融合多策略的增强麻雀搜索算法及其应用

针对麻雀搜索算法（SSA）易陷入局部最优和寻优精度低等问题，提出一种融合多策略的增强麻雀搜索算法（ESSA）。首先，在发现者飞行位置引入莱维飞行和云自适应权重，以扩大算法搜索范围并丰富其种群多样性；其次，通过基于模糊控制的自适应透镜成像策略对当前最优位置进行反向学习，以增强算法跳出局部最优的能力；最后选用CEC2017中的12个函数作为测试集，将ESSA和标准SSA，以及其他四种改进麻雀算法（ISSA、MSSSA、HSSA、SHSSA）进行性能测试。实验结果表明ESSA能够获得更好的搜索性能。将ESSA算法应用于三维无人机路径规划问题，仿真结果表明ESSA在无人机三维路径寻优上也能获取最优的结果。

Enhanced sparrow search algorithm with multiple strategies and its application

Aiming at the problems that SSA is prone to fall into local optimal and relatively low accuracy during search iteration. This paper proposed an enhanced sparrow search algorithm with multiple strategies(ESSA). At first, ESSA applied Lévy flight and cloud based adaptive weights to refine the original discoverers'' position update equation, which expanded the search range and enriched population diversity of the algorithm. Secondly, ESSA adopted a fuzzy control based adaptive lens imaging strategy to get the reversed position of the current optimal position to enhance the algorithm''s ability to jump out of local optimal. This paper selected 12 test functions from CEC2017 testbed as benchmark to evaluate the performance of ESSA with standard SSA, and other 4 improved sparrow algorithms: ISSA, MSSSA, HSSA, and SHSSA. Experiment result shows that ESSA can achieve the supreme results among evaluated algorithms. This paper also applied ESSA to the 3D UAV path planning problem. The simulation result illustrates that ESSA can also find the supreme 3D paths for UAV.