| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2017-10-03 |
| タイトル |
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|
タイトル |
Sequential Approximate Optimization using Radial Basis Function network for engineering optimization |
| 言語 |
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|
言語 |
eng |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| ID登録 |
|
|
ID登録 |
10.24517/00008486 |
|
ID登録タイプ |
JaLC |
| 著者 |
Kitayama, Satoshi
Arakawa, Masao
Yamazaki, Koetsu
|
| 著者別表示 |
北山, 哲士
荒川, 雅生
山崎, 光悦
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| 提供者所属 |
|
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内容記述タイプ |
Other |
|
内容記述 |
金沢大学理工研究域機械工学系 |
| 書誌情報 |
Optimization and Engineering
巻 12,
号 4,
p. 535-557,
発行日 2011-12-01
|
| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
1389-4420 |
| NCID |
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収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA11484866 |
| DOI |
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|
関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1007/s11081-010-9118-y |
| 出版者 |
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出版者 |
Springer Science+Business Media, LLC |
| 抄録 |
|
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内容記述タイプ |
Abstract |
|
内容記述 |
This paper presents a Sequential Approximate Optimization (SAO) procedure that uses the Radial Basis Function (RBF) network. If the objective and constraints are not known explicitly but can be evaluated through a computationally intensive numerical simulation, the response surface, which is often called meta-modeling, is an attractive method for finding an approximate global minimum with a small number of function evaluations. An RBF network is used to construct the response surface. The Gaussian function is employed as the basis function in this paper. In order to obtain the response surface with good approximation, the width of this Gaussian function should be adjusted. Therefore, we first examine the width. Through this examination, some sufficient conditions are introduced. Then, a simple method to determine the width of the Gaussian function is proposed. In addition, a new technique called the adaptive scaling technique is also proposed. The sufficient conditions for the width are satisfied by introducing this scaling technique. Second, the SAO algorithm is developed. The optimum of the response surface is taken as a new sampling point for local approximation. In addition, it is necessary to add new sampling points in the sparse region for global approximation. Thus, an important issue for SAO is to determine the sparse region among the sampling points. To achieve this, a new function called the density function is constructed using the RBF network. The global minimum of the density function is taken as the new sampling point. Through the sampling strategy proposed in this paper, the approximate global minimum can be found with a small number of function evaluations. Through numerical examples, the validities of the width and sampling strategy are examined in this paper. © 2010 Springer Science+Business Media, LLC. |
| 著者版フラグ |
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|
出版タイプ |
AM |
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出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
TE-PR-KITAYAMA-S-535.pdf (765.8 kB)
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