A metamodel optimization methodology based on multi-level fuzzy clustering space reduction strategy and its applications

被引：15

作者：

Hu, Wang ^{[1
]}

Li Enying ^{[1
]}

Li, G-Y. ^{[1
]}

Zhong, Z. H. ^{[1
]}

机构：

[1] Hunan Univ, MOE, Key Lab Adv Technol Vehicle Body Design & Manufac, Changsha 410082, Hunan, Peoples R China

来源：

COMPUTERS & INDUSTRIAL ENGINEERING | 2008年 / 55卷 / 02期

关键词：

Multi-level; Metamodel; Fuzzy clustering; Nonlinear; Kriging interpolation;

D O I：

10.1016/j.cie.2008.01.011

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper proposes metamodel optimization methodology based on multi-level fuzzy-clustering space reduction strategy with Kriging interpolation. The proposed methodology is composed of three levels. In the 1st level, the initial samples need partitioning into several clusters due to design variables by fuzzy-clustering method. Sequentially, only some of the clusters are involved in building metamodels locally in the 2nd level. Finally, the best optimized result is collected from all metamodels in the 3rd level. The nonlinear problems with multi-humps as test functions arc implemented for proving accuracy and efficiency of proposed method. The practical nonlinear engineering problems are optimized by suggested methodology and satisfied results are also obtained. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：503 / 532

页数：30

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