Abstract:

To enhance the safety and on-time performance of flight rerouting under thunderstorm conditions, the airspace was first discretized into grids. Considering meteorological avoidance zones, aircraft performance, and fuel consumption, an ARRPTC model incorporating multiple safety factors was established based on the aircraft's origin, destination, and departure time. The model determined both the aircraft's airborne holding time at the departure point and its optimal rerouting path, with the objective of minimizing the total flight time. Subsequently, a multi-strategy improved DBO (MSDBO) was designed according to the problem characteristics to improve convergence speed and accuracy while avoiding premature convergence of the algorithm. The performance of the proposed MSDBO was then compared with four other swarm intelligence algorithms by means of six benchmark test functions to verify the effectiveness of the improvement strategies. Finally, a case study based on an actual aircraft rerouting scenario under thunderstorm conditions was conducted. The results indicate that, compared with traditional models, the proposed ARRPTC model reduces the detour time and distance by 52.3% and 53.0%, respectively, while the total flight time and distance decrease by 16.5% and 14.3%, and fuel consumption is reduced by 13.6%. Compared with the other four swarm intelligence algorithms, MSDBO demonstrates faster convergence, stronger global exploration, and superior local exploitation capabilities. In the ARRPTC model, MSDBO can obtain more optimal flight trajectories, significantly improving the efficiency of flight rerouting. Furthermore, as the thunderstorm safety threshold and turbulence coefficient threshold decrease, the total rerouting distance tends to increase.

Key words: aircraft rerouting route planning under thunderstorm conditions (ARRPTC), dung beetle optimizer (DBO), spiral search, swarm intelligence, mixed distribution perturbation