改进的蚁群干扰资源分配方法

doi:10.16182/j.issn1004731x.joss.20-FZ0812E

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (12): 2967-2974.doi: 10.16182/j.issn1004731x.joss.20-FZ0812E

• 仿真模型/系统置信度评估技术 • 上一篇下一篇

改进的蚁群干扰资源分配方法

王青云¹, 焦德忠^1,*, 史铄¹, 彭根燕¹, 孙俊华², 段雨昕³

1.北京中船信息科技有限公司,北京 100094;
2.北京九天利建信息技术有限公司,北京 100094;
3.北京仿真中心,北京 100854

收稿日期:2021-06-11 修回日期:2021-08-11 出版日期:2021-12-18 发布日期:2022-01-13

Improved Ant Colony Optimization Algorithm for Jamming Resource Allocation

Wang Qingyun¹, Jiao Dezhong^1,*, Shi Shuo¹, Peng Genyan¹, Sun Junhua², Duan Yuxin³

1. Beijing Information Technology Co, Ltd, Beijing 100094, China;
2. Beijing Novsky Information Technology Co, Ltd, Beijing 100094, China;
3. Beijing Simulation Center, Beijing 100854, China

Received:2021-06-11 Revised:2021-08-11 Online:2021-12-18 Published:2022-01-13
Contact: Jiao Dezhong (1982-), male, master, assistant engineer, research area: simulation training. E-mail: 252275683@qq.com
About author:Wang Qingyun (1988-), male, master, assistant engineer, research area: simulation training. E-mail: qywang16@163.com
Supported by:
National Natural Science Foundation (61402365, 61271300); Shaanxi Education Natural Science Foundation (2013JK1076); National Visiting Scholarship Program (201406965022); Shaanxi Industry Surmount Foundation (2013K-33, 2014KW01-04)

摘要/Abstract

摘要： 蚁群算法作为新型智能优化算法,应用于干扰资源分配时,寻优过程的收敛速度较慢,且获得全局最优值的概率较低。为了改善基于蚁群算法的干扰资源分配效率,同时提升得到全局最优解的概率,提出了衰减因子在寻优过程中按照指数型函数进行变化,即初始寻优阶段衰减因子取相对较小的数值,随着迭代次数的增加,衰减因子取值单调递增且呈现指数规律变化。仿真分析验证了所提方法的正确性,该方法不仅可以改善干扰资源分配过程中的收敛效率,同时具有较高的全局最优获取概率。

关键词: 干扰资源分配, 蚁群算法, 衰减因子, 收敛性, 稳定性

Abstract: Ant Colony Optimization (ACO) is a new intelligence optimization algorithm. When applied to jamming resource allocation, the velocity of convergence in optimization process is slow and the probability of obtaining the global optimal solution is low. In order to raise the efficiency of jamming resource allocation and the probability of getting global optimal solution, the attenuation factor is improved to a variable that changes according to the exponential function in optimization process. The attenuation factor is taken as a relatively small value in the initial search phase, and increases monotonically and exponentially as the number of iterations increases. Simulation results illustrate the effectiveness of the proposed method, the high efficiency of jamming resource allocation, and higher global optimal solution acquisition probability.

Key words: jamming resource allocation, ant colony optimization, attenuation factor, convergence, stability

中图分类号:

TP391.9

王青云, 焦德忠, 史铄, 彭根燕, 孙俊华, 段雨昕. 改进的蚁群干扰资源分配方法[J]. 系统仿真学报, 2021, 33(12): 2967-2974.

Wang Qingyun, Jiao Dezhong, Shi Shuo, Peng Genyan, Sun Junhua, Duan Yuxin. Improved Ant Colony Optimization Algorithm for Jamming Resource Allocation[J]. Journal of System Simulation, 2021, 33(12): 2967-2974.

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改进的蚁群干扰资源分配方法

Improved Ant Colony Optimization Algorithm for Jamming Resource Allocation

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