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基于军民融合的全局飞行流量协同优化方法

吴文浩 张学军 顾博 朱晓辉

吴文浩, 张学军, 顾博, 等 . 基于军民融合的全局飞行流量协同优化方法[J]. 北京航空航天大学学报, 2018, 44(9): 1926-1932. doi: 10.13700/j.bh.1001-5965.2018.0006
引用本文: 吴文浩, 张学军, 顾博, 等 . 基于军民融合的全局飞行流量协同优化方法[J]. 北京航空航天大学学报, 2018, 44(9): 1926-1932. doi: 10.13700/j.bh.1001-5965.2018.0006
WU Wenhao, ZHANG Xuejun, GU Bo, et al. A global network flight flow assignment algorithm based on civil-military integration[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1926-1932. doi: 10.13700/j.bh.1001-5965.2018.0006(in Chinese)
Citation: WU Wenhao, ZHANG Xuejun, GU Bo, et al. A global network flight flow assignment algorithm based on civil-military integration[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1926-1932. doi: 10.13700/j.bh.1001-5965.2018.0006(in Chinese)

基于军民融合的全局飞行流量协同优化方法

doi: 10.13700/j.bh.1001-5965.2018.0006
基金项目: 

国家科技支撑计划 2015BAG15B01

详细信息
    作者简介:

    吴文浩  男, 博士研究生, 工程师。主要研究方向:空中交通管理、飞行流量协同优化调控、空管数据信息分析处理等

    张学军  男, 博士, 教授, 博士生导师。主要研究方向:空中交通管理、数据通信与航空监视等

    通讯作者:

    张学军.E-mail:zhxj@buaa.edu.cn

  • 中图分类号: V355

A global network flight flow assignment algorithm based on civil-military integration

Funds: 

National Key Technology Research and Development Program of China 2015BAG15B01

More Information
  • 摘要:

    随着飞行活动需求的持续快速增长和空域资源使用矛盾的日益凸显,全局飞行流量协同优化已成为减少飞行延误、降低飞行危险、确保空域运行安全的一个重要手段。空中交通管理作为军民融合发展的重点领域,迫切需要对军民航飞行流量实施统一、高效、兼顾各自特点的协同优化。在实际研究中,全局飞行流量协同优化问题具有大规模、多目标、难分解等特点,是一类复杂的工程优化问题。本文贯彻军民融合发展思想,设计了一种基于军民航异质化飞行活动管制要求、考虑差异化调配方法与代价、兼顾军民航管制员各自工作特点、有效解决扇区网络运行安全性和经济性问题的全局飞行流量多目标协同优化模型——CMI模型;为解决种群在进化过程中“不平衡不充分”的问题,提出了一种动态自适应多目标遗传算法(DA-MOGA),并针对性设计了基于聚集距离和种群多样性的交叉变异概率动态调整机制。利用中国扇区网络实际数据,对本文提出的模型和算法进行了验证,算法结果优于2种经典的多目标进化算法。

     

  • 图 1  3种算法Pareto前沿对比

    Figure 1.  Comparison of pareto Front among three algorithms

    图 2  DA-MOGA交叉、变异概率进化趋势

    Figure 2.  Evolutionary trend of crossover and variation probability for DA-MOGA

    图 3  不同αcmi下的Pareto前沿对比

    Figure 3.  Comparison of Pareto front at different αcmi

    表  1  算法主要参数取值

    Table  1.   Main parameter setting of algorithms

    算法 种群个数 进化代数 交叉概率 变异概率
    MOGA 100 100 0.5 0.09
    NSGA-Ⅱ 100 100 0.7 0.07
    DA-MOGA 100 100
    下载: 导出CSV

    表  2  主要性能评价指标对比

    Table  2.   Comparison of main performance evaluation indexes

    算法 Ih(方差) Id(方差) Δ(方差)
    MOGA 0.559 9
    (0.274 8)
    0.395 8
    (0.362 6)
    0.999 5
    (0.001 1)
    NSGA-Ⅱ 0.546 4
    (0.207 0)
    0.294 0
    (0.215 3)
    0.998 5
    (0.001 4)
    DA-MOGA 0.605 9
    (0.228 3)
    0.241 2
    (0.307 1)
    0.999 4
    (0.000 8)
    下载: 导出CSV

    表  3  3种算法目标函数值对比

    Table  3.   Comparison of objective function value among three algorithms

    优先考虑 目标函数值 MOGA NSGA-Ⅱ DA-MOGA DA-MOGA比MOGA减少比例/% DA-MOGA比NSGA-Ⅱ减少比例/%
    效率 空中交通拥堵 2 318.44 2 672.48 2 276.39 1.81 14.82
    总体飞行延误代价 13 457.6 15 728 11 736 12.79 25.38
    安全 空中交通拥堵 2 294.87 2 543.53 2 266.92 1.22 10.88
    总体飞行延误代价 14 480.3 17 108 11 906 17.78 30.41
    下载: 导出CSV

    表  4  DA-MOGA时间复杂度s

    Table  4.   Time complexity of DA-MOGA

    伪代码步骤 时间复杂度
    选择 O(N|F|)
    遗传操作 O(N|F|)
    约束处理 O(N|F|)
    动态自适应算子计算 O(N)
    目标函数计算 O(MN|T|)
    重组和保留 O(N2)
    下载: 导出CSV
  • [1] DE MATOS P A L, POWELL P L.Decision support for flight rerouting in Europe[J].Decision Support Systems, 2002, 34(4):397-412. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ029893188
    [2] BERTSIMAS D, LULLI G, ODONI A.The air traffic flow management problem: An integer optimization approach[C]//Proceedings of the 13th International Conference on Integer Programming and Combinatorial Optimization.Berlin: Springer, 2008: 36-46.
    [3] SHERALI H, STAATS R, TRANI A.An airspace planning and collaborative decision-making model:Part Ⅰ-Probabilistic conflicts, workload, and equity considerations[J].Transportation Science, 2003, 37(4):434-456. doi: 10.1287/trsc.37.4.434.23272
    [4] SHERALI H, STAATS R, TRANI A.An airspace-planning and collaborative decision-making model:Part Ⅱ-Cost model, data considerations, and computations[J].Transportation Science, 2006, 40(2):147-164. doi: 10.1287/trsc.1050.0141
    [5] DELL'OLMO P, LULLI G.A new hierarchical architecture for air traffic management:Optimization of airway capacity in a free flight scenario[J].European Journal of Operational Research, 2002, 144(1):179-193. http://cn.bing.com/academic/profile?id=d90a5a264823d257831d1144aae275ec&encoded=0&v=paper_preview&mkt=zh-cn
    [6] DANIEL D, OUSSEDIK S, STEPHANE P.Airspace congestion smoothing by multi-objective genetic algorithm[C]//Proceedings of the 2005 ACM Symposiumon on Applied Computing.New York: ACM, 2005: 907-912.
    [7] YAOWIWAT S, LOHATEPANONT M, PUNYABUKKANA P.Multi objective micro genetic algorithm for combine and reroute problem[J].International Journal of Intelligent Systems and Technologies, 2007, 2(4):245-255. http://www.researchgate.net/publication/242405340_Multi_Objective_Micro_Genetic_Algorithm_for_Combine_and_Reroute_Problem
    [8] MA Z P, CUI D G, CHENG P.Dynamic network flow model for short-term air traffic flow management[J].IEEE Transactions on Systems, Man and Cybernetics-Part A:Systems and Humans, 2004, 34(3):351-358. doi: 10.1109/TSMCA.2003.822969
    [9] CAI K Q, ZHANG J, ZHOU C, et al.Using computational intelligence for large scale air route networks design[J].Applied Soft Computing, 2012, 12(9):2790-2800. doi: 10.1016/j.asoc.2012.03.063
    [10] GUAN X M, ZHANG X J, ZHU Y B, et al.An airway network flow assignment approach based on an efficient multiobjective optimization framework[J].The Scientific World Journal, 2015, 2015:302615. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Doaj000004268376
    [11] XIAO M M, CAI K Q, LINKE F.An evolutionary multi-objective approach for stochastic air traffic network flow optimization[C]//Proceedings of the 18th IEEE International Conference on Intelligent Transportation Systems (ITSC).Piscataway, NJ: IEEE Press, 2015: 2059-2065.
    [12] MEI Y, TANG K, YAO X.Decomposition-based memetic algorithm for multi-objective capacitated arc routing problem[J].IEEE Transactions on Evolutionary Computation, 2011, 15(2):151-165. doi: 10.1109/TEVC.2010.2051446
    [13] ZITZLER E, LAUMANNS M, THIELE L.SPEA2: Improving the strength Pareto evolutionary algorithm[C]//Proceedings of Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, 2002: 95-100.
    [14] CZYZZAK P, JASZKIEWICZ A.Pareto simulated annealing-A metaheuristic technique for multiple-objective combinatorial optimization[J].Journal of Multi-Criteria Decision Analysis, 1998, 7(1):34-47. doi: 10.1002/(SICI)1099-1360(199801)7:1<34::AID-MCDA161>3.0.CO;2-6
    [15] DEB K, PRATAP A, AGARWAL S.A fast and elitist multi-objective genetic algorithm:NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computing, 2002, 6(2):182-197. doi: 10.1109/4235.996017
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出版历程
  • 收稿日期:  2018-01-08
  • 录用日期:  2018-04-20
  • 刊出日期:  2018-09-20

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