融合多策略的增强麻雀搜索算法及其应用.

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. [ABSTRACT FROM AUTHOR]