Hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization

• Item type: SIG Technical Reports(1)
• 公開日: 2024-07-15
• タイトル: Hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization
• タイトル / 言語: en
• タイトル: Hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Information Science and Technology, Hokkaido University
• 著者所属: Institute of Science and Technology, Niigata University
• 著者所属: Information Initiative Center, Hokkaido University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Hokkaido University
• 著者所属(英): en
• 著者所属(英): Institute of Science and Technology, Niigata University
• 著者所属(英): en
• 著者所属(英): Information Initiative Center, Hokkaido University
• 著者名: Rui, Zhong; Jun, Yu; Masaharu, Munetomo
• 著者名(英): Rui, Zhong; Jun, Yu; Masaharu, Munetomo
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper proposes a novel hyper-heuristic differential evolution (HHDE). We design mutation archive, crossover archive, and boundary repair archive as low-level heuristics of HHDE. A learning-free selection function is employed as the high-level component. Comprehensive numerical experiments on CEC2022 benchmark functions are conducted to assess the efficacy of our proposed HHDE. The performance of HHDE was compared against a range of other state-of-the-art competitor optimizers. The experimental results and statistical analysis confirm the competitiveness and efficiency of HHDE.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper proposes a novel hyper-heuristic differential evolution (HHDE). We design mutation archive, crossover archive, and boundary repair archive as low-level heuristics of HHDE. A learning-free selection function is employed as the high-level component. Comprehensive numerical experiments on CEC2022 benchmark functions are conducted to assess the efficacy of our proposed HHDE. The performance of HHDE was compared against a range of other state-of-the-art competitor optimizers. The experimental results and statistical analysis confirm the competitiveness and efficiency of HHDE.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 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 10, p. 1-5, 発行日 2024-07-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8833
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会