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.
PSO was first used to identify the best group of trend functions and to optimize the correlation parameter thereafter.
The Kriging surrogate model was used to resolve the fuselage optimization of an unmanned helicopter.
The optimization results indicated that an appropriate PSO scheme can improve the efficiency of the Kriging surrogate model.
Both the STANDARD PSO and the original PSO algorithms were chosen to show the effect of PSO on a Kriging surrogate model.
The authors gratefully acknowledge the support of the National Natural Science Foundation of China (No.91538204) and the Aerospace Science and Technology Fund.
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
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