基于改进麻雀搜索算法的无人机航路规划研究

Research on UAV Route Planning Based on Improved Sparrow Search Algorithm

针对三维复杂地形环境中无人机航路规划时存在的求解效率低、易陷入局部最优值等问题,提出了一种基于融合多策略改进的麻雀搜索算法(CFSSA)的实时航迹规划方法。为解决麻雀搜索算法收敛速度慢、寻优精度低等问题,首先,采用Circle混沌映射初始化种群,使种群分布更加均匀,提高了麻雀种群位置的多样性;其次,对麻雀的位置信息进行优化,采用萤火虫扰动策略使得算法的灵活性和搜索范围增加,抑制群体过早的收敛,提高了算法的收敛速度和精度。最后,通过6个标准测试函数验证了CFSSA算法的有效性,能够突破局部最优解的限制,获得更高的精度。仿真结果显示CFSSA算法较其他算法代价最优且方差较小。

Aiming at the problems of low solution efficiency and easy to fall into local optimal values in the course planning of UAVs in three-dimensional complex environment, a real-time track planning method based on the improved sparrow search algorithm(CFSSA) of fusion multi-strategy is proposed. Due to the problems of slow convergence speed and low optimization accuracy of the sparrow search algorithm, firstly, the Circle chaotic mapping is used to initialize the population, the population distribution is more uniform, and the diversity of sparrow population positions is improved. Secondly, the position information of the sparrow is optimized, and the firefly disturbance strategy is used to increase the flexibility and search range of the algorithm, inhibit the premature convergence of the group, and improve the convergence speed and accuracy of the algorithm. Finally, the effectiveness of the CFSSA algorithm is verified by six standard test functions, which can break through the limitation of local optimal solution and obtain higher accuracy. Applying it to solve route planning problems, simulation results show that the CFSSA algorithm is more expensive and has a smaller variance than other algorithms.