Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 134-137.

• Intelligent Computing • Previous Articles     Next Articles

Path Planning Method of Large-scale Fire Based on Multiple Starting Points and Multiple Rescue Points

LI Shan-shan, LIU Fu-jiang, LIN Wei-hua   

  1. (School of Information Engineering,China University of Geosciences,Wuhan 430074,China)
  • Online:2019-11-10 Published:2019-11-20

Abstract: Aiming at the real-time path planning of joint emergency rescue with multiple starting points,multiple rescue points and multiple exits,an improved ant colony algorithm(IACA) was proposed and a combined optimization path construction method was designed.In order to improve the convergence of ant colony algorithm,this paper updated the equivalent distance between two position nodes in real time,improved the pheromone update rules,and adaptively adjusted the pheromone volatility parameters.A local search algorithm that effectively combines with ant colony algorithm was constructed to improve the ability of fast optimization presented.To solve the limitation of the single emergency rescue of traditional path planning,this paper proposes a path construction method based on combined optimization ant colony algorithm.The simulation results show that the improved ant colony algorithm based on combinatorial optimization can quickly find a set of paths from multiple starting points to multiple rescue points and back to multiple exits in real time,and its convergence speed and the shortest path are better,which can improve the rate and optimization in large emergency rescue route planning.

Key words: Emergency rescue, Improved ant colony algorithm, Path planning

CLC Number: 

  • TP301.6
[1]公安部消防局.中国消防年鉴2015[M].昆明:云南人民出版社,2015.
[2]公安部消防局.中国消防年鉴2014[M].昆明:云南人民出版社,2014.
[3]公安部消防局.中国消防年鉴2013[M].昆明:云南人民出版社,2013.
[4]公安部消防局.中国消防年鉴2012[M].昆明:云南人民出版社,2012.
[5]公安部消防局.中国消防年鉴2011[M].昆明:云南人民出版社,2011.
[6]公安部消防局.中国消防年鉴2010[M].昆明:云南人民出版社,2010.
[7]DORIGO M.Optimization,learning and natural algorithms[D].Milano:Politecnico di Milano,1992.
[8]YANG J,SHI M,HAN Z.Research Intelligent Fire Evacuation System Based on Ant Colony Algorithm and MapX[C]∥Se-venth International Symposium on Computational Intelligence and Design.IEEE,2015:100-103.
[9]尹克强,郭勇,王丹.WSN在地铁车辆车载设备火灾预警中应用[J].仪表技术与传感器,2014,3(5):87-89.
[10]刘笑笑,汪云甲,毕京学,等.矿井火灾逃生路径规划及其三维仿真研究[J].中国安全科学学报,2017,27(10):26-31.
[11]ADHIKARI J,PATIL S.Double threshold energy aware load balancing in cloud computing [C]∥2013 Fourth Internat lal Conference on Computing,Communications and Networking Technologies (ICCCNT).IEEE,2013:1-6.
[12]NING J,ZHANG Q,ZHANG C,et al.A best-path-updating information-guided ant colony optimization algorithm[J].Information Sciences,2018,433-434:142-162.
[13]VISERAS A,LOSADA R O,MERINO L.Planning with ants:Efficient path planning with rapidly exploring random trees and ant colony optimization[J].Int J of Advanced Robotic Systems,2016,13(5):1-16.
[14]周锦龙,易永华.基于蚁群算法的矿井救援最短路径研究[J].煤炭技术,2015,34(11):196-197.
[15]中华人民共和国国家标准.GB 50016-2014.建筑设计防火规范[S].北京:中国计划出版社,2014.
[16]JIAO Z,MA K,RONG Y,et al.A path planning method using adaptive polymorphic ant colony algorithm for smart wheelchairs[J].Journal of Computational Science,2018,25:50-57.
[17]MUKHOPADHYAY A,MAULIK U,BBANDYOPADHYA-YS,et al.Survey of Multiobjective Evolutionary Algorithms for Data BANDYOPADHYAY:Part II [J].IEEE Transactions on Evolutionary Computation,2014,18(1):20-35.
[18]王晓燕,杨乐,张宇,等.基于改进势场蚁群算法的机器人路径规划[J].控制与决策,2018,33(10):775-1781.
[19]杜鹏桢,唐振民,陆建峰,等.不确定环境下基于改进萤火虫算法的地面自主车辆全局路径规划方法[J].电子学报,2014,42(3):616-624.
[20]PANDA R K,CHOUDHURY B B.An effective path planning of mobile robot using genetic algorithm[C]∥IEEE Int Conf on Computational Intelligence & Communication Technology.Ghaziabad,2015:287-291.
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