Enhancing Multiobjective Evolutionary Algorithms by Local Dominance and Local Recombination: Performance Verification in Multiobjective 0/1 Knapsack Problems

• Item type: Trans(1)
• 公開日: 2007-02-15
• タイトル: Enhancing Multiobjective Evolutionary Algorithms by Local Dominance and Local Recombination: Performance Verification in Multiobjective 0/1 Knapsack Problems
• タイトル / 言語: en
• タイトル: Enhancing Multiobjective Evolutionary Algorithms by Local Dominance and Local Recombination: Performance Verification in Multiobjective 0/1 Knapsack Problems
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: オリジナル論文
• 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
• 著者所属: Faculty of Engineering Shinshu University
• 著者所属: Faculty of Engineering Shinshu University
• 著者所属: Faculty of Engineering Shinshu University
• 著者所属(英): en
• 著者所属(英): Faculty of Engineering, Shinshu University
• 著者所属(英): en
• 著者所属(英): Faculty of Engineering, Shinshu University
• 著者所属(英): en
• 著者所属(英): Faculty of Engineering, Shinshu University
• 著者名: Hiroyuki, Sato; Hern´an, Aguirre; Kiyoshi, Tanaka
• 著者名(英): Hiroyuki, Sato; Hern´an, Aguirre; Kiyoshi, Tanaka
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper proposes a method to enhance single population multiobjective evolutionary algorithms (MOEAs) by searching based on local dominance and local recombination. In this method first all fitness vectors of individuals are transformed to polar coordinate vectors in objective function space. Then the population is iteratively divided into several subpopulations by using declination angles. As a result each sub-population covers a sub-region in the multiobjective space with its individuals located around the same search direction. Next local dominance is calculated separately for each sub-population after alignment of its principle search direction by rotation. Selection recombination and mutation are applied to individuals within each sub-population. The proposed method can improve the performance of MOEAs that use dominance based selection and can reduce the entire computational cost to calculate dominance among solutions as well. In this paper we verify the effectiveness of the proposed method obtaining Pareto optimal solutions in two representative MOEAs i.e. NSGA-II and SPEA2 with Multiobjective 0/1 Knapsack Problems.
• 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 method to enhance single population multiobjective evolutionary algorithms (MOEAs) by searching based on local dominance and local recombination. In this method, first, all fitness vectors of individuals are transformed to polar coordinate vectors in objective function space. Then, the population is iteratively divided into several subpopulations by using declination angles. As a result, each sub-population covers a sub-region in the multiobjective space with its individuals located around the same search direction. Next, local dominance is calculated separately for each sub-population after alignment of its principle search direction by rotation. Selection, recombination, and mutation are applied to individuals within each sub-population. The proposed method can improve the performance of MOEAs that use dominance based selection, and can reduce the entire computational cost to calculate dominance among solutions as well. In this paper we verify the effectiveness of the proposed method obtaining Pareto optimal solutions in two representative MOEAs, i.e. NSGA-II and SPEA2, with Multiobjective 0/1 Knapsack Problems.
• 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: AA11464803
• 書誌情報: 情報処理学会論文誌数理モデル化と応用（TOM）; 巻 48, 号 SIG2(TOM16), p. 98-113, 発行日 2007-02-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7780
• 出版者 / 言語: ja
• 出版者: 情報処理学会