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- Kaho Tomoki
- Muroran Institute of Technology
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- Watanabe Shinya
- Muroran Institute of Technology
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- Sakakibara Kazutoshi
- Toyama Prefectural University
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<p>Traditional multi-objective branch-and-bound approaches to multi-objective mixed-integer linear programming (MOMILP) problems are very expensive to search due to the huge number of Pareto-optimal solutions. In this research, we propose a practical method of dividing a multi-objective problem into multiple single-objective problems by weight vectors and applying the branch-and-bound method (BB) to each subproblem. The proposed method is named multi-objective branch-and-bound based on decomposition (MOBB/D) because it is a combination of the concept of multi-objective evolutionary algorithm based on decomposition (MOEA/D) and BB. The most important feature of MOBB/D is to obtain many Pareto solutions efficiently by sharing information between nearby subproblems in MOMILP problems. In this paper, we describe an approach to share tables in the simplex method as an example of information. Moreover, MOBB/D can control computation cost for solving MOMILP problems by adjusting the number of obtained Pareto solutions. To verify the effectiveness of the proposed method, we compared the search performance of MOBB/D with and without the use of neighborhood information.</p>
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 142 (3), 373-381, 2022-03-01
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390291767485279872
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- NII論文ID
- 130008165797
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 032021126
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 使用不可