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Large Scale Optimization by Adaptive Differential Evolution with Landscape Modality Detection and a Diversity Archive
https://kindai.repo.nii.ac.jp/records/11843
https://kindai.repo.nii.ac.jp/records/11843adb05ff4-f83b-4c7c-aee1-8af461f131ee
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
AN10437975-20120320-0055.pdf (3.8 MB)
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| Item type | ☆紀要論文 / Departmental Bulletin Paper(1) | |||||||||||||
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| 公開日 | 2013-01-07 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Large Scale Optimization by Adaptive Differential Evolution with Landscape Modality Detection and a Diversity Archive | |||||||||||||
| 言語 | en | |||||||||||||
| 著者 |
阪井, 節子
× 阪井, 節子
× 高濱, 徹行
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| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| キーワード | ||||||||||||||
| 主題 | differential evolution, large scale optimization, landscape modality, parametercontrol | |||||||||||||
| 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 | departmental bulletin paper | |||||||||||||
| 著者(英) | ||||||||||||||
| 言語 | en | |||||||||||||
| 値 | Sakai, Setsuko | |||||||||||||
| 著者(英) | ||||||||||||||
| 言語 | en | |||||||||||||
| 値 | Takahama, Tetsuyuki | |||||||||||||
| 著者 所属 | ||||||||||||||
| 値 | 広島修道大学商学部; 教授 | |||||||||||||
| 著者 所属 | ||||||||||||||
| 値 | 広島市立大学大学院情報学研究科; 教授 | |||||||||||||
| 版 | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| 出版者 名前 | ||||||||||||||
| 出版者 | 近畿大学商経学会 | |||||||||||||
| 書誌情報 |
商経学叢 en : Shokei-gakuso: Journal of Business Studies 巻 58, 号 3, p. 55-77, 発行日 2012-03-01 |
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| ISSN | ||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 04502825 | |||||||||||||
| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | [Abstract ] Nonlinear optimization problems are very important and frequently appear in the real world. Differential Evolution (DE), which is one of evolutionary algorithms, is newly proposed to solve the problems. In this study, in order to solve large scale optimization problems we propose adaptive DE with landscape modality detection and a diversity archive (LMaDEa). In DE, large population size, which is much larger than the number of decision variables, is adopted in order to keep the diversity of search. However, it is difficult to adopt such large size to solve large scaled optimization problems because the population size will become too large and the search efficiency will degrade. In this study, we propose to solve large scale optimization problems using small population size and a large archive for diversity. Also, we propose simple control of scaling factor by observing landscape modality of search points and success-based adaptive control of crossover operation. The advantage of LMaDEa is shown by solving the set of benchmark functions provided for the CEC2010 Special Session on Large Scale Global Optimization and comparing the results with those of other methods. | |||||||||||||
| フォーマット | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | application/pdf | |||||||||||||
