Objective To address the insufficient dynamic adaptability of carrier-based aircraft scheduling and the inadequate characterization of task-profile coupling, a task-profile-driven hybrid optimization method is proposed to improve scheduling efficiency and decision-making effectiveness in complex dynamic environments..
Methods First, a particle swarm optimization–genetic algorithm (PSO-GA) hybrid optimization strategy is developed. The overall operational mission is decomposed according to its task profile, and a multi-stage dynamic scheduling model is established to uniformly describe the temporal relationships and resource constraints among different operational stages, including aircraft launch and recovery. The task profile is further incorporated into the scheduling process as a formal decision-making input, thereby strengthening the coupling between mission requirements and aircraft scheduling. Subsequently, an adaptive PSO-GA optimization strategy is designed by combining the rapid convergence capability of particle swarm optimization (PSO) with the global search capability of the genetic algorithm (GA), thereby improving the balance between search efficiency and global optimization capability.
Results Simulation experiments demonstrate the effectiveness of the proposed method. For a scheduling scenario involving 15 aircraft, the total scheduling time is 1 012.36 s, which is reduced by 0.56%, 1.72%, and 0.12% compared with conventional GA, conventional PSO, and the method without task-profile information, respectively. The average waiting time per aircraft is reduced to 162.84 s, indicating that the proposed method can effectively reduce unnecessary waiting during aircraft scheduling. Meanwhile, the significance index of catapult allocation balance reaches 0.99, demonstrating improved balance in the utilization of critical launch resources.
Conclusion The proposed method effectively addresses the multi-aircraft collaborative scheduling problem under complex temporal and resource constraints. Its main contribution is the integration of task-profile-driven modeling with hybrid evolutionary optimization, enabling mission-level requirements to be more effectively coupled with operational-level scheduling decisions. By combining the complementary advantages of PSO and GA, the proposed method improves scheduling efficiency, reduces aircraft waiting time, and enhances the balanced utilization of catapult resources. The proposed approach provides a useful reference for dynamic aircraft force allocation, intelligent scheduling, and coordinated decision-making in high-tempo carrier-based aircraft operations.
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