In this paper, a lens learning sparrow search algorithm (LLSSA) is proposed to improve the defects of the new sparrow search algorithm, which is random and easy to fall into local optimum. The algorithm has achieved good results in function optimization and has planned a safer and less costly path to the three-dimensional UAV path planning. In the discoverer stage, the algorithm introduces the reverse learning strategy based on the lens principle to improve the search range of sparrow individuals and then proposes a variable spiral search strategy to make the follower's search more detailed and flexible. Finally, it combines the simulated annealing algorithm to judge and obtain the optimal solution. Through 15 standard test functions, it is verified that the improved algorithm has strong search ability and mining ability. At the same time, the improved algorithm is applied to the path planning of 3D complex terrain, and a clear, simple, and safe route is found, which verifies the effectiveness and practicability of the improved algorithm.

中文翻译：

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镜头学习麻雀搜索算法

本文提出了一种镜头学习麻雀搜索算法（LLSSA），以改善新的麻雀搜索算法的缺陷，该算法是随机的，容易陷入局部最优。该算法在功能优化上取得了良好的效果，并规划了一条更安全，成本更低的三维无人机路径规划路径。在发现者阶段，该算法引入了基于镜头原理的逆向学习策略，以提高麻雀个体的搜索范围，然后提出了一种可变螺旋搜索策略，以使追随者的搜索更加详细和灵活。最后，结合模拟退火算法进行判断并获得最优解。通过15种标准测试功能，验证了改进算法具有较强的搜索能力和挖掘能力。同时，