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山东大学学报(理学版) ›› 2017, Vol. 52 ›› Issue (1): 23-28.doi: 10.6040/j.issn.1671-9352.0.2016.316

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基于变邻域搜索改进的冲突解脱粒子群算法

马兰,李伟岸,尹天懿   

  1. 中国民航大学空中交通管理学院, 天津 300300
  • 收稿日期:2016-06-29 出版日期:2017-01-20 发布日期:2017-01-16
  • 作者简介:马兰(1966— ),女,博士,副教授,中国民航大学空中交通管理学院,研究方向为空中交通系统优化、空管信息处理等.E-mail:malan66@263.net
  • 基金资助:
    国家自然基金委员会和中国民用航空局联合基金资助项目(U1333116)

Improved particle swarm optimization for flight conflict resolution based on variable neighborhood search

MA Lan, LI Wei-an, YIN Tian-yi   

  1. College of Air Traffic Management, China Civil Aviation University, Tianjin 300300, China
  • Received:2016-06-29 Online:2017-01-20 Published:2017-01-16

摘要: 飞行冲突解脱是航空器安全运行的关键,粒子群优化(particle swarm optimization, PSO)算法和变邻域搜索(variable neighborhood search, VNS)算法都可以用于解决飞行冲突,但PSO算法接近最优解时收敛速度降低,VNS算法的全局搜索能力较差。为融合PSO算法全局搜索的快速收敛特性和VNS算法的局部搜索能力,提出了变邻域搜索改进的粒子群优化算法。仿真结果证明该算法能够快速搜索到全局最优解,继承了二者的优势,同时提高了最终解脱航迹的适应值,并减少了收敛时间。

关键词: 粒子群优化算法, 变邻域搜索算法, 飞行冲突解脱

Abstract: Flight conflict resolution is the key to the safe operation of aircrafts. Both Particle Swarm Optimization(PSO)and Variable Neighborhood Search(VNS)can be used to resolve flight conflict problems. But the rate of PSO will be lower when the solution is close to the optimal one, while VNS is weak at global search. In order to combine fast convergence for global search of PSO and local search of VNS, we proposed enhanced PSO with VNS. The simulation results prove that the improved algorithm can search the global optimal solution effectively, meanwhile inherit advantages of both two algorithms and improve the quality of final solved flight tracks.

Key words: particle swarm optimization, flight conflict resolution, variable neighborhood search

中图分类号: 

  • TP301
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