改进粒子群算法的紫外光协作多无人机任务分配方法
Improved particle swarm algorithm for UV collaborative Multi-UAV task assignment
赵太飞 1刘阳 1杜浩辰1
作者信息
- 1. 西安理工大学自动化与信息工程学院,西安 710048;西安市无线光通信与网络研究重点实验室,西安 710048
- 折叠
摘要
为了解决无人机协同作战问题,需要将多任务分配给多个无人机.利用无线紫外光实现强电磁干扰环境下无人机机间隐秘信息传输,提出一种改进粒子群算法的多无人机任务分配方法,综合考虑无人机执行任务所付出的威胁代价、航程代价以及完成任务的时间差,结合压缩因子和差分进化思想解决粒子群优化算法容易陷入局部最优的问题.仿真结果表明,改进粒子群算法相较于传统粒子群算法在不同无人机和任务数量比下的任务分配平均成功率提高了约16％,算法在收敛时的迭代次数平均减少了约4.5倍,最优适应度值平均减小了近一倍,在多无人机任务分配的实际应用中有一定的意义.
Abstract
In order to solve the UAV cooperative operation problem,multi-tasks need to be assigned to multiple UAVs.Wireless ultraviolet light is used to realize the covert information transmission between UAVs under the strong electromagnetic interference environment,and an improved particle swarm algorithm for multi-UAV task allocation is proposed,which takes into account the threat cost,voyage cost,and the time difference of completing the task for UA-Vs to perform the task,and combines the compression factor and the differential evolution idea to solve the problem that particle swarm optimization algorithm is easy to fall into the local optimum.Simulation results show that the im-proved particle swarm algorithm improves the average success rate of task allocation by about 16％compared with the traditional particle swarm algorithm under different ratios of UAVs and number of tasks,reduces the number of itera-tions of the algorithm by about 4.5 times on average at the time of convergence,and the optimal fitness value decreases by nearly double on average,which is of some significance in the practical application of multi-UAV task allocation.
关键词
紫外光通信/任务分配/粒子群算法/差分进化Key words
uv optical communication/task assignment/particle swarm algorithm/differential evolution引用本文复制引用
出版年
2024
NETL
NSTL
万方数据