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Many-objective rapid optimization of reactor shielding design based on NSGA - III

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成果类型:
期刊论文
作者:
Li, Xiaomeng;Song, Yingming;Mao, Jie;Zhang, Zehuan
通讯作者:
Yingming Song
作者机构:
[Li, Xiaomeng; Mao, Jie; Song, Yingming] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.
[Zhang, Zehuan] Tsinghua Univ, Inst Nucl Energy & New Energy Technol, Beijing 100084, Peoples R China.
[Zhang, Zehuan] Beijing Key Lab Nucl Detect & Measurement Technol, Beijing 100084, Peoples R China.
通讯机构:
[Yingming Song] S
School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
语种:
英文
关键词:
Many-objective optimization;Neural network;NSGA - III;Shielding design
期刊:
Annals of Nuclear Energy
ISSN:
0306-4549
年:
2022
卷:
177
页码:
109322
机构署名:
本校为第一机构
院系归属:
核科学技术学院
摘要:
In this paper, a many-objective optimization method for reactor shielding design coupled with NSGA -III and neural network is proposed. By establishing the many-objective optimization model, using Monte Carlo method to calculate samples to train neural network, and the prediction results of neural network are used as the parameters of fitness function for many-objective optimization. The coupling between neural network and NSGA -III is realized, and the Pareto optimal solution of many-objective optimization of reactor shielding design is obtained. The results show that the method of neural net...

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