A novel improvement of Kriging surrogate model

被引：8

作者：

He, Wei ^{[1
,2
]}

Xu, Yuanming ^{[1
]}

Zhou, Yaoming ^{[1
]}

Li, Qiuyue ^{[3
]}

机构：

[1] Beihang Univ, Sch Aeronaut Sci & Engn, Beijing, Peoples R China

[2] Aviat Univ Air Force, Dept Aviat Theory, Changchun, Jilin, Peoples R China

[3] Aviat Univ Air Force, Fundamental Dept, Changchun, Jilin, Peoples R China

来源：

AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY | 2019年 / 91卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Particle swarm optimization; Fuselage optimization; Kriging surrogate model; Unmanned helicopter; OPTIMIZATION;

D O I：

10.1108/AEAT-06-2018-0157

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

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.

引用

页码：994 / 1001

页数：8

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