Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach: Fid-Bau Portal

Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach

Fang, Zhixiang / Zong, Xinlu / Li, Qingquan / Li, Qiuping / Xiong, Shengwu

Research highlights ► A destination-oriented evacuation network organization and two critical strategies (the hierarchical heuristic searching strategy of an ant, and the binary pheromone updating strategy) are adopted to improve the searching efficiency of an intelligent ant’s path in finding evacuation routings. ► Evacuation time, distance and congestion degree are both embedded into evacuation routing research, which makes space-time evacuation paths more practical and reasonable. ► The proposed multi-objective approach provides an alternative perspective to solve evacuation routing problem in stadium.

Abstract Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space–time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.