Improved nondominated sorting genetic algorithm II for multi-objective optimization of scheduling arrival aircrafts

被引：0
|
作者：
Feng, Xiang [1 ,2 ]
Yang, Hong-Yu [1 ]
机构：
[1] State Key Lab of Air Traffic Control Automation Technology and Systems, Sichuan University
[2] Sichuan Jiuzhou Electric Group Co. Ltd.
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2014年 / 43卷 / 01期
关键词：
Heuristic crossover operator; Multi-objective optimization; NSGA-II; Pareto optimal; Scheduling arrival aircrafts;
D O I：
10.3969/j.issn.1001-0548.2014.01.011
中图分类号：
学科分类号：
摘要：
Based on the Pareto optimal conception, an Improved nondominated sorting genetic algorithm II (NSGA-II) seeking non-inferior solution set of multi-objective optimization (MO) problems is proposed, while the heuristic crossover operator based on nearest-neighborhood, the improved mutation operator and the filtering of non-inferior solutions are focused and discussed. The algorithm proposed is applied to a two-objective optimization of scheduling of arrival aircrafts at an airport with multiple runways, where both the sum of all the delays squared and the fuel cost of all the aircrafts were required to be minimized. After the simulation experiment, the optimal solutions are analyzed and compared with the best solutions founded by some existing algorithms. The research result demonstrates that improved NSGA-II possesses a good application foreground for multi-objective optimization of scheduling arrival aircrafts at an airport with multiple runways.
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页码：66 / 70
页数：4
相关论文
共 14 条
  • [1] Yu J., Yang Y.-B., Zhou H., Innovative predatory search algorithm for aircraft arrival sequencing and scheduling problem, Transaction of Nanjing University of Aeronautics and Astronautics, 27, 4, pp. 361-366, (2010)
  • [2] Yang K., Zhang J.-W., Research of arriving aircrafts scheduling model based on push-back and swap, Journal of Sichuan University (Engineering Science Edition), 43, 3, pp. 1-6, (2011)
  • [3] Cheng V.H.L., Crawford L.S., Menon P.K., Air traffic control using genetic search techniques, Proceedings of the 1999 IEEE International Conference on Control Applicatios, pp. 249-254, (1999)
  • [4] Hansen J.V., Genetic search methods in air traffic control, Computers & Operations Research, 31, 3, pp. 445-459, (2004)
  • [5] Capr S., Ignaccolo M., Genetic algorithm for solving the aircraft-sequencing problem: the introduction of departures into the dynamic model, Journal of Air Transport Management, 10, 5, pp. 345-351, (2004)
  • [6] Yu J., Luo X.-L., Application of genetic algorithm on scheduling arrival aircrafts, Aeronautical Computing Technique, 37, 3, pp. 1-4, (2007)
  • [7] Yang Q.-H., You Z.-H., Feng Z.-L., Et al., Scheduling arrival aircrafts on multiple ruways based on an improved genetic algorithm, Journal of Sichuan University (Engineering Science Edition), 38, 2, pp. 141-145, (2006)
  • [8] Meng X.-W., Zhang P., Li C.-J., Memetic algorithm for aircraft arrival sequencing and scheduling problem, Journal of Southwest Jiaotong University, 46, 3, pp. 488-494, (2011)
  • [9] Ying S.-G., Sun F.-C., Hu L.-H., Et al., Multi-objective dynamic programming algorithm for aircraft arrival sequence and runway scheduling, Control Theory & Application, 27, 7, pp. 826-835, (2010)
  • [10] Zhang H.-H., Hu M.-H., Multi-objection optimization for collaborative scheduling aircraft landing on multi-runways, Journal of Southwest Jiao-tong University, 44, 3, pp. 402-409, (2009)
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