Feasible Route Determination Using Ant Colony Optimization in Evacuation Planning

Arief Rahman, Ahmad Kamil Mahmood
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引用次数: 9

Abstract

The most complex aspect of people movement on emergency condition is how to select the shortest way out from multi exit ways in the multi-floors building. The purpose of this paper is to demonstrate how an ant colony optimization (ACO) can be used in evacuation planning. Modified ACO applied as the algorithm to determine the feasible route during emergency evacuation. Physical obstacle during building evacuation such as bottleneck or disaster problem has been considered in transitional probability rule of ACO. By creating exit sign (an agent) with ACO as the algorithm, this agent decides the feasible route and guides the occupant during the evacuation. Two scenarios present to observe the performance of two different approaches in making decision during evacuation. When the obstacle appeared, route selection based on ACO algorithm has faster total evacuation time significantly than familiarity of environment exit method.
基于蚁群优化的疏散规划可行路径确定
在多层建筑中,如何从多个出口中选择最短的出口是紧急情况下人员流动最复杂的问题。本文的目的是演示如何将蚁群优化(ACO)用于疏散规划。将改进蚁群算法应用于紧急疏散过程中可行路线的确定。蚁群算法的转移概率规则考虑了建筑物疏散过程中的物理障碍,如瓶颈问题或灾害问题。通过以蚁群算法创建出口标志(agent),该agent在疏散过程中确定可行路线并引导乘员。通过两个场景来观察两种不同方法在疏散决策中的表现。当障碍物出现时,基于蚁群算法的路径选择比熟悉环境的退出方法的总疏散时间要快得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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