带精英集并行遗传算法的无人机干扰资源调度

邓敏; 伍志高; 姚志强; 陈永其

doi:10.11999/JEIT210349

带精英集并行遗传算法的无人机干扰资源调度

doi: 10.11999/JEIT210349

邓敏^{1, 2, ,},
伍志高²,
姚志强²,
陈永其¹

1.
中国电子科技集团第36研究所通信信息控制和安全技术重点实验室嘉兴 314000
2.
湘潭大学自动化与电子信息学院湘潭 411105

基金项目: 湖南省自然科学基金(2019JJ50620)，国家重点研发计划(2020YFA0713502)，GF科技重点实验室基金(61421060404)

详细信息

作者简介:
邓敏：女，1987年生，讲师，研究方向为通信干扰对抗、认知无线电

伍志高：男，1997年生，硕士生，研究方向为干扰资源调度

姚志强：男，1975年生，教授，研究方向为导航定位、下一代宽带无线通信

陈永其：男，1969年生，研究员级高级工程师，研究方向为数字信号处理、特种通信

通讯作者:
邓敏　iemdeng@xtu.edu.cn

中图分类号: TN974; TN972
计量
- 文章访问数: 496
- HTML全文浏览量: 168
- PDF下载量: 61
- 被引次数: 0
出版历程
- 收稿日期: 2021-04-23
- 修回日期: 2021-10-12
- 网络出版日期: 2022-04-17
- 刊出日期: 2022-06-21

Unmanned Aerial Vehicle Jamming Resource Scheduling Based on Parallel Genetic Algorithm with Elite Set

1.
Science and Technology on Communication Information Security Control Laboratory, No. 36 Research Institute of China Electronics Technology Group Corporation, Jiaxing 314000, China
2.
School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China

Funds: The Provincial Natural Science Foundation of Hunan (2019JJ50620), The National Key R&D Program of China (2020YFA0713502), The National Science Key Laboratory Foundation (61421060404)

摘要

摘要: 在中大规模无人机干扰资源调度中，针对现有模型约束条件简单、调度算法适用规模较小的问题，该文提出了带最少任务数约束的资源调度模型，以最大化干扰效益和最小化成本为目标，用层次分析法对效益与成本指标赋权，并设计了一种用精英集加快收敛的改进并行遗传算法。在中等规模和500:500(干扰资源数:目标数)的更大规模仿真实验中，所提算法与遗传算法、非支配排序遗传算法II、修复遗传算法、基于岛屿模型的并行遗传算法和自适应模拟退火遗传禁忌搜索算法的性能相比，能在更短的时长内达到较优的目标函数值。
- 干扰对抗 /
- 资源调度 /
- 遗传算法 /
- 并行算法 /
- 混合模型
Abstract: In order to solve the optimization problem of jamming resource scheduling in medium and large-scale Unmanned Aerial Vehicle (UAV) jamming scenarios, a jamming resource scheduling model that can meet the minimum number of tasks constraint is proposed to improve the simple constraints and small-scale solution algorithms of the existing models. The interference benefit and cost indicators are weighted by the analytic hierarchy process. Then an improved parallel genetic algorithm is designed, where the elite set is introduced to accelerate the convergence of the algorithm. The simulation results in medium scale and larger scale jamming situations, such as 500:500 (number of jamming resources: number of targets) show that the proposed algorithm converges faster and achieves better objective function value than the existing representative and improved genetic algorithms.
- Interference countermeasure /
- Resource scheduling /
- Genetic algorithm /
- Parallel algorithm /
- Hybrid mode

HTML全文

图 1 干扰关系

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图 2 混合主-从和岛屿模型结构

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图 3 中、大规模资源调度优化目标函数值迭代趋势对比

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图 4 算法收敛性能对比(100次仿真平均)

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图 5 干扰资源与目标的空间位置

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图 6 中规模资源调度优化的目标函数值迭代趋势对比

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表 1 基于混合模型的并行遗传算法(算法1)

　1：　t=1// 迭代次数

　2：计算总干扰效益矩阵E，规模为M×N

　3：初始化S个子种群(pop₁,pop₂,···,pop_s)，种群规模为P，设置
　　每个子种群的最差个体g_1k，全局最优个体b₀;//$S \ge 3$，根据
　　算法计算最短时间和并行线程而定；

　4：while(t<t_max)

　5：parfors=1:S;//S个子种群并行计算

　6：(pop_k,g_k,g_1k)=cacaulation(pop_k);//子种群进行选择，交叉，
　　变异等遗传操作，并计算操作后的目标函数值，选出最优个
　　体g_k,和最差个体g_1k，k=1,2,···,S

　7：end parfor

　8：G=(g₁,g₂,···,g_k);//k=1,2,···,S

　9：b₁=max(G)

　10：if b₁>b₀

　11：b₀=b₁;

　12：end if

　13：for k=1:S

　14：pop_k=change(g_1k,b₀,pop_k)//所有子种群将最差个体g_1k替换
　　为b₀

　15：end for

　16：end while

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表 2 迭代1500次所用时间/最终目标函数值对比(100次仿真平均值)

算法	对抗规模
	100:50		200:100		400:200		500:250		500:500
	耗时(s)	函数值	耗时(s)	函数值	耗时(s)	函数值	耗时(s)	函数值	耗时(s)	函数值
本文算法	3.61	19.87	12.16	42.01	44.66	81.13	69.74	97.27	69.94	120.35
GA	9.84	18.56	37.95	37.85	101.03	76.82	217.29	92.06	212.15	121.03
文献[18]	1.83	14.63	2.42	30.13	3.65	55.34	4.11	67.65	4.20	68.36
文献[21]	5.42	15.72	13.82	36.79	54.42	74.33	78.12	90.80	80.67	98.19
文献[20]	287.60	14.38	1126.33	28.82	3912.38	56.50	6054.23	70.09	6343.78	75.40
文献[22]	13.57	15.71	53.01	28.67	219.72	54.54	300.68	67.34	310.36	68.93

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表 3 在M=12, N=6规模下各算法在100次计算中取得最优解次数的百分比

算法	本文算法	GA	文献[18]	文献[21]	文献[20]	文献[22]
百分比(%)	96	41	24	80	0	4

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