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Proposal of adaptive range particle swarm optimization
https://doi.org/10.24517/00007740
https://doi.org/10.24517/00007740c5327c31-dd9d-4a69-9860-ee372e517c74
名前 / ファイル | ライセンス | アクション |
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TE-PR-KITAYAMA-S-280.pdf (735.4 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 | Proposal of adaptive range particle swarm optimization | |||||
言語 | ||||||
言語 | eng | |||||
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 | 10.24517/00007740 | |||||
ID登録タイプ | JaLC | |||||
その他のタイトル | ||||||
領域適応型Particle Swarm Optimizationの提案 | ||||||
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 | |||||
提供者所属 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 金沢大学工学部 | |||||
書誌情報 |
Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C 巻 73, 号 725, p. 280-287, 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 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1299/kikaic.73.280 | |||||
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 | This paper proposes a new method which is called as Adaptive Range Particle Swarm Optimization (ARPSO), based on Adaptive Range Genetic Algorithm. That is, the active search domain is determined by using the mean and standard deviation of each design variable. In general, multipoints methods are utilized in the field of evolutionary computation. At the initial search stage it is preferable to explore the search domain widely, and is also preferable to explore the smaller search domain as the search goes on. To achieve this objective, new parameter which determines the active search domain is introduced. This new parameter gradually increases as the search goes on. Finally it is possible to shrink the search domain. The way to determine the maximum value of this new parameter is also shown in this paper. The optimum solution with high accuracy and a. little number of function calls is obtained by proposed method in compared with original Particle Swarm Optimization. Through numerical examples, the effectiveness and validity of proposed method 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 |