Constructing Search Algorithms Taking Account of Global-Multimodality and Many-Objectiveness
| Project/Area Number |
26330272
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Soft computing
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Ono Isao 東京工業大学, 情報理工学院, 准教授 (00304551)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 進化計算 / 大域的最適化 / 多目的最適化 / 大域的多峰性 / 連続関数最適化 / 自然進化戦略 / 有望領域囲い込み法 / AWA / 有望個体囲い込み法 / ブラックボックス最適化 / 多数目的性 / AREX-NSH / 政策の多様性 / PF-XC / 進化計算手法 / niching / AWA-ER |
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| Outline of Final Research Achievements |
In this study, we proposed new search methods for efficiently finding good approximate solutions in globally-multimodal optimization problems and new ones for efficiently searching for good approximate solution sets in many-objective optimization problems. From the viewpoint of searching for good approximate solutions in the globally-multimodal search space, we proposed new methods to discover big-valleys with the optimal solutions and to search for the best solution in each big-valley. We showed that the proposed methods showed better performance than conventional ones on benchmark problems and real-world ones. From the viewpoint of searching for good approximate solution sets in many-objective optimization problems, we proposed new multi-start search methods based on scalarization that are excellent in terms of the coverage. We demonstrated that the proposed methods outperformed conventional ones on benchmark problems.
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Report
(4 results)
Research Products
(33 results)
