基于A-HPSO算法的无人机集群协同目标分配  

Collaborative Target Allocation of UAV Cluster Based on A-HPSO Algorithm

作　　者：沈越 范国梁[1] 李丽娟 SHEN Yue;FAN Guo-liang;LI Li-juan(Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院自动化研究所,北京100190

出　　处：《计算机仿真》2024年第9期18-25,共8页Computer Simulation

摘　　要：首先对透明态势下无人机集群近视距空战协同任务目标分配这一问题进行了数学建模。针对在建模后问题求解过程中时常产生的“算法易陷入局部极值而无法继续搜索”这一“算法早熟”问题,在混合粒子群算法(HPSO)基础上,提出了一种引入堵塞检测机制和强制打散操作的自打散混合粒子群算法(A-HPSO)。为验证研究思路及改进后算法性能,分别将HPSO算法及其两种改进型算法M-HPSO算法和I-HPSO算法与A-HPSO算法在六种标准测试目标优化函数及固定-随机两种空战态势下所建模问题上进行对比仿真。实验结果显示,在多约束多目标函数的优化求解上,改进后的A-HPSO算法显著避免了“算法早熟”问题的发生,并进一步提升了HPSO算法的收敛速度和收敛精度。Firstly,a mathematical model was established for the problem of target allocation in close range aerial combat collaborative tasks of unmanned aerial vehicle swarms under transparent conditions.Aiming at the problem of "algorithm premature convergence" that often occurs during the problem solving process after modeling,a self destructing hybrid particle swarm algorithm(A-HPSO) is proposed based on the hybrid particle swarm algorithm(HPSO),which introduces a blockage detection mechanism and a forced fragmentation operation.In order to verify the research idea of this paper and the performance of the improved algorithm,HPSO,M-HPSO,I-HPSO and A-HPSO algorithms are respectively applied to the simulation experiments of six standard test target optimization functions and the simulation experiments of modeling problems under two kinds of fixed-random air combat situations.The experimental results show that compared with the original algorithm,the improved algorithm A-HPSO significantly overcomes the "premature algorithm" problem and further improves the convergence speed and accuracy of the HPSO algorithm in the optimization of multi-constraint and multi-objective functions.

关 键 词：粒子群算法 混合粒子群算法 自打散混合粒子群算法 无人机集群协同目标分配 

分 类 号：V271.4[航空宇航科学与技术—飞行器设计]