Study on Effective Learning on Multi-objective Sequential Optimization and its Applications
| Project/Area Number |
16K01269
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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 |
Social systems engineering/Safety system
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| Research Institution | Kansai University |
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Principal Investigator |
YUN YEBOON 関西大学, 環境都市工学部, 教授 (10325326)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 逐次近似最適化 / 機械学習 / モデル予測 / メター学習 / meta-learning / RBF neural networks / hyper-parameter / 多目的モデル予測制御 / サポートベクターマシン / モデル予測制御 / 多目的最適化 / ニューラルネットワーク / 遺伝的アルゴリズム / メタモデル / 計算知能 / 工学設計 / 多目的逐次近似最適化 |
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| Outline of Final Research Achievements |
In optimization with high cost objective function, an approximate function is used as a surrogate. For generating an approximate function based on some sample points, machine learning such as Radial basis networks and Support vector machines is effective. The precision of approximate functions depends on hyper-parameters used in basis and kernel functions, which is deeply related to learning. The new methods of meta-learning in SVM and RBF networks were proposed with the aim of generating an approximate function of high accuracy with small number of function evaluations. Furthermore, for multi-objective optimal control problems based on meta-model under a dynamic environment, this study suggested the method of combining machine learning methods and predetermined linear model in order to construct more accurate and stable model prediction, Finally, the effectiveness of the proposed methods in this research was validated through some numerical examples and engineering design problems.
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| Academic Significance and Societal Importance of the Research Achievements |
計算知能や多目的最適化法に関し、そのものを対象とした研究はすでに多く存在する。しかし、基礎的研究にとどまることが多く、実際の応用という観点からの検討が不十分であるか、逆に理論的な根拠は希薄であるが、これまでの経験に基づく方法による試行錯誤的な研究も多いというのが現状である。さらに、GAやPSOの進化的アルゴリズムを用いたパレート解の生成法に関する研究は活発であるが、既存の方法では多くの計算回数を要する。これらのことを総合的に踏まえたうえで、多角な観点から、理論のみならず実問題へ適用性も考慮した成果であり、学術的にも実用的にも有意義であると考える。
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Report
(5 results)
Research Products
(16 results)
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[Journal Article] On selecting hyper-parameters in RBF networks2019
Author(s)
S. Yoshida，Y．B. Yun, H．Nakayama, M．Yoon
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Journal Title
International Conference on Nonlinear Analysis and Convex Analysis and International Conference on Optimization: Techniques and Applications
Volume: -
Pages: 185-185
Related Report
Peer Reviewed / Int'l Joint Research
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[Journal Article] Multi-Objective Model Predictive Control2018
Author(s)
Yeboon Yun, Hirotaka Nakayama, Min Yoon
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Journal Title
Proceedings of 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems
Volume: 1
Pages: 304-308
DOI
Related Report
Peer Reviewed / Int'l Joint Research
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