References
Liu W Y, Hwang I. Probabilistic trajectory prediction and conflict detection for air traffic control. J Guid Control Dyn, 2011, 34: 1779–1789
Lü R L, Guan X M, Li X Y, et al. A large-scale flight multi-objective assignment approach based on multiisland parallel evolution algorithm with cooperative coevolutionary. Sci China Inf Sci, 2016, 59: 072201
Tomlin C, Pappas G J, Sastry S. Conflict resolution for air traffic management: a study in multiagent hybrid systems. IEEE Trans Autom Control, 1998, 43: 509–521
Colorn A, Dorigo M, Maniezzo V. An investigation of some properties of an ant algorithm. In: Proceedings of Parallel Problan Solving from Nature Conference (PPSN 92), Brussels, 1992. 509–520
Guan X M, Zhang X J, Lü R L, et al. A largescale multi-objective flights conflict avoidance approach supporting 4D trajectory operation. Sci China Inf Sci, 2017, 60: 112202
Qian C, Shi J C, Tang K, et al. Constrained monotone k-submodular function maximization using multi-objective evolutionary algorithms with theoretical guarantee. IEEE Trans Evol Comput, 2017. doi: 10.1109/TEVC.2017.2749263
Qian C, Yu Y, Zhou Z H. Pareto ensemble pruning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15), Austin, 2015. 2935–2941
Du W B, Liang B Y, Yan G, et al. Identifying vital edges in Chinese air route network via memetic algorithm. Chinese J Aeronaut, 2017, 30: 330–336
Du W B, Zhou X L, Lordan O, et al. Analysis of the Chinese airline network as multi-layer networks. Transport Res Part E-Log Transport Rev, 2016, 89: 108–116
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. U1533119) and State Key Program of National Natural Science Foundation of China (Grant No. 71731001).
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Liu, H., Liu, F., Zhang, X. et al. Aircraft conflict resolution method based on hybrid ant colony optimization and artificial potential field. Sci. China Inf. Sci. 61, 129103 (2018). https://doi.org/10.1007/s11432-017-9310-5
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DOI: https://doi.org/10.1007/s11432-017-9310-5