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A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation

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Abstract

Recently, the long-term conflict avoidance approaches based on large-scale flights scheduling have attracted much attention due to their ability to provide solutions from a global point of view. However, current approaches which focus only on a single objective with the aim of minimizing the total delay and the number of conflicts, cannot provide controllers with variety of optional solutions, representing different tradeoffs. Furthermore, the flight track error is often overlooked in the current research. Therefore, in order to make the model more realistic, in this paper, we formulate the long-term conflict avoidance problem as a multi-objective optimization problem, which minimizes the total delay and reduces the number of conflicts simultaneously. As a complex air route network needs to accommodate thousands of flights, the problem is a large-scale combinatorial optimization problem with tightly coupled variables, which make the problem difficult to deal with. Hence, in order to further improve the search capability of the solution algorithm, a cooperative co-evolution (CC) algorithm is also introduced to divide the complex problem into several low dimensional sub-problems which are easier to solve. Moreover, a dynamic grouping strategy based on the conflict detection is proposed to improve the optimization efficiency and to avoid premature convergence. The well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D) is then employed to tackle each sub-problem. Computational results using real traffic data from the Chinese air route network demonstrate that the proposed approach obtained better non-dominated solutions in a more effective manner than the existing approaches, including the multi-objective genetic algorithm (MOGA), NSGAII, and MOEA/D. The results also show that our approach provided satisfactory solutions for controllers from a practical point of view.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. U1533119, U1433203), and Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 61221061).

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Correspondence to Xuejun Zhang.

Additional information

Conflict of interest The authors declare that they have no conflict of interest.

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Guan, X., Zhang, X., Lv, R. et al. A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation. Sci. China Inf. Sci. 60, 112202 (2017). https://doi.org/10.1007/s11432-016-9024-y

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Keywords

  • air traffic management
  • conflict avoidance
  • combinatorial optimization
  • multi-objective
  • cooperative co-evolution