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Global optimization by generalized random tunneling algorithm (5th report, approximate optimization using RBF network)
https://doi.org/10.24517/00007433
https://doi.org/10.24517/000074335b60768f-de4a-4d9c-ba74-551b1617f0a6
名前 / ファイル | ライセンス | アクション |
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TE-PR-KITAYAMA-S-1299.pdf (238.9 kB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2017-10-03 | |||||
タイトル | ||||||
タイトル | Global optimization by generalized random tunneling algorithm (5th report, approximate optimization using RBF network) | |||||
言語 | ||||||
言語 | jpn | |||||
資源タイプ | ||||||
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 | 10.24517/00007433 | |||||
ID登録タイプ | JaLC | |||||
著者 |
Kitayama, Satoshi
× Kitayama, Satoshi× Arakawa, Masao× Yamazaki, Koetsu |
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著者別表示 |
北山, 哲士
× 北山, 哲士× 荒川, 雅生× 山崎, 光悦 |
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提供者所属 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 金沢大学大学院自然科学研究科知的システム創成 | |||||
書誌情報 |
Transactions of the Japan Society of Mechanical Engineers, Part C 巻 73, 号 5, p. 1299-1306, 発行日 2007-01-01 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0387-5024 | |||||
出版者 | ||||||
出版者 | 日本機械学会 | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | In practical applications, it is important to reduce the function evaluations in the simulation, and obtain the approximate optimum with high accuracy. To achieve these objectives, the integrative optimization system using the RBF Network (RBFN) and the Generalized Random Tunneling Algorithm (GRTA) is proposed in this paper. This system consists of three parts. (1) Construction of the response surface, (2) Optimization by the GRTA, and (3) Adding the sampling points. The RBFN is used to construct the response surface. The radius on RBFN, which affects the accuracy of response surface, is an important parameter. Firstly new equation for the radius is proposed, based on the examination of existing equation. Secondly a simple sampling strategy to obtain an optimum with high accuracy is also proposed. In general, the objective function and the constraints are approximated, separately. However, the optimum of response surface will often violate the constraints. To avoid such situations, the augmented objective function is utilized in this paper. Then the proposed sampling strategy is applied. Through typical benchmark problems, the validity and effectiveness are examined. | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |