基于混沌扰动机制粒子群算法的战场频率分配方法

doi:10.16182/j.issn1004731x.joss.20-0336

摘要/Abstract

摘要： 为在战场电磁环境下进行科学分配频率,减少各部队用频设备之间的相互干扰,提出一种基于混沌扰动机制粒子群算法的频率分配方法,将战场频率分配转化为带约束条件的最优频谱资源查找求解问题,建立以干扰代价最低为目标的频率分配模型,通过改进的粒子群算法进行频率分配。该算法引入混沌扰动机制,提高种群多样性和算法全局寻优能力,避免算法陷入局部最优。通过实验表明,在频率分配过程中该算法是可行的,并且其收敛性和解集的多样性均明显优于粒子群算法。

关键词: 频率分配, 粒子群算法, 混沌扰动, 干扰代价, 约束条件

Abstract: In order to carry out the frequency allocation in the electromagnetic environment of battlefield and reduce the frequency equipment interference of various forces, a frequency allocation method based on chaotic perturbation mechanism particle swarm optimization algorithm is proposed. Which transforms battlefield frequency allocation into the optimal spectrum resource search and solution problem with constraints. The frequency allocation model with the lowest interference cost is built and the frequency allocation through the improved particle swarm optimization algorithm is carries out. The chaotic perturbation mechanism is introduced to improve the population diversity and the global optimization ability of the algorithm, which avoids the algorithm falling into the local optimization. Experiments show that the algorithm is feasible and the convergency and diversity of the solution are obviously better than those of particle swarm optimization algorithm.

Key words: frequency allocation, Particle Swarm Optimization(PSO), chaotic perturbation, interfere with the price, constraint condition

中图分类号:

TP391

牛侃, 李冰, 付强. 基于混沌扰动机制粒子群算法的战场频率分配方法[J]. 系统仿真学报, 2021, 33(8): 1905-1913.

Niu Kan, Li Bing, Fu Qiang. Method of Battlefield Frequency Allocation Based on Chaotic Perturbation Mechanism Particle Swarm Optimization Algorithm[J]. Journal of System Simulation, 2021, 33(8): 1905-1913.

参考文献

[1] 罗文坚, 曹先彬, 王煦法. 用一种免疫遗传算法求解频率分配问题[J]. 电子学报, 2003, 31(6): 915-917.
Luo Wenjian, Cao Xianbin, Wang Xufa.Solving Frequency Assignment Using an Immune Genetic Algorithm[J]. Acta Electronica Sinica, 2003, 31(6): 915-917.
[2] Ngo C Y, Li V O K. Fixed Channel Assignment in Cellular Radio Networks Using a Modified Genetic Algorithm[J]. IEEE Transactions on Vehicular Technology (S0018-9545), 1998, 47(1): 163-172.
[3] 王云璐, 戴伏生, 李怀远. 遗传粒子群算法在频率分配中的应用[J]. 信息技术, 2016(7): 168-171, 175.
Wang Yunlu, Dai Fusheng, Li Huaiyuan.Application of Gentic and Particle Swarm Algorithm in Frequency Assignment[J]. Information Technology, 2016(7): 168-171, 175.
[4] 王川, 张剑. 一种基于蚁群算法的海战场分布式频率分配方法[J]. 计算机与数字工程, 2018, 46(2): 281-283.
Wang Chuan, Zhang Jian.A Method of Ant Algorithm in Distributed Frequency Assignment on Sea Battlefield[J]. Computer & Digital Engineering, 2018, 46(2): 281-283.
[5] 于江, 贺赛飞, 张凤霞, 等. 模拟退火算法在战场频率资源分配中的应用[J]. 中国无线电, 2018(1): 34-38.
Yu Jiang, He Saifei, Zhang Fengxia, et al.Application of Simulated Annealing Algorithm in Battlefield Frequency Resource Allocation[J]. China Radio, 2018(1): 34-38.
[6] 王新增, 刘佳楠, 肖金保, 等. 基于粒子群算法的电磁频率分配方法研究[J]. 现代电子技术, 2013, 36(17): 5-8.
Wang Xinzeng, Liu Jia'nan, Xiao Jinbao, et al.Method of Electro-magnetism Frequency Assignment Based on Particle Swarm Optimization Algorithm[J]. Modern Electronics Technique, 2013, 36(17): 5-8.
[7] 李东海, 王勇军. 基于粒子群优化的蚁群算法在战场电磁频率分配中的应用[J]. 指挥控制与仿真, 2016, 38(2): 48-51.
Li Donghai, Wang Yongjun.Application of Ant Colony Algorithm Based on Particle Swarm in Frequency Assignment on Battlefield[J]. Command Control ＆ Simulation, 2016, 38(2): 48-51.
[8] Aardal K I, Hoesel S M, Mannino K C.Models and Solution Techniques for Frequency Assignment Problems[J]. Quarterly Journal of the Belgian, French and Italian Operations Research Societies (S1619-4500), 2003, 1(4): 261-317.
[9] 牛侃, 董旭, 吴天昊. 调频广播互调信号对军队航空通信频段的干扰分析[J]. 数字通信世界, 2019(1): 57.
Niu Kan, Dong Xu, Wu Tianhao.Interference analysis of FM Radio Intermodulation Signal to Military Aviation Communication Frequency Band[J]. Digital Communication World, 2019(1): 57.
[10] 徐雪飞, 李建华, 沈迪, 等. 基于量子遗传算法的航空通信频率动态分配[J]. 电讯技术, 2015, 55(12): 1311-1317.
Xu Xuefei, Li Jianhua, Shen Di, et al.Dynamic Aeronautical Communication Frequency Assignment Based on Quantum Genetic Algorithm[J]. Telecommunication Engineering, 2015, 55(12): 1311-1317.
[11] 黄澄. 基于粒子群优化的毫米波通信网络资源分配算法[J]. 物联网技术, 2019(11): 33-35.
Huang Cheng.Resource Allocation Algorithm for Millimeter Wave Communication Network Based on Particle Swarm Optimization Algorithm[J]. Internet of Things Technologies, 2019(11): 33-35.
[12] 牛侃. 基于博弈模型的云服务资源调度技术研究[D]. 郑州: 解放军信息工程大学, 2017.
Niu Kan.Research on Scheduling Technology of Cloud Service Resource Based on Game Model[D]. Zhengzhou: PLA Information Engineering University, 2017.
[13] Mostaghim S, Teich J.Strategies for Finding Good Local Guides in Multi-objective Particle Swarm Optimization[C]// 2003 IEEE Swarm Intelligence Symposium Indianapolis. Dallas: ACM Press, 2003: 26-33.
[14] Fang Y Q, Wang F, Ge J W.A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing[C]// 2th International Conference on Web Information Systems and Mining, Berlin, Germany: Springer-Verlag, 2010: 156-162.

[1]	马立新, 程颍. 计及可中断负荷的园区综合能源系统优化调度[J]. 系统仿真学报, 2022, 34(4): 817-825.
[2]	张凯庆, 嵇启春. 速度时变的多中心半开放式车辆路径问题研究[J]. 系统仿真学报, 2022, 34(4): 836-846.
[3]	陈魁, 毕利, 王文雅. 柔性作业车间AGV与机器双资源集成调度研究[J]. 系统仿真学报, 2022, 34(3): 461-469.
[4]	孙晓安, 栾小丽, 刘飞. 基于智能优化灰色模型的电子固废预测[J]. 系统仿真学报, 2022, 34(3): 536-542.
[5]	魏腾飞, 潘庭龙. 基于改进PSO优化LSTM网络的短期电力负荷预测[J]. 系统仿真学报, 2021, 33(8): 1866-1874.
[6]	孟建军, 王终军, 胥如迅, 李德仓. 基于模糊复合策略的高速列车主动悬挂控制研究[J]. 系统仿真学报, 2021, 33(7): 1554-1564.
[7]	魏瑞轩, 吴子沉. 无人机集群实时任务分配方法研究[J]. 系统仿真学报, 2021, 33(7): 1574-1581.
[8]	刘晓琳, 吴竟祎. 飞机舵机电液加载系统控制补偿器设计与仿真[J]. 系统仿真学报, 2021, 33(5): 1031-1038.
[9]	汪润鸿, 王红军, 邹湘军, 曾泽钦, 李慧, 黄钊丰, 刘伟良. 基于混合粒子群算法的组合式变速箱传动比优化研究[J]. 系统仿真学报, 2021, 33(4): 825-836.
[10]	陈魁, 毕利. 改进粒子群算法在考虑运输时间下的FJSP研究[J]. 系统仿真学报, 2021, 33(4): 845-853.
[11]	孙宇贞, 唐毅伟, 李帅. 基于改进粒子群算法的超超临界燃煤机组负荷系统建模[J]. 系统仿真学报, 2021, 33(4): 875-882.
[12]	胡小曼, 王艳, 纪志成. 模糊信息粒化与改进RVM的滚动轴承寿命预测[J]. 系统仿真学报, 2021, 33(11): 2561-2571.
[13]	蔡敏, 王艳, 纪志成. 基于多策略融合量子粒子群算法的MOFFJSP研究[J]. 系统仿真学报, 2021, 33(11): 2615-2626.
[14]	曹振乾, 印江, 张津华. 基于改进粒子群算法的主蒸汽温度系统辨识[J]. 系统仿真学报, 2021, 33(10): 2411-2419.
[15]	肖运启, 王乙. 基于混合建模仿真的大型风电场降损控制[J]. 系统仿真学报, 2020, 32(9): 1831-1838.