细胞型脉冲神经P系统结合GWO的多无人机航迹规划OA

In response to the issue of multi-UAV trajectory estimation within acceptable accuracy and time constraints in complex three-dimensional environments,a new multi-UAV trajectory planning algorithm based on a Cell-like Spiking Neural P System combined with a hybrid Grey Wolf Optimization(CSP-GWO)algorithm is proposed.This work leverages the membrane system framework to simulate the parallel operation scenarios of multiple UAVs,while the proposed algorithm utilizes parallelism to model the social hierarchy and behavior of the grey wolf optimizer.A novel fuzzy variant of the hierarchical op-erator is introduced into the grey wolf optimizer to simulate the hunting process,enhancing the algorithm's exploratory capability and ensuring convergence speed.Testing on IEEE CEC benchmark functions vali-dates the optimization and robustness of the proposed method.Additionally,CSP-GWO is used to plan more feasible and stable trajectory paths for three different scales of UAVs,ensuring minimal trajectory costs and shortest simulation time,thereby demonstrating the effectiveness of CSP-GWO in the multi-UAV trajectory planning problem.