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Fire Evacuation using Ant Colony Optimization Algorithm

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Kanika Singhal, Shashank Sahu

Kanika Singhal and Shashank Sahu. Article: Fire Evacuation using Ant Colony Optimization Algorithm. International Journal of Computer Applications 139(8):17-20, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Kanika Singhal and Shashank Sahu},
	title = {Article: Fire Evacuation using Ant Colony Optimization Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {8},
	pages = {17-20},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


ACO is one of all-powerful meta-heuristics algorithms and some researchers have expressed the strength of some applications with the algorithm the evacuation route planning is the key aspect in case of fire disaster.Ant Colony Optimization (ACO) can be used in rescue planning. Altered ACO applied as the algorithm to demonstrate the potential path during emergency rescue. Physical interference during building rescue such as blockage or disaster complication has been studied in transitional probability rule of ACO.There exits multiple route from source of fire to the exit, hence the selection of shortest path is the fundamental objective of evacuation route planning.The objective of the algorithm is to minimizes the entire rescue time of all evacuees.The ant colony optimization algorithm is used to solve the complications of shortest route planning. Presented paper gives a comparative overview of various emergency scenarios using ant colony optimization algorithm.


  1. Naiwei Cheng, “ An Optimization Method for Dynamic Evacuation Route Programming Based on Improved Ant Colony Algorithm” Shenyang Aerospace University, 2010.
  2. Arief Rahman, Ahmad Kamil Mahmood, “Feasible Route Determination Using Ant Colony Optimization in Evacuation Planning”, The 5th Student Conference on Research and Development, December, 2007.
  3. Yuan Yuan, Dingwei Wang,” Multi-Objective Path Selection Model and Algorithm for Emergency Evacuation”, Proceedings of the IEEE International Conference on Automation and Logistics 2007.
  4. Changbo Wang, Chenhui Li, Yuhua Liu, Jin Cui, Tianlun Zhang, “Behavior-based Simulation of Real-time Crowd Evacuation”, 12th International Conference on Computer-Aided Design and Computer Graphic,2011.
  5. PengfeiDuan, ShengwuXionh, HongxinJiang“Multi-objective Optimization Model Based on Heuristic Ant Colony Algorithm for Emergency Evacuation”, International IEEE Conference on Intelligent Transportation Systems Anchorage, Alaska, USA, 2012.
  6. Feng Zhang, Min Liu*, Zhuo Zhou, Wei-mingShen,”Quantum Ant Colony Algorithm-Based Emergency Evacuation Path Choice Algorithm”, School of Electronic and Information Engineering, 2013.
  7. Jing Yang, Mingquan Shi1,Zhenfeng Han, “Research intelligent fire evacuation system based on ant colony algorithm and MapX”, International Symposium on Computational Intelligence and Design, 2014.
  8. ShiYong Li, YongQiang Chen, Yan Li, “Ant colony algorithm and its application” M. Harbin Institute of Technology Press, 2004.
  9. LiMin Xia, Hua Wang, “Research for optimal routing problem base on ant colony algorithm,” Computer Engineering and Design, 2007.
  10. XinluZong, ShengwuXiong, Zhixiang Fang, Wanru Lin, Multi objective ant colony optimization model for emergency evacuation, proceedings-2010 6th International Conference on Natural Computation, 2010.
  11. Liu Li-Qiang,Dai Yun-Tao and Wang Li-Hua,”Ant Colony Algorithm Parameters Optimization,”Computer Engineering, 2008.
  12. W. F. Yuan, K. H. Tan, “An evacuation model using cellular automata,” Physica A, 2007.
  13. LU huibin, FAN qinhui, JIA xingwei. “Improved adaptive ant colony optimization algorithm”.ComputerEngineering and Design,2005.
  14. M. Dorigo, L.M. Gambardella, “Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem,” IEEETransactions on Evolutionary Computation, 1997.
  15. Z.X. Fang, X.L. Zong, Q.Q. Li, Q.P. Li, S.W. Xiong, “Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach,” Journal of Transport Geography, May 2011.
  16. M. Dorigo and L. M. Gambardella, "Ant colony system: a cooperative learning Approach to the traveling sales man problem," IEEE Transaction on Evolutionary Computation, 1997.
  17. XinluZong, ShengwuXiong, Zhixiang Fang, Wanru Lin. Multi-objective Ant Colony Optimization Model for Emergency Evacuation, Natural Computation (ICNC), Sixth International Conference on, 2010.
  18. M. Dorigo, V. Maniezzo, and A. Colomi, "Ant system: optimization by a colony of cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics, 1996.
  19. M. Dorigo, and K. Socha, "An introduction to ant colony optimization, IRIDIA” Bruxelles, Technical report series, 2007.