Optimized deployment of a radar network based on an improved firefly algorithm

  • Xue-jun Zhang
  • Wei Jia
  • Xiang-min Guan
  • Guo-qiang Xu
  • Jun Chen
  • Yan-bo ZhuEmail author


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.

Key words

Improved firefly algorithm Radar surveillance network Deployment optimization Unmanned aerial vehicle (UAV) invasion defense 

CLC number

TN954 O224 


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Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronic and Information Engineering, Beihang UniversityNational Key Laboratory of CNS/ATMBeijingChina
  2. 2.Department of General AviationCivil Aviation Management Institute of ChinaBeijingChina
  3. 3.Lincoln School of EngineeringUniversity of LincolnLincolnUK

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