Dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radar
Abstract Multiple objectives optimization of frequency selective surface (FSS) structures is challenging in electromagnetic wave filter design. For example, one of the sub-objectives, the sidelobe level (SLL), is critical to directional anti-interference, which is complicated and becomes the bottlen...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-20167-x |
| _version_ | 1811226751184928768 |
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| author | Yuan Pei Anran Yu Jiajun Qin Ruichen Yi Xianxi Yu Shaobo Liu Guangrui Zhu Chunqin Zhu Xiaoyuan Hou |
| author_facet | Yuan Pei Anran Yu Jiajun Qin Ruichen Yi Xianxi Yu Shaobo Liu Guangrui Zhu Chunqin Zhu Xiaoyuan Hou |
| author_sort | Yuan Pei |
| collection | DOAJ |
| description | Abstract Multiple objectives optimization of frequency selective surface (FSS) structures is challenging in electromagnetic wave filter design. For example, one of the sub-objectives, the sidelobe level (SLL), is critical to directional anti-interference, which is complicated and becomes the bottleneck for radar design. Here, we established a dynamic algorithm for fitness function to automatically adjust the weights of multiple objectives in the optimization process of FSS structures. The dynamic algorithm could efficiently evaluate the achieving probability of sub-objectives according to the statistical analysis of the latest individual distribution so that the fitness function could automatically adjusted to focus on the sub-objective difficult to optimize, such as SLL. Computational results from the dynamic algorithm showed that the efficiency of multi-objective optimization was greatly improved by 213%, as compared to the fixed-weighted algorithm of the fitness function. Specifically for SLL, the efficiency rate increased even better, up to 315%. More interestingly, the FSS structures were most improved while picking median value or golden section value as the reference value. Taken together, the current study indicated that the dynamic algorithm with fitness function might be a better choice for FSS structural optimization with SLL suppression and potentially for the better design of lower SLL radar. |
| first_indexed | 2024-04-12T09:30:36Z |
| format | Article |
| id | doaj.art-bbc70847360e4f5fb554635b537c76e2 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T09:30:36Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-bbc70847360e4f5fb554635b537c76e22022-12-22T03:38:23ZengNature PortfolioScientific Reports2045-23222022-10-0112111010.1038/s41598-022-20167-xDynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radarYuan Pei0Anran Yu1Jiajun Qin2Ruichen Yi3Xianxi Yu4Shaobo Liu5Guangrui Zhu6Chunqin Zhu7Xiaoyuan Hou8State Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) and Collaborative Innovation Center of Advanced Microstructures, Fudan UniversityCentre of Micro Nano System, School of Information Science and Technology, Fudan UniversityDepartment of Physics, Chemistry and Biology (IFM), Linköping UniversityState Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) and Collaborative Innovation Center of Advanced Microstructures, Fudan UniversityState Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) and Collaborative Innovation Center of Advanced Microstructures, Fudan UniversityState Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) and Collaborative Innovation Center of Advanced Microstructures, Fudan UniversityState Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) and Collaborative Innovation Center of Advanced Microstructures, Fudan UniversityState Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) and Collaborative Innovation Center of Advanced Microstructures, Fudan UniversityState Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) and Collaborative Innovation Center of Advanced Microstructures, Fudan UniversityAbstract Multiple objectives optimization of frequency selective surface (FSS) structures is challenging in electromagnetic wave filter design. For example, one of the sub-objectives, the sidelobe level (SLL), is critical to directional anti-interference, which is complicated and becomes the bottleneck for radar design. Here, we established a dynamic algorithm for fitness function to automatically adjust the weights of multiple objectives in the optimization process of FSS structures. The dynamic algorithm could efficiently evaluate the achieving probability of sub-objectives according to the statistical analysis of the latest individual distribution so that the fitness function could automatically adjusted to focus on the sub-objective difficult to optimize, such as SLL. Computational results from the dynamic algorithm showed that the efficiency of multi-objective optimization was greatly improved by 213%, as compared to the fixed-weighted algorithm of the fitness function. Specifically for SLL, the efficiency rate increased even better, up to 315%. More interestingly, the FSS structures were most improved while picking median value or golden section value as the reference value. Taken together, the current study indicated that the dynamic algorithm with fitness function might be a better choice for FSS structural optimization with SLL suppression and potentially for the better design of lower SLL radar.https://doi.org/10.1038/s41598-022-20167-x |
| spellingShingle | Yuan Pei Anran Yu Jiajun Qin Ruichen Yi Xianxi Yu Shaobo Liu Guangrui Zhu Chunqin Zhu Xiaoyuan Hou Dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radar Scientific Reports |
| title | Dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radar |
| title_full | Dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radar |
| title_fullStr | Dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radar |
| title_full_unstemmed | Dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radar |
| title_short | Dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radar |
| title_sort | dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface for better design of radar |
| url | https://doi.org/10.1038/s41598-022-20167-x |
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