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Dynamic algorithm for fitness function greatly improves the optimization efficiency of frequency selective surface
Yuan Pei1, Anran Yu2, Jiajun Qin3
1State Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) and Collaborative Innovation Center of Advanced Microstructures, Fudan University, Shanghai, 200433, China.
Scientific Reports
|October 5, 2022
Summary
A new dynamic algorithm optimizes frequency selective surface (FSS) structures by adjusting objective weights. This method significantly improves multi-objective optimization efficiency, particularly for reducing sidelobe levels (SLL) in radar design.
Area of Science:
- Electromagnetics
- Materials Science
- Computational Engineering
Background:
- Optimizing frequency selective surface (FSS) structures for multiple objectives is complex in electromagnetic wave filter design.
- Sidelobe level (SLL) is a critical but challenging sub-objective for directional anti-interference, impacting radar system design.
Purpose of the Study:
- To establish a dynamic algorithm for fitness function to automatically adjust objective weights during FSS structure optimization.
- To enhance the efficiency of multi-objective optimization for FSS structures, focusing on difficult sub-objectives like SLL.
Main Methods:
- Developed a dynamic algorithm that statistically analyzes individual distributions to adjust fitness function weights.
- Implemented adaptive weighting to prioritize sub-objectives with lower achievement probability, such as SLL.
- Evaluated optimization efficiency compared to fixed-weighted algorithms, using median and golden section values as references.
Main Results:
- The dynamic algorithm improved multi-objective optimization efficiency by 213% compared to fixed-weighted methods.
- SLL optimization efficiency saw a remarkable increase of up to 315%.
- Optimal FSS structure improvements were observed when using median or golden section values as reference points.
Conclusions:
- The dynamic algorithm offers a superior approach for FSS structural optimization, especially for SLL suppression.
- This method holds potential for designing advanced radar systems with reduced sidelobe levels.
- The adaptive fitness function strategy enhances the overall efficiency and effectiveness of multi-objective optimization in electromagnetic applications.
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