系统仿真学报 ›› 2023, Vol. 35 ›› Issue (4): 878-886.doi: 10.16182/j.issn1004731x.joss.22-FZ0893

• 论文 • 上一篇    

智能无人蜂群作战系统适应性进化模型仿真研究

李志强1(), 李元龙2, 殷来祥2, 马向平3   

  1. 1.国防大学 联合作战学院,北京 100091
    2.国防大学 研究生院,北京 100091
    3.唐山师范学院 计算机系,河北 唐山 063000
  • 收稿日期:2022-08-03 修回日期:2022-10-11 出版日期:2023-04-29 发布日期:2023-04-12
  • 作者简介:李志强(1973-),男,教授,博士生导师,博士,研究方向为兵棋推演与作战实验。E-mail:2683014096@qq.com

Research on Unmanned Swarm Combat System Adaptive Evolution Model Simulation

Zhiqiang Li1(), Yuanlong Li2, Laixiang Yin2, Xiangping Ma3   

  1. 1.Joint Operation Institute, National Defense University, Beijing 100091, China
    2.Graduate School, National Defense University, Beijing 100091, China
    3.Department of Computer, Tangshan Teachers College, Tangshan 063000, China
  • Received:2022-08-03 Revised:2022-10-11 Online:2023-04-29 Published:2023-04-12

摘要:

智能无人蜂群作战系统主要由有限行为能力的大规模作战个体组成,一般不具备应对复杂战场环境和作战对手变化的适应能力。采用遗传算法与增强学习相结合的方法探索构建基于个体的无人蜂群作战系统适应性进化模型,为了提高系统适应性进化速度,提出采用个体针对型变异优化策略改进遗传算法来提高蜂群系统的学习进化效率,在复杂系统建模仿真的SWARM平台上进行仿真实验研究,验证了本文方法的有效性。

关键词: 无人蜂群, 遗传算法, 适应性, 进化, 增强学习

Abstract:

Aiming at the fact that the intelligent unmanned swarm combat system is mainly composed of large-scale combat individuals with limited behavioral capabilities and has limited ability to adapt to the changes of battlefield environment and combat opponents, a learning evolution method combining genetic algorithm and reinforcement learning is proposed to construct an individual-based unmanned bee colony combat system evolution model. To improve the adaptive evolution efficiency of bee colony combat system, an improved genetic algorithm is proposed to improve the learning and evolution speed of bee colony individuals by using individual-specific mutation optimization strategy. Simulation experiment on SWARM platform of complex system modeling and simulation verify the effectiveness of the proposed theoretical method.

Key words: unmanned swarm, genetic algorithms, adaptability, evolution, reinforcement learning

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