基于改进蛙跳算法的多无人机协同任务分配研究

Research on Multi-UAV Collaborative Task Allocation Based on ISFLA

针对多无人机协同任务分配问题,提出了一种基于Levy飞行的改进随机蛙跳算法用于解决多无人机的协同任务预分配问题,通过引入动态跳跃步长、Levy飞行因子和族群认知因子有效改进了算法的搜索性能,提高了搜索效率。针对多无人机协同执行任务时可能遭遇的突发任务,通过引入市场拍卖机制提高了算法的计算收敛效率。通过仿真算例分析,验证了改进的随机蛙跳算法解决多无人机协同任务分配问题的有效性。

To solve the problem of collaborative task allocation for multi-UAVs,an improved shuffle frog leaping algorithm(ISFLA)based on Levy flight is proposed to solve the problem of collaborative task pre-allocation for multi-UAVs.Firstly,the dynamic leap steps,Levy flight factors and population cognition factors are introduced to improve the search performance of the algorithm effectively.The search efficiency is improved.Secondly,according to the pop-up task encountered during the execution of the collaborative task of multi-UAV,the calculation convergence rate of the algorithm is improved by introducing the market auction mechanism.Finally,a simulation example is given to verify the effectiveness of the improved shuffle frog leaping algorithm to solve the problem of multi-UAV collaborative task allocation.