| ○種別 (必須): | □ | 学術論文 (審査論文)
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| ○言語 (必須): | □ | 日本語
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| ○著者 (必須): | 1. | (英) (日) 濱田 直希 (読)
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| 2. | 永田 裕一 ([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.知能情報コース.情報工学講座])
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| 3. | (英) (日) 小林 重信 (読)
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| 4. | 小野 功
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| ○題名 (必須): | □ | (英) BS-AWA: A More Scalable Adaptive Weighted Aggregation for Continuous Multiobjective Optimization (日) BS-AWA: Adaptive Weighted Aggregationの目的数に対するスケーラビリティの向上
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| ○要約 (任意): | □ | (英) This paper proposes a more scalable variant of Adaptive Weighted Aggregation (AWA) with respect to the number of objectives in continuous multiobjective optimization. AWA is a scalarization-based multi-start strategy for generating finite points that approximate the entire Pareto set and Pareto front, which is especially focused on many-objective problems (having four or more objectives). In our last study, we discussed a reasonable stopping criterion for AWA, the <em>representing iteration</em>, and analyzed the time and space complexity of AWA when the representing iteration is used as a stopping criterion. Theoretical and empirical results showed that the running time and memory consumption of AWA depends on the number of solutions found in the representing iteration, the <em>representing number</em>. Due to the factorial increase of the representing number for objectives, the applicability of AWA is limited to 16-objective problems. In this study, we therefore redesign two central operations in AWA, the <em>subdivision</em> and the <em>relocation</em>, in order to reduce the representing number. The new subdivision is based on the simplicial complex and its barycentric subdivision and the new relocation is based on the simplicial approximation of a mapping and its range, both of which are well-known notions in topology. We theoretically compare the new AWA, named the <em>barycentric subdivision-based AWA (BS-AWA)</em>, with the old AWA in terms of their representing iteration, representing number and approximate memory consumption to illustrate the improvement of scalability; the result implies that BS-AWA is applicable to over 20-objective problems. Numerical experiments using 2- to 17-objective benchmark problems show that BS-AWA achieves a better coverage of obtained solutions than conventional multi-start descent methods in both the variable and objective spaces. The running time and the solution distribution of BS-AWA are also discussed. (日)
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| ○キーワード (推奨): | 1. | (英) multi-objective optimization (日) (読)
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| 2. | (英) continuous optimization (日) (読)
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| 3. | (英) scalarization (日) (読)
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| 4. | (英) multi-start search (日) (読)
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| 5. | (英) weight adaptation (日) (読)
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| ○発行所 (推奨): | □ | (英) The Japanese Society for Evolutionary Computation (日) 進化計算学会 (読)
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| ○誌名 (必須): | □ | 進化計算学会論文誌 (進化計算学会)
(eISSN: 2185-7385)
| ○ISSN (任意): | □ | 2185-7385
ISSN: 2185-7385
(eISSN: 2185-7385) Title: 進化計算学会論文誌Supplier: 進化計算学会 (J-STAGE)
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| ○巻 (必須): | □ | 5
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| ○号 (必須): | □ | 1
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| ○頁 (必須): | □ | 1 15
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| ○年月日 (必須): | □ | 西暦 2014年 4月 初日 (平成 26年 4月 初日)
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| ○URL (任意): | □ | https://ci.nii.ac.jp/naid/130004965149/
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| ○DOI (任意): | □ | 10.11394/tjpnsec.5.1 (→Scopusで検索)
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| ○CRID (任意): | □ | 1390282680342337152
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| ○NAID : | □ | 130004965149
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