| Item type |
SIG Technical Reports(1) |
| 公開日 |
2024-07-15 |
| タイトル |
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|
タイトル |
Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization |
| タイトル |
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言語 |
en |
|
タイトル |
Introducing Competitive Mechanism to Differential Evolution for Numerical 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_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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Graduate School of Information Science and Technology, Hokkaido University |
| 著者所属 |
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Graduate School of Information Science and Technology, Hokkaido University |
| 著者所属 |
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Graduate School of Information Science and Technology, Hokkaido University |
| 著者所属 |
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Information Initiative Center, Hokkaido University |
| 著者所属(英) |
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en |
|
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Graduate School of Information Science and Technology, Hokkaido University |
| 著者所属(英) |
|
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|
en |
|
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Graduate School of Information Science and Technology, Hokkaido University |
| 著者所属(英) |
|
|
|
en |
|
|
Graduate School of Information Science and Technology, Hokkaido University |
| 著者所属(英) |
|
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|
en |
|
|
Information Initiative Center, Hokkaido University |
| 著者名 |
Rui, Zhong
Yang, Cao
Enzhi, Zhang
Masaharu, Munetomo
|
| 著者名(英) |
Rui, Zhong
Yang, Cao
Enzhi, Zhang
Masaharu, Munetomo
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| 論文抄録 |
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内容記述タイプ |
Other |
|
内容記述 |
This paper introduces a novel competitive mechanism into differential evolution (CDE). CDE features a simple yet efficient mutation strategy: DE/winner-to-best/1. Essentially, the proposed DE/winner-to-best/1 strategy can be recognized as an intelligent integration of the existing mutation strategies of DE/rand-to-best/1 and DE/cur-to-best/1. The incorporation of DE/winner-to-best/1 and the competitive mechanism provide new avenues for advancing DE techniques. To investigate the performance of the proposed CDE, comprehensive numerical experiments are conducted on engineering simulation optimization tasks with other state-of-the-art optimizers and DE variants employed as competitor algorithms. The experimental results and statistical analyses highlight the promising potential of CDE as an alternative optimizer for addressing diverse optimization challenges. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
This paper introduces a novel competitive mechanism into differential evolution (CDE). CDE features a simple yet efficient mutation strategy: DE/winner-to-best/1. Essentially, the proposed DE/winner-to-best/1 strategy can be recognized as an intelligent integration of the existing mutation strategies of DE/rand-to-best/1 and DE/cur-to-best/1. The incorporation of DE/winner-to-best/1 and the competitive mechanism provide new avenues for advancing DE techniques. To investigate the performance of the proposed CDE, comprehensive numerical experiments are conducted on engineering simulation optimization tasks with other state-of-the-art optimizers and DE variants employed as competitor algorithms. The experimental results and statistical analyses highlight the promising potential of CDE as an alternative optimizer for addressing diverse optimization challenges. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
|
収録物識別子 |
AN10505667 |
| 書誌情報 |
研究報告数理モデル化と問題解決（MPS）
巻 2024-MPS-149,
号 9,
p. 1-5,
発行日 2024-07-15
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| 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 |
2188-8833 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-MPS24149009.pdf (1.8 MB)
