Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System

Chen Wang, Lincoln C. Wood, Heng Li, Zhenye Aw, Abolfazl Keshavarzsaleh

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


Every minute counts in an event of fire evacuation where evacuees need to make immediate routing decisions in a condition of low visibility, low environmental familiarity, and high anxiety. However, the existing fire evacuation routing models using various algorithm such as ant colony optimization or particle swarm optimization can neither properly interpret the delay caused by congestion during evacuation nor determine the best layout of emergency exit guidance signs; thus bee colony optimization is expected to solve the problem. This study aims to develop a fire evacuation routing model "Bee-Fire" using artificial bee colony optimization (BCO) and to test the routing model through a simulation run. Bee-Fire is able to find the optimal fire evacuation routing solutions; thus not only the clearance time but also the total evacuation time can be reduced. Simulation shows that Bee-Fire could save 10.12% clearance time and 15.41% total evacuation time; thus the congestion during the evacuation process could be effectively avoided and thus the evacuation becomes more systematic and efficient.
Original languageEnglish
Article number7962952
JournalJournal of Applied Mathematics
Publication statusPublished - 24 Apr 2018

ASJC Scopus subject areas

  • Applied Mathematics


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