, Vol. 36 No. 3, pp. 830-849. https://doi.org/10.1108/EC-08-2018-0390
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited