| Project/Area Number |
17K06508
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Control engineering/System engineering
|
|---|
| Research Institution | Kindai University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2017-04-01 – 2022-03-31
|
| Project Status |
Completed (Fiscal Year 2021)
|
| Budget Amount *help |
¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
|
|---|
| Keywords | 機会制約問題 / 最適化手法 / 進化計算 / 標本抽出法 / 不確実性 / モンテカルロ法 / 生産システム工学 |
|---|
| Outline of Final Research Achievements |
In order to solve practical Chance Constrained Problems (CCPs), which are formulations of various real-world problems under uncertainties, it is necessary to estimate the probability that a candidate solution satisfies the constraint conditions using a large number of samples selected randomly from a probability distribution. In this study, we devised a method that can estimate the above probabilitiy from a much smaller number of samples than the conventional random sampling method. Furthermore, by combining the above sample reduction method and an evolutionary algorithm called differential evolution, we developed a global optimization method for CCPs and confirmed its effectiveness on several practical CCPs.
|
| Academic Significance and Societal Importance of the Research Achievements |
実世界の不確実性を含む諸問題は機会制約問題（CCP）に定式化できる。しかし、従来の確率計画法に基づく最適化手法では、解候補が制約条件を満たす確率を膨大な数の標本から推定する必要があり、現実的なCCPを解くことが難しい。本研究で開発した現実的なCCPに対する最適化手法は、CCPの応用分野を広げるものであり、不確実性を含む諸問題の解決に寄与することが期待される。また、新たに考案した標本数の削減法は、経験分布の構築や確率の推定のほか、機械学習における教師データの作成など、様々な分野での利用が考えられる。
|
