Zehao Hou

Low-altitude wind fields significantly increase the complexity of multi-UAV cooperative path planning (MUAVCP) by affecting flight time, energy consumption, trajectory feasibility, and arrival coordination. To address this problem, this study establishes a three-dimensional MUAVCP model considering kinematic constraints, no-fly zones, temporal coordination, inter-UAV separation, and multiple mission-related costs. An improved hybrid Rime–Snow Ablation Optimizer (IHRSO) is proposed. IHRSO employs the previously developed IRIME as the primary search component and a normal-cloud-model-enhanced SAOHTC (NCM-SAOHTC) as the auxiliary component. The two components are coordinated through an adapted stagnation-triggered mechanism that activates auxiliary search after insufficient primary-search improvement and subsequently returns the shared elite to IRIME for further refinement. IHRSO is evaluated on the CEC2017 and CEC2022 benchmark suites and several multi-UAV path-planning scenarios under low-altitude wind conditions. Ablation experiments show that IHRSO achieves the best Friedman mean rank of 1.375 and reduces optimization error by an average of 37.30% compared with fixed-period switching. In multi-UAV simulations, IHRSO consistently generates high-quality coordinated trajectories with lower mission delays and planning costs. In the large-fleet experiments, IHRSO maintains favorable solution quality and mission-delay performance as the fleet size increases to 20 UAVs, although the computational cost rises substantially with problem dimensionality.