Applied Intelligence

, Volume 42, Issue 1, pp 24–35 | Cite as

Escape planning in realistic fire scenarios with Ant Colony Optimisation

  • Morten Goodwin
  • Ole-Christoffer Granmo
  • Jaziar Radianti


An emergency requiring evacuation is a chaotic event, filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation increases when predefined escape routes are blocked by a hazard, and there is a need to re-think which escape route is safest. This paper addresses automatically finding the safest escape routes in emergency situations in large buildings or ships with imperfect knowledge of the hazards. The proposed solution, based on Ant Colony Optimisation, suggests a near optimal escape plan for every affected person — considering dynamic spread of fires, movability impairments caused by the hazards and faulty unreliable data. Special focus in this paper is on empirical tests for the proposed algorithms. This paper brings together the Ant Colony approach with a realistic fire dynamics simulator, and shows that the proposed solution is not only able to outperform comparable alternatives in static and dynamic environments, but also in environments with realistic spreading of fire and smoke causing fatalities. The aim of the solutions is usage by both individuals, such as from a personal smartphone of one of the evacuees, or for emergency personnel trying to assist large groups from remote locations.


Escape planning Optimisation Ant colony optimisation Swarm intelligence 


  1. 1.
    Li Q, Rus D (2005) Navigation protocols in sensor networks. ACM Trans Sens Netw (TOSN) 1(1):3–35CrossRefGoogle Scholar
  2. 2.
    Li Q, De Rosa M, Rus D (2003) Distributed algorithms for guiding navigation across a sensor network. In: Proceedings of the 9th annual international conference on Mobile computing and networking. ACM, pp 313–325Google Scholar
  3. 3.
    Radianti J, Granmo OC, Bouhmala N, Sarshar P, Yazidi A, Gonzalez J (2013) Crowd models for emergency evacuation: a review targeting human-centered sensing. In: Proceedings of the 2013 46th Hawaii International Conference on System Sciences (HICSS). IEEE, pp 156–165Google Scholar
  4. 4.
    Thompson PA, Marchant EW (1995) A computer model for the evacuation of large building populations. Fire Saf J 24(2):131–148CrossRefGoogle Scholar
  5. 5.
    Thompson PA, Marchant EW (1995) Testing and application of the computer model simulex. Fire Safety Journal 24(2):149–166CrossRefGoogle Scholar
  6. 6.
    Hamacher H, Tjandra S (2002) Mathematical modelling of evacuation problems — a state of the art. Pedestrian and Evacuation Dynamics 2002:227–266. Google Scholar
  7. 7.
    Kim D, Shin S (2006) Local path planning using a new artificial potential function composition and its analytical design guidelines. Adv Robot 20(1):115–135CrossRefMathSciNetGoogle Scholar
  8. 8.
    Braun A, Bodmann B, Musse S (2005) Simulating virtual crowds in emergency situations. ACM, pp 244–252Google Scholar
  9. 9.
    Li F.Y., Kure Ø. (2005) Optimal physical carrier sensing range in multirate wireless ad hoc networks: analytical versus realistic In: Wireless conference 2005-next generation wireless and mobile communications and services (European Wireless), 11th European, VDE, pp 1–7Google Scholar
  10. 10.
    Liu S, Yang L, Fang T, Li J (2009) Evacuation from a classroom considering the occupant density around exits. Phys A: Statistical Mechanics and its Applications 388(9):1921–1928CrossRefGoogle Scholar
  11. 11.
    Wang J, Lo S, Sun J, Wang Q, Mu H (2012) Qualitative simulation of the panic spread in large-scale evacuation. SimulationGoogle Scholar
  12. 12.
    Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407(6803):487–490CrossRefGoogle Scholar
  13. 13.
    Jin T (2002) Visibility and human behavior in fire smoke. In: DiNenno PJ, Drysdale D, Beyler CL, Walton WD, Custer RLP, Hall JR Jr, Watts JM Jr (eds)The SFPE handbook of fire protection engineering, 3rd edn. Society of Fire Protection Engineers/National Fire Protection Association, Quincy, MassachusettsGoogle Scholar
  14. 14.
    Purser D (2008) Assessment of hazards to occupants from smoke, toxic gases, and heat. In: SFPE handbook of fire protection engineering, vol 4, pp 2–96Google Scholar
  15. 15.
    HSEIndicative human vulnerability to the hazardous agents present offshore for application in risk assessment of major accidents. Online: Accessed 10 Dec 2013Google Scholar
  16. 16.
    Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39CrossRefGoogle Scholar
  17. 17.
    Gutjahr W. (2000) A graph-based ant system and its convergence. Futur Gener Comput Syst 16(8):873–888CrossRefGoogle Scholar
  18. 18.
    Liu B, Abbas H, McKay B (2003) Classification rule discovery with ant colony optimization, vol 2003. IEEE, pp 83–88Google Scholar
  19. 19.
    Abolhasan M, Wysocki T, Dutkiewicz E (2004) A review of routing protocols for mobile ad hoc networks. Ad Hoc Netw 2(1):1–22CrossRefGoogle Scholar
  20. 20.
    Dorigo M, Stützle T (2010) Ant colony optimization: overview and recent advances.Handbook of metaheuristics, pp 227–263Google Scholar
  21. 21.
    Ducatelle F, Di Caro G, Gambardella L (2006) An analysis of the different components of the anthocnet routing algorithm.Ant Colony Optimization and swarm intelligence, pp 37–48Google Scholar
  22. 22.
    Murtala Zungeru A, Ang L (2012) Performance evaluation of ant-based routing protocols for wireless sensor networksGoogle Scholar
  23. 23.
    Goodwin M, Granmo OC, Radianti J, Sarshar P, Glimsdal S (2013) Ant colony optimisation for planning safe escape routes. In: Recent trends in applied artificial intelligence. Springer, pp 53–62Google Scholar
  24. 24.
    Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271CrossRefMathSciNetzbMATHGoogle Scholar
  25. 25.
    Di Caro G, Dorigo M (2011). Antnet: distributed stigmergetic control for communications networks. arXiv preprint arXiv:1105.5449
  26. 26.
    Di Caro G, Ducatelle F, Gambardella L (2005) Anthocnet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Eur Trans Telecommun 16(5):443–455CrossRefGoogle Scholar
  27. 27.
    Dressler F, Koch R, Gerla M (2012) Path heuristics using aco for inter-domain routing in mobile ad hoc and sensor networks.Bio-inspired models of network, information, and computing systems, pp 128–142Google Scholar
  28. 28.
    Selvan G, Pothumani S, Manivannan R, Senthilnayaki R, Balasubramanian K (2012) Weakness recognition in network using aco and mobile agents. In: 2012 International Conference on Advances in Engineering, Science and Management (ICAESM). IEEE, pp 459–462Google Scholar
  29. 29.
    McGrattan K, Forney GP (2008) Fire dynamics simulator (version 5), user’s guide, vol 1019. NIST special publication, pp 1–186Google Scholar
  30. 30.
    Chow WK (1995) Use of computational fluid dynamics for simulating enclosure fires. J Fire Sci 13(4):300–334CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Morten Goodwin
    • 1
  • Ole-Christoffer Granmo
    • 1
  • Jaziar Radianti
    • 1
  1. 1.Faculty of Engineering and Sciences, Department of ICTUnivesity of AgderGrimstadNorway

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