A Knee-Guided Evolutionary Algorithm for Multi-Objective Air Traffic Flow Management

Air traffic flow management (ATFM) plays a crucial role in efficient aviation. Most existing studies assume the flight speed as constant throughout the trip, leading to ineffective fixed-speed schedules. To address this issue, we propose a new problem model, which allows variable speed control to improve the flexibility and maneuverability of the management. In addition, we consider two conflicting objectives, which are minimizing the total flight delays and conflicts between flights, where the conflicts depend on the flight 4-D trajectories (3-D position plus time). To solve this new challenging problem, we propose a novel multiobjective evolutionary algorithm with new problem-specific individual representation and search operators. Specifically, the multichromosomes encoding scheme is designed to adapt to different types of operations. Then, to search the huge search space effectively, we develop a hybrid crossover operator that recombines the parents based on their flight routes. Furthermore, to balance the exploration and exploitation, we develop a new mutation strategy to utilize the heterogeneous search potential of different individuals. For exploitation, the knee individual in the Pareto front is improved by a new time shift operator for exploitation, and other nondominated solutions are mutated by fixed-route mutation. For exploration, the dominated solutions are mutated randomly. To verify the effectiveness, we compare it with the real ATFM schedules and the state-of-the-art algorithms on a range of real-world air traffic datasets. Extensive results show that the proposed algorithm can significantly outperform the baselines in generating safe and efficient 4-D trajectories.