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.

基于改进萤火虫算法的雷达网络最优化部署

[1]Aruchamy R, Vasantha KD, 2011. A comparative performance study on hybrid swarm model for micro array data. Int J Comput Appl, 30:10-14.

[2]Baker CJ, Hume AL, 2003. Netted radar sensing. IEEE Aerosp Electron Syst Mag, 18(2):3-6.

[3]Blake LV, 1986. Radar Range-Performance Analysis. Artech House, Inc., Norwood, MA, USA.

[4]Difranco JV, Kaiteris C, 1981. Radar performance review in clear and jamming environments. IEEE Trans Aerosp Electron Syst, AES-17(5):701-710.

[5]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.

[6]Fister I, Fister IJr, Yang XS, et al., 2012. A comprehensive review of firefly algorithms. Swarm Evol Comput, 13:34-46.

[7]Gandomi AH, Yang XS, Talatahari S, et al., 2013. Firefly algorithm with chaos. Commun Nonl Sci Numer Simul, 18(1):89-98.

[8]Gao S, 2008. Research on optimum deployment problem of radar. Proc ISECS Int Colloquium on Computing, Communication, Control, and Management, p.466-469.

[9]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.

[10]Hu CH, Jiang W, Wang TJ, 2010. Continuous ant algorithm based on cooperation in radar network optimization. Proc 17$^rm th$ Int Conf on Management Science & Engineering, p.224-233.

[11]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.

[12]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.

[13]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 10$^rm th$ World Congress on Intelligent Control and Automation, p.4395-4400.

[14]Liu WT, Fan ZY, 2011. Coverage optimization of wireless sensor networks based on chaos particle swarm algorithm. J Comput Appl, 31(2):338-340.

[15]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.

[16]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.

[17]Srinivas M, Patnaik LM, 1994. Genetic algorithms: a survey. Computer, 27(6):17-26.

[18]Srinivasan R, 1986. Distributed radar detection theory. IEE Proc F Commun Radar Signal Process, 133(1):55-60.

[19]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.

[20]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.

[21]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.

[22]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.

[23]Yang XS, 2008. Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome, UK.

[24]Yang XS, 2010. Nature-Inspired Metaheuristic Algorithms (2^nd Ed.). Luniver Press, Frome, UK.

[25]Yang XS, 2011. Metaheuristic optimization: algorithm analysis and open problems. Proc 10^th Int Symp on Experimental Algorithms, p.21-32.

[26]Yoon Y, Kim YH, 2013. An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Trans Cybern, 43(5):1473-1483.

[27]Yu L, Liu K, Li KS, 2007. Ant colony optimization in continuous problem. Front Mech Eng China, 2(4):459-462.

[28]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.

[29]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.

[30]Zheng GQ, Zheng Y, 2011. Radar netting technology & its development. Proc IEEE CIE Int Conf on Radar, p.933-937.