Abstract: ObjectiveTo address the issues of insufficient dynamic adaptability and inadequate characterization of mission profile coupling in carrier-based aircraft scheduling, this study proposes a mission profile-driven hybrid optimization method. MethodsA Particle Swarm Optimization-Genetic Algorithm (PSO-GA) hybrid strategy is developed. First, a multi-stage dynamic scheduling model is constructed through mission profile decomposition to uniformly describe the temporal relationships and resource constraints of operations such as launch and recovery. Second, an adaptive hybrid optimization strategy, which integrates the fast convergence of PSO with the global search capability of GA, is introduced to solve the model under complex dynamic environments. ResultsSimulation experiments demonstrate that for a scale of 15 aircraft, the proposed method achieves a total scheduling duration of 950.81s, which is a reduction of 4.49%, 1.63%, and 1.04% compared to the traditional GA, PSO, and non-mission profile-based methods, respectively. The average waiting time per aircraft is significantly reduced from 160.46s (GA) to 66.95s. Furthermore, the significance index of catapult allocation balance improved to 0.98, ensuring stable resource utilization. ConclusionThe integration of mission profile decomposition and hybrid optimization effectively resolves multi-aircraft cooperative scheduling under complex constraints. This approach significantly enhances scheduling efficiency and resource utilization, providing a feasible optimization paradigm for dynamic aviation force allocation in high-tempo combat environments.