| Project/Area Number |
18K11469
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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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| Review Section |
Basic Section 61040:Soft computing-related
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| Research Institution | Tamagawa University (2022)
Hiroshima University (2018-2021) |
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Principal Investigator |
ORITO Yukiko 玉川大学, 工学部, 准教授 (60364494)
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| Co-Investigator(Kenkyū-buntansha) |
花田 良子 関西大学, システム理工学部, 准教授 (30511711)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 進化計算 / 初期探索空間の限定 / 縁付きヘッセ行列 / 資産配分問題 / 大規模大域的最適化問題 / 探索空間の限定 |
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| Outline of Final Research Achievements |
In this research project, we tried to determine the search space region from the entire solution space for a number of evolutionary computation methods to efficiently search for the optimal solution in multidimensional large-scale optimization problems.
Specifically, by using the extreme value determination method of the bordered Hessian, which is one method for solving equality-constrained optimization problems, we developed an apporoach to narrow the search space region on the design variables for finding the promising search space regions that are expected to have the optimal solution.
From the viewpoints of the limitations of the mathematical expansion of bordered Hessian Matrix and the results of numerical experiments, for finding the promising search space region, we reveal the problems with the approach of narrowing the domain of design variables and then the effectiveness of the approach of reducing the number of design variables.
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| Academic Significance and Societal Importance of the Research Achievements |
進化計算の各手法は強力な最適解探索ツールであるが、実問題に多くある制約付きかつ多次元の大規模最適化問題において、その有効性は未知な部分が多い。このため、そのような大規模最適化問題においても進化計算の有効性を広く発信する必要がある。
本研究課題では、解空間全体から最適解が存在すると期待される探索空間の絞り込み方法の開発を行った。本研究課題の成果は、多様かつ煩雑な情報のデータ分析の必要性の高まりとともに、今後ますます高次元化される最適化問題において、進化計算により効率的に最適化するための探索領域限定方法の一つとしての貢献が期待できる。
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