基于改进樽海鞘群算法的无人机高程模型航迹规划OA

To tackle path fluctuations and suboptimal optimization in 3D UAV path planning over complex terrain and multiple threats,we propose a convex hull strategy with elevation data and an improved salp swarm algorithm(ISSA).First,construct elevation models for a moun-tainous area in Hangzhou and an urban area in New York based on ASTER GDEMV3 and Open Street Map data.Next,we encode the terrain elevation information using the convex hull strategy and construct paths using B-spline curves.Finally,we enhance the SSA by incorporating adaptive Alpha-stable distribution strategy and nonlinear disturbance strategies in the individual position update formula to balance global ex-ploration and local exploitation.Additionally,we introduce greedy and fish aggregation device(FAD)strategies to improve the efficiency and accuracy searches.The performance of the improved algorithm was validated by conducting experimental comparisons using the CEC2020 test functions.The results demonstrate that the convex hull strategy significantly enhances the planning capability of the algorithm.Compared to tra-ditional algorithms,the improved SSA achieves higher optimization accuracy and lower cost functions for UAV path planning.