一种增强型改进麻雀搜索算法的三维航迹规划

针对无人机在三维复杂环境中多约束的最优化问题，提出了一种增强型改进麻雀搜索(enhanced modified sparrow search algorithm, EMSSA)用于航迹规划问题的求解。首先，利用Logistic-tent混沌序列初始化麻雀搜索算法，增强种群初始位置的随机性，提高算法全局搜索能力。其次在发现者-警戒者位置更新中加入了动态自适应调整策略，扩大算法搜索范围，提高算法的收敛速度。然后通过高斯-柯西变异策略，对麻雀个体进行位置更新，增强算法前期的全局搜索能力和后期局部发掘能力。最后选取11种测试函数和Wilcoxon秩和检验验证改进算法的有效性。仿真结果表明，增强型改进麻雀搜索算法在寻优精度、算法稳定性和收敛速度方面要优于其他对比搜索算法，并且可以在复杂的多约束环境中找到一条无碰撞的全局最优路径。在三维航迹规划中EMSSA算法相较于ISSA寻优精度提升了4.11%,相较于SSA提升了9.51%。

An Enhanced Modified Sparrow Search Algorithm for 3D Track Planning

Aiming at the multi-constraint optimization problem of UAV in a three-dimensional complex environment, an enhanced modified sparrow search algorithm is proposed to solve the path planning problem. Firstly, the sparrow search algorithm is initialized by Logistic-tent chaotic sequence to enhance the randomness of the initial position of the population and improve the global search ability of the algorithm. Then the dynamic adaptive adjustment strategy is added to the finder-vigilant position update to expand the search range and improve the convergence speed of the algorithm. Secondly, the sparrow position is updated by Gaussian-Cauchy mutation strategy to enhance the global search ability in the early stage and local search ability in the later stage. Finally, 11 test functions and Wilcoxon rank sum test are selected to verify the effectiveness of the improved algorithm. The simulation results show that the enhanced modified sparrow search algorithm is superior to other comparative search algorithms in optimization accuracy, algorithm stability and convergence speed, and can find a collision free global optimal path in a complex multi constraint environment.In three-dimensional track planning, the optimization accuracy of EMSSA algorithm is improved by 4.11% compared with ISSA algorithm and 9.51% compared with SSA algorithm.