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A novel improvement of Kriging surrogate model

Wei He (School of Aeronautic Science and Engineering, Beihang University, Beijing, China, and Department of Aviation Theory, Aviation University of Air Force, Changchun, China)
Yuanming Xu (School of Aeronautic Science and Engineering, Beihang University, Beijing, China)
Yaoming Zhou (School of Aeronautic Science and Engineering, Beihang University, Beijing, China)
Qiuyue Li (Fundamental Department, Aviation University of Air Force, Changchun, China)

Aircraft Engineering and Aerospace Technology

ISSN: 0002-2667

Publication date: 8 July 2019

Abstract

Purpose

This paper aims to introduce a method based on the optimizer of the particle swarm optimization (PSO) algorithm to improve the efficiency of a Kriging surrogate model.

Design/methodology/approach

PSO was first used to identify the best group of trend functions and to optimize the correlation parameter thereafter.

Findings

The Kriging surrogate model was used to resolve the fuselage optimization of an unmanned helicopter.

Practical implications

The optimization results indicated that an appropriate PSO scheme can improve the efficiency of the Kriging surrogate model.

Originality/value

Both the STANDARD PSO and the original PSO algorithms were chosen to show the effect of PSO on a Kriging surrogate model.

Keywords

Acknowledgements

The authors gratefully acknowledge the support of the National Natural Science Foundation of China (No.91538204) and the Aerospace Science and Technology Fund.

Citation

He, W., Xu, Y., Zhou, Y. and Li, Q. (2019), "A novel improvement of Kriging surrogate model", Aircraft Engineering and Aerospace Technology, Vol. 91 No. 7, pp. 994-1001. https://doi.org/10.1108/AEAT-06-2018-0157

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited