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タイトル: | Reinforcement learning for optimum design of a plane frame under static loads |
著者: | Hayashi, Kazuki https://orcid.org/0000-0002-4026-8234 (unconfirmed) Ohsaki, Makoto https://orcid.org/0000-0003-4935-8874 (unconfirmed) |
著者名の別形: | 林, 和希 大﨑, 純 |
キーワード: | Steel frame Cross-sectional optimization Machine learning Reinforcement learning Q-learning Artificial neural network |
発行日: | Jul-2021 |
出版者: | Springer Nature |
誌名: | Engineering with Computers |
巻: | 37 |
開始ページ: | 1999 |
終了ページ: | 2011 |
抄録: | A new method is presented for optimum cross-sectional design of planar frame structures combining reinforcement learning (RL) and metaheuristics. The method starts from RL jointly using artificial neural network so that the action taker, or the agent, can choose a proper action on which members to be increased, reduced or kept their size. The size of the neural network is compressed into small numbers of inputs and outputs utilizing story-wise decomposition of the frame. The trained agent is used in the process of generating a neighborhood solution during optimization with simulated annealing (SA) and particle swarm optimization (PSO). Because the proposed method is able to explore the solution space efficiently, better optimal solutions can be found with less computational cost compared with those obtained solely by metaheuristics. Utilization of RL agent also leads to high-quality optimal solutions regardless of variation of parameters of SA and PSO or initial solution. Furthermore, once the agent is trained, it can be applied to optimization of other frames with different numbers of stories and spans. |
著作権等: | This is a post-peer-review, pre-copyedit version of an article published in Engineering with Computers. The final authenticated version is available online at: https://doi.org/10.1007/s00366-019-00926-7. The full-text file will be made open to the public on 7 January 2021 in accordance with publisher's 'Terms and Conditions for Self-Archiving'. This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
URI: | http://hdl.handle.net/2433/264258 |
DOI(出版社版): | 10.1007/s00366-019-00926-7 |
出現コレクション: | 学術雑誌掲載論文等 |
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