Abstract
The threats and challenges of unmanned aerial vehicle (UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm (FA) and four recently proposed FA variants.
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Project supported by the National Key Laboratory of CNS/ATM, Beijing Key Laboratory for Network-Based Cooperative Air Traffic Management, and the National Natural Science Foundation of China (No. 71731001)
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Zhang, Xj., Jia, W., Guan, Xm. et al. Optimized deployment of a radar network based on an improved firefly algorithm. Frontiers Inf Technol Electronic Eng 20, 425–437 (2019). https://doi.org/10.1631/FITEE.1800749
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DOI: https://doi.org/10.1631/FITEE.1800749
Key words
- Improved firefly algorithm
- Radar surveillance network
- Deployment optimization
- Unmanned aerial vehicle (UAV) invasion defense