Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade Shape
A multi-objective turbine shape optimization method based on deep reinforcement learning (DRL) is proposed. DRL-based optimization methods are useful for repeating optimization tasks that arise in applications such as the design of turbines and automotive parts. In conventional research, DRL is appl...
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MDPI AG
2024-09-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/5/4/85 |
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| author | Kazuo Yonekura Ryusei Yamada Shun Ogawa Katsuyuki Suzuki |
| author_facet | Kazuo Yonekura Ryusei Yamada Shun Ogawa Katsuyuki Suzuki |
| author_sort | Kazuo Yonekura |
| collection | DOAJ |
| description | A multi-objective turbine shape optimization method based on deep reinforcement learning (DRL) is proposed. DRL-based optimization methods are useful for repeating optimization tasks that arise in applications such as the design of turbines and automotive parts. In conventional research, DRL is applied only to single-optimization tasks. In this study, a multi-objective optimization method using improvements in hypervolume is proposed. The proposed method is applied to a benchmark problem and a turbine optimization problem. It succeeded in efficiently solving the problems, and Pareto optimal solutions are obtained. |
| format | Article |
| id | doaj-art-67b6e6f0533941aa970c2ac52e781616 |
| institution | OA Journals |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-67b6e6f0533941aa970c2ac52e7816162025-08-20T02:01:01ZengMDPI AGAI2673-26882024-09-01541731174210.3390/ai5040085Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade ShapeKazuo Yonekura0Ryusei Yamada1Shun Ogawa2Katsuyuki Suzuki3Department of Systems Innovation, The University of Tokyo, Tokyo 113-8654, JapanDepartment of Systems Innovation, The University of Tokyo, Tokyo 113-8654, JapanDepartment of Systems Innovation, The University of Tokyo, Tokyo 113-8654, JapanDepartment of Systems Innovation, The University of Tokyo, Tokyo 113-8654, JapanA multi-objective turbine shape optimization method based on deep reinforcement learning (DRL) is proposed. DRL-based optimization methods are useful for repeating optimization tasks that arise in applications such as the design of turbines and automotive parts. In conventional research, DRL is applied only to single-optimization tasks. In this study, a multi-objective optimization method using improvements in hypervolume is proposed. The proposed method is applied to a benchmark problem and a turbine optimization problem. It succeeded in efficiently solving the problems, and Pareto optimal solutions are obtained.https://www.mdpi.com/2673-2688/5/4/85deep reinforcement learningrepeating optimization taskmulti-objective optimizationPareto solutions |
| spellingShingle | Kazuo Yonekura Ryusei Yamada Shun Ogawa Katsuyuki Suzuki Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade Shape AI deep reinforcement learning repeating optimization task multi-objective optimization Pareto solutions |
| title | Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade Shape |
| title_full | Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade Shape |
| title_fullStr | Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade Shape |
| title_full_unstemmed | Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade Shape |
| title_short | Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade Shape |
| title_sort | hypervolume based multi objective optimization method applying deep reinforcement learning to the optimization of turbine blade shape |
| topic | deep reinforcement learning repeating optimization task multi-objective optimization Pareto solutions |
| url | https://www.mdpi.com/2673-2688/5/4/85 |
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