改进布谷鸟算法用于阵列天线方向图优化
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摘要
针对目前群智能优化及进化计算算法在解决直线形阵列天线辐射方向图的旁瓣电平抑制和零陷控制问题时易出现求解精度不高及收敛速度较慢的问题,提出了一种基于扩散变异策略的布谷鸟搜索算法。该算法首先利用聚集扩散策略来提高算法的全局搜索性能,然后引入遗传算法中的基因突变策略,用以提高种群的多样性,进而提高算法的局部搜索能力。仿真结果表明,所提算法在抑制直线形阵列天线方向图的旁瓣电平和控制零陷方面与萤火虫算法、粒子群算法、传统布谷鸟搜索算法、金斑蝶优化算法和蚯蚓优化算法相比,具有更快的收敛速度和更高的求解精度。
To solve the problems of sidelobe level(SLL)suppression and nulls control of the linear antenna arrays(LAA),a spread variation cuckoo search(SVCS)algorithm is proposed.First,the SVCS uses the aggregated diffusion strategy to improve the possibility of obtaining the global optimal solutions of the algorithm.Second,the gene mutation method of the genetic algorithm is introduced to improve the population diversity so as to avoid the algorithm falling into the local optima.Simulation results show that the proposed SVCS has a better performance in terms of the convergence rate and accuracy compared with the firefly algorithm,the particle swarm optimization,the conventional cuckoo search algorithm,the monarch butterfly optimization algorithm and the earthworm optimization algorithm for reducing the SLL of the LAA.Moreover,the SVCS also has a better performance for solving the joint optimization problem of SLL suppression and nulls control compared with the above mentioned benchmark algorithms.
To solve the problems of sidelobe level(SLL)suppression and nulls control of the linear antenna arrays(LAA),a spread variation cuckoo search(SVCS)algorithm is proposed.First,the SVCS uses the aggregated diffusion strategy to improve the possibility of obtaining the global optimal solutions of the algorithm.Second,the gene mutation method of the genetic algorithm is introduced to improve the population diversity so as to avoid the algorithm falling into the local optima.Simulation results show that the proposed SVCS has a better performance in terms of the convergence rate and accuracy compared with the firefly algorithm,the particle swarm optimization,the conventional cuckoo search algorithm,the monarch butterfly optimization algorithm and the earthworm optimization algorithm for reducing the SLL of the LAA.Moreover,the SVCS also has a better performance for solving the joint optimization problem of SLL suppression and nulls control compared with the above mentioned benchmark algorithms.
引文
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[12]刘燕,焦永昌,张亚明,等.入侵杂草优化算法用于阵列天线方向图综合[J].西安电子科技大学学报,2014,41(1):29-33.LIU Yan,JIAO Yongchang,Zhang Yaming,et al.Application of the Invasive Weed Optimization Algorithm to the Pattern Synthesis of Array Antennas[J].Journal of Xidian University,2014,41(1):29-33.
[13]GANDOMI A H,YANG X S,ALAVI A H.Cuckoo Search Algorithm:a Metaheuristic Approach to Solve Structural Optimization Problems[J].Engineering with Computers,2013,29(1):17-35.
[14]兰少峰,刘升.布谷鸟搜索算法研究综述[J].计算机工程与设计,2015,36(4):1063-1067.LAN Shaofeng,LIU Sheng.Overview of Research on Cuckoo Search Algorithm[J].Computer Engineering and Design,2015,36(4):1063-1067.
[15]WANG G G,DEB S,CUI Z.Monarch Butterfly Optimization[J].Neural Computing and Applications,2015.https://DOI.org/10.1007/soo521-015-1923-y.
[16]WANG G G,DEB S,COELHO L D S.Earthworm Optimization Algorithm:a Bio-inspired Metaheuristic Algorithm for Global Optimization Problems[J].International Journal of Bio_Inspired Computation,2015.DOI:10.1504/IJBIC.2015.10004283.
[2]YU T,YIN C Y,LIU H.Analysis on Spherical Conformal Microstrip Antenna Array by Characteristic Basis Function Method[J].Acta Physica Sinica,2014,63(23):230701.
[3]CAI W,WANG P,LI Y,et al.Deployment Optimization of Uniform Linear Antenna Arrays for a Two-Path Millimeter Wave Communication System[J].IEEE Communications Letters,2015,19(4):669-672.
[4]SUN G,LIU Y H,LIANG S,et al.Sidelobe and Energy Optimization Array Node Selection Algorithm for Collaborative Beamforming in Wireless Sensor Networks[J].IEEE Access,2017,6:2515-2530.
[5]KHALID A,SHEIKH S A,SHAH I U H,et al.Synthesis of Linear Antenna Array Using Genetic Algorithm to Reduce Peak Sidelobe Level[C]//Proceedings of the 2015 9th International Conference on Electrical and Electronics Engineering.Piscataway:IEEE,2015:346-350.
[6]SAKONKANAPONG A,TODNATEE S,PHONGCHAROENAPANICH C.Multi-parents Genetic Algorithm Optimization Scheme for Linear Array Antenna Synthesis[C]//Proceedings of the 2017IEEE International Conference on Computational Electromagnetics.Piscataway:IEEE,2017:266-268.
[7]BANERJEE S,MANDAL D.Array Pattern Optimization for Steerable Linear Isotropic Antenna Array using Novel Particle Swarm Optimization[J].Journal of Electromagnetic Waves and Applications,2017,31(2):182-208.
[8]王维博,冯全源.粒子群算法在阵列天线方向图综合中的应用[J].西安电子科技大学学报,2011,38(3):175-180.WANG Weibo,FENG Quanyuan.Application of PSO Algorithm in Pattern Synthesis for Antenna Arrays[J].Journal of Xidian Unviersity,2011,38(3):175-180.
[9]SAXENA P,KOTHARI A.Ant Lion Optimization Algorithm to Control Side Lobe Level and Null Depths in Linear Antenna Arrays[J].AEU-International Journal of Electronics and Communications,2016,70(9):1339-1349.
[10]SUN G,LIU Y H,LIANG S,et al.Beam Pattern Design of Circular Antenna Array via Efficient Biogeography-based Optimization[J].AEU-International Journal of Electronics and Communications,2017,79:275-285.
[11]BAUERNFEIND T,BAUMGARTNER P,BíRóO,et al.PEEC-based Multi-objective Synthesis of Non-uniformly Spaced Linear Antenna Arrays[C]//Proceedings of the 2017 17th Biennial Conference on Electromagnetic Field Computation.Piscataway:IEEE,2017:7815912.
[12]刘燕,焦永昌,张亚明,等.入侵杂草优化算法用于阵列天线方向图综合[J].西安电子科技大学学报,2014,41(1):29-33.LIU Yan,JIAO Yongchang,Zhang Yaming,et al.Application of the Invasive Weed Optimization Algorithm to the Pattern Synthesis of Array Antennas[J].Journal of Xidian University,2014,41(1):29-33.
[13]GANDOMI A H,YANG X S,ALAVI A H.Cuckoo Search Algorithm:a Metaheuristic Approach to Solve Structural Optimization Problems[J].Engineering with Computers,2013,29(1):17-35.
[14]兰少峰,刘升.布谷鸟搜索算法研究综述[J].计算机工程与设计,2015,36(4):1063-1067.LAN Shaofeng,LIU Sheng.Overview of Research on Cuckoo Search Algorithm[J].Computer Engineering and Design,2015,36(4):1063-1067.
[15]WANG G G,DEB S,CUI Z.Monarch Butterfly Optimization[J].Neural Computing and Applications,2015.https://DOI.org/10.1007/soo521-015-1923-y.
[16]WANG G G,DEB S,COELHO L D S.Earthworm Optimization Algorithm:a Bio-inspired Metaheuristic Algorithm for Global Optimization Problems[J].International Journal of Bio_Inspired Computation,2015.DOI:10.1504/IJBIC.2015.10004283.
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