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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aruchamy R, Vasantha KD, 2011. A comparative performance study on hybrid swarm model for micro array data. Int J Comput Appl, 30:10–14.
Baker CJ, Hume AL, 2003. Netted radar sensing. IEEE Aerosp Electron Syst Mag, 18(2):3–6. https://doi.org/10.1109/MAES.2003.1183861
Blake LV, 1986. Radar Range-Performance Analysis. Artech House, Inc., Norwood, MA, USA.
Difranco JV, Kaiteris C, 1981. Radar performance review in clear and jamming environments. IEEE Trans Aerosp Electron Syst, AES-17(5):701–710. https://doi.org/10.1109/TAES.1981.309102
Farahani SM, Abshouri AA, Nasiri B, et al., 2012. Some hybrid models to improve firefly algorithm performance. Int J Artif Intell, 8(12):97–117.
Fister I, Fister I Jr, Yang XS, et al., 2012. A comprehensive review of firefly algorithms. Swarm Evol Comput, 13:34–46. https://doi.org/10.1016/j.swevo.2013.06.001
Gandomi AH, Yang XS, Talatahari S, et al., 2013. Firefly algorithm with chaos. Commun Nonl Sci Numer Simul, 18(1):89–98. https://doi.org/10.1016/j.cnsns.2012.06.009
Gao S, 2008. Research on optimum deployment problem of radar. Proc ISECS Int Colloquium on Computing, Communication, Control, and Management, p.466–469. https://doi.org/10.1109/CCCM.2008.100
Hassanzadeh T, Faez K, Seyfi G, 2012. A speech recognition system based on structure equivalent fuzzy neural network trained by firefly algorithm. Proc Int Conf on Biomedical Engineering, p.63–67. https://doi.org/10.1109/ICoBE.2012.6178956
Hu CH, Jiang W, Wang TJ, 2010. Continuous ant algorithm based on cooperation in radar network optimization. Proc 17th Int Conf on Management Science & Engineering, p.224–233. https://doi.org/10.1109/ICMSE.2010.5719809
Kurdzo JM, Palmer RD, 2011. On the use of genetic algorithms for optimization of a multi-band, multi-mission radar network. Proc IEEE RadarCon, p.231–236. https://doi.org/10.1109/RADAR.2011.5960534
Kurdzo JM, Palmer RD, 2012. Objective optimization of weather radar networks for low-level coverage using a genetic algorithm. J Atmos Ocean Technol, 29(6):807–821. https://doi.org/10.1175/JTECH-D-11-00076.1
Lian XY, Zhang J, Chen C, et al., 2012. Three-dimensional deployment optimization of sensor network based on an improved particle swarm optimization algorithm. Proc 10th World Con gress on Intelligent Control and Automation, p.4395–4400. https://doi.org/10.1109/WCICA.2012.6359220
Liu WT, Fan ZY, 2011. Coverage optimization of wireless sensor networks based on chaos particle swarm algorithm. J Comput Appl, 31(2):338–340. https://doi.org/10.3724/SP.J.1087.2011.00338
Liu XX, 2012. Sensor deployment of wireless sensor networks based on ant colony optimization with three classes of ant transitions. IEEE Commun Lett, 16(10):1604–1607. https://doi.org/10.1109/LCOMM.2012.090312.120977
Luthra J, Pal SK, 2011. A hybrid firefly algorithm using genetic operators for the cryptanalysis of a monoalphabetic substitution cipher. Proc World Congress on Information and Communication Technologies, p.202–206. https://doi.org/10.1109/WICT.2011.6141244
Srinivas M, Patnaik LM, 1994. Genetic algorithms: a survey. Computer, 27(6):17–26. https://doi.org/10.1109/2.294849
Srinivasan R, 1986. Distributed radar detection theory. IEE Proc F Commun Radar Signal Process, 133(1):55–60. https://doi.org/10.1049/ip-f-1.1986.0010
Subutic M, Tuba M, Stanarevic N, 2012. Parallelization of the firefly algorithm for unconstrained optimization problems. Latest Adv Inform Sci Appl, 22(3):264–269.
Wang H, Cui ZH, Sun H, et al., 2017. Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft Comput, 21(18):5325–5339. https://doi.org/10.1007/s00500-016-2116-z
Yang L, Liang J, Liu WW, 2013. Graphical deployment strategies in radar sensor networks (RSN) for target detection. EURASIP J Wirel Commun Netw, 2013(1):55. https://doi.org/10.1186/1687-1499-2013-55
Yang LP, Xiong JJ, Cui J, 2009. Method of optimal deployment for radar netting based on detection probability. Proc Int Conf on Computational Intelligence and Software Engineering, p.1–5.https://doi.org/10.1109/CISE.2009.5364961
Yang XS, 2008. Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome, UK.
Yang XS, 2010. Nature-Inspired Metaheuristic Algorithms (2nd Ed.). Luniver Press, Frome, UK.
Yang XS, 2011. Metaheuristic optimization: algorithm analysis and open problems. Proc 10th Int SymponExperimental Algorithms, p.21–32. https://doi.org/10.1007/978-3-642-20662-7_2
Yoon Y, Kim YH, 2013. An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Trans Cybern, 43(5):1473–1483. https://doi.org/10.1109/TCYB.2013.2250955
Yu L, Liu K, Li KS, 2007. Ant colony optimization in continuous problem. Front Mech Eng China, 2(4):459–462. https://doi.org/10.1007/s11465-007-0079-6
Yu SH, Su SB, Lu QP, et al., 2014. A novel wise step strategy for firefly algorithm. Int J Comput Math, 91(12):2507–2513. https://doi.org/10.1080/00207160.2014.907405
Zhao CH, Yu ZQ, Chen P, 2007. Optimal deployment of nodes based on genetic algorithm in heterogeneous sensor networks. Proc Int Conf on Wireless Communications, Networking and Mobile Computing, p.2743–2746. https://doi.org/10.1109/WICOM.2007.681
Zheng GQ, Zheng Y, 2011. Radar netting technology & its development. Proc IEEE CIE Int Conf on Radar, p.933–937. https://doi.org/10.1109/CIE-Radar.2011.6159694
Author information
Authors and Affiliations
Corresponding author
Additional information
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)
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1800749
Key words
- Improved firefly algorithm
- Radar surveillance network
- Deployment optimization
- Unmanned aerial vehicle (UAV) invasion defense