基于改进蚁群算法的办公区火灾疏散路径优化研究  

作　　者：张庆 伍爱友 曾雯[1] 

机构地区：[1]湖南科技大学资源环境与安全工程学院,湖南湘潭411201 [2]湖南科技大学煤矿安全开采技术湖南省重点实验室,湖南湘潭411201

出　　处：《科技创新与应用》2024年第36期66-70,共5页Technology Innovation and Application

基　　金：2020年湖南省教育厅重点资助项目(20A188)。

摘　　要：火灾发生时,传统的疏散路线往往是固定的,不能根据火情实时调整,这可能会导致逃生人员被引向火源,增加逃生的危险性。改进蚁群算法(IACO)应用于火灾疏散路径动态规划,通过优化启发函数和信息素更新方式,提升全局搜索能力和疏散效率。结合火灾动力学软件(FDS),实时获取火灾环境参数与疏散时间,动态判断疏散出口的安全性,避免传统固定疏散路径引发的安全隐患。仿真实验表明,IACO模型相较传统ACO能够实时调整疏散路径,避开危险区域和拥堵出口,显著减少疏散时间,提高路径规划的安全性与有效性。When a fire breaks out,traditional evacuation routes are often fixed and cannot be adjusted in real time according to the fire situation.This may lead to people fleeing to be led to the fire source and increase the risk of escape.The improved ant colony algorithm(IACO)is applied to dynamic planning of fire evacuation paths.By optimizing heuristic functions and pheromone update methods,global search capabilities and evacuation efficiency are improved.Combined with fire dynamics software(FDS),fire environment parameters and evacuation time are obtained in real time,the safety of evacuation exits is dynamically judged,and safety hazards caused by traditional fixed evacuation paths are avoided.Simulation experiments show that compared with traditional ACO,the IACO model can adjust evacuation routes in real time,avoid dangerous areas and congested exits,significantly reduce evacuation time,and improve the safety and effectiveness of path planning.

关 键 词：火灾模拟 火灾疏散 改进蚁群算法 路径规划 办公区 

分 类 号：TU998.1[建筑科学—市政工程]