Multi-objective optimization based on meta-modeling by using support vector regression

被引：70
|
作者：
Yun, Yeboon [1 ]
Yoon, Min [2 ]
Nakayama, Hirotaka [3 ]
机构：
[1] Kagawa Univ, Fac Engn, Kagawa 7610396, Japan
[2] Konkuk Univ, Seoul 120749, South Korea
[3] Konan Univ, Kobe, Hyogo 6588501, Japan
来源：
OPTIMIZATION AND ENGINEERING | 2009年 / 10卷 / 02期
关键词：
Multi-objective optimization; Pareto frontier; Support vector regression; Sequential approximation method; Evolutionary multi-objective optimization;
D O I：
10.1007/s11081-008-9063-1
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Practical engineering design problems have a black-box objective function whose forms are not explicitly known in terms of design variables. In those problems, it is very important to make the number of function evaluations as few as possible in finding an optimal solution. So, in this paper, we propose a multi-objective optimization method based on meta-modeling predicting a form of each objective function by using support vector regression. In addition, we discuss a way how to select additional experimental data for sequentially revising a form of objective function. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.
引用
收藏
页码：167 / 181
页数：15
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