A new Ensemble Framework based on MOEA/D

• Item type: SIG Technical Reports(1)
• 公開日: 2022-07-19
• タイトル: A new Ensemble Framework based on MOEA/D
• タイトル / 言語: en
• タイトル: A new Ensemble Framework based on MOEA/D
• 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: Muroran Institute of Technology
• 著者所属: Muroran Institute of Technology
• 著者所属(英): en
• 著者所属(英): Muroran Institute of Technology
• 著者所属(英): en
• 著者所属(英): Muroran Institute of Technology
• 著者名: Jiayi, Han; Shinya, Watanabe
• 著者名(英): Jiayi, Han; Shinya, Watanabe
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a powerful algorithm and provides a framework for solving multi-objective optimization problems (MOPs). Differential evolution (DE) algorithm and its variants are often used to generate new solutions in MOEA/D heuristically. However, based on the “No Free Lunch” theory, only a fixed algorithm for generating new solutions in the original MOEA/D cannot efficiently solve all MOPs. Therefore, in this paper, we propose a new framework based on MOEA/D named MOEA/D-EF (Ensemble Framework), which can contain a variety of new-solutions generating algorithms (candidate algorithms) with different search capabilities to improve the overall universality of the algorithm. In the new approach, the whole iteration is divided into the evaluation generation (EG) and the implementation generation (IG). We provide a fair evaluation environment for each candidate algorithm at the beginning of each generation belonging to the EG and evaluate their performance by using the Hypervolume indicator. The algorithm with the best performance in one EG will be chosen and executed in the following IG. Also, we believe that some historical information representing evolutionary details can help generate superior new solutions. Thus, in numerical experiments, we take our original DE variant based on the ideal point and historical information as one of the candidate algorithms for generating new solutions. The numerical experiments show that the new framework has broader universality.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a powerful algorithm and provides a framework for solving multi-objective optimization problems (MOPs). Differential evolution (DE) algorithm and its variants are often used to generate new solutions in MOEA/D heuristically. However, based on the “No Free Lunch” theory, only a fixed algorithm for generating new solutions in the original MOEA/D cannot efficiently solve all MOPs. Therefore, in this paper, we propose a new framework based on MOEA/D named MOEA/D-EF (Ensemble Framework), which can contain a variety of new-solutions generating algorithms (candidate algorithms) with different search capabilities to improve the overall universality of the algorithm. In the new approach, the whole iteration is divided into the evaluation generation (EG) and the implementation generation (IG). We provide a fair evaluation environment for each candidate algorithm at the beginning of each generation belonging to the EG and evaluate their performance by using the Hypervolume indicator. The algorithm with the best performance in one EG will be chosen and executed in the following IG. Also, we believe that some historical information representing evolutionary details can help generate superior new solutions. Thus, in numerical experiments, we take our original DE variant based on the ideal point and historical information as one of the candidate algorithms for generating new solutions. The numerical experiments show that the new framework has broader universality.
• 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 2022-MPS-139, 号 7, p. 1-4, 発行日 2022-07-19
• 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