Abstract
Airspace congestion has become more and more serious in recent years due to the sharp increase of aircraft which has caused many unsafe factors and economic losses. Hence, how to assign flights to reduce congestion and delay has attracted much more attention. However, the flight assignment problem is very difficult to deal with it because in general has multiple objectives and involves in a large amount of flights. In this paper, we propose a new flight assignment method based on parallel evolution algorithm (PEA), which has great superiority for large-scale complicated problem. Besides, a left–right probability migration topology is presented to further improve the optimization capability. Experiments on real data of the national route of China show that our method outperforms the current three flight assignment approaches. Moreover, the congestion and delay are effectively alleviated.
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References
Zhang X, Guan X, Hwang I, Cai K (2012) A hybrid distributed-centralized conflict resolution approach for multi-aircraft based on cooperative co-evolutionary. Sci Chin Inf Sci 56:1–16
Su J, Zhang X, Guan X (2013) 4D-trajectory conflict resolution using cooperative coevolution. In: Proceedings of the 2012 international conference on information technology and software engineering. Springer, Berlin, pp 387–395
Odoni AR (1987) The flow management problem in air traffic control. Flow control of congested networks. Springer, Berlin, pp 269–288
Terrab M (1989) ATC flow control through ground-holding. MIT, Cambridge
Vranas PB, Bertsimas DJ, Odoni AR (1994) The multi-airport ground-holding problem in air traffic control. Oper Res 42(2):249–261
Delahaye D, Odoni AR (1997) Airspace congestion smoothing by stochastic optimization. Evolutionary Programming VI. Springer, Berlin, pp 163–176
Delahaye D, Sofiane O, Puechmorel S (2005) Airspace congestion smoothing by multi-objective genetic algorithm. In: ACM symposium on applied computing, pp 907–912
Tian W, Hu M (2010). Study of air traffic flow management optimization model and algorithm based on multi-objective programming. In: ICCMS 2010–2010 international conference on computer modeling and simulation. ICCMS, Sanya, China, pp 210–214
Liu H, Zhu Y, Cai K, Pan Q (2011) Route network flow assignment in the new generation of aviation by cooperative co-evolution, cybernetics and intelligent systems (CIS). In: 2011 IEEE 5th international conference on 17–19 Sept, pp 175–180
Oussedik S, Delahaye D (1998) Reduction of air traffic congestion by genetic algorithms. In: Proceedings of the fifth international conference on parallel problem solving from nature. Springer, Berlin, pp 855–864
Tang J, Lim MH, Ong YS (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput 11(9):873–888
Cantu-Paz E (2000) On the effects of migration on the fitness distribution of parallel evolutionary algorithms. In: Workshop on evolutionary computation and parallel processing at GECCO
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Zitzler E, Thiele L, Laumanns M et al (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2):117–132
Fleischer M (2003) The measure of Pareto optima applications to multiobjective metaheuristics. Evolutionary multi-criterion optimization. Springer, Berlin, pp 519–533
Zhang Q, Li H (2007) MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731
Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999
Acknowledgments
This work is supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110101), the National Natural Science Foundation of China (Grant No. 61201314), and the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20101102110005).
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Lei, J., Zhang, X., Guan, X. (2014). Study of Migration Topology in Parallel Evolution Algorithm for Flight Assignment. In: Wen, Z., Li, T. (eds) Practical Applications of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54927-4_34
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DOI: https://doi.org/10.1007/978-3-642-54927-4_34
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