Multi-Objective optimization for massive pedestrian evacuation using ant colony algorithm

  • Authors:

  • Xinlu Zong;Shengwu Xiong;Zhixiang Fang;Qiuping Li

  • Affiliations:

  • School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

  • Venue:
  • ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
  • Year:
  • 2010

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Abstract

Evacuation route planning is one of the most crucial tasks for solving massive evacuation problem In large public places, pedestrians should be transferred to safe areas when nature or man-made accidents happen A multi-objective ant colony algorithm for massive pedestrian evacuation is presented in this paper In the algorithm, three objectives, total evacuation time of all evacuees, total routes risk degree and total crowding degree are minimized simultaneously Ants search routes and converge toward the Pareto optimal solutions in the light of the pheromone The experimental results show that the approach is efficient and effective to solve massive evacuation problem with rapid, reasonable and safe plans.