大型公共建筑物智能疏散路径优化自适应蚁群算法实现及应用

Adaptive Ant Colony Algorithm Based Evacuation Route Optimization Model and Application in Large Public Building Fire

目的将改进的自适应蚁群算法应用于大型公共建筑物智能疏散路径寻优,实现与建筑消防设施的联动控制.方法对建筑空间网络节点以及疏散通道静态属性和动态属性进行定义和描述,实现路径优化算法与火灾探测报警系统烟气态势信息的数据传递,以多层教学楼建筑物为例进行案例分析,并与Max-Min算法进行对比.结果参数取值为α=2,β=5,ρ=0.1时,笔者所建立的算法很好地克服了局部最优和死循环问题,提高了优化效率.结论改进的自适应蚁群算法适用于大型公共建筑物火灾时人员疏散路径的动态优化,可以实现与智能疏散系统的集成.

Based on the definition and description of the static and dynamic attributes of construction space network nodes and evacuation corridors, data transmission is successfully achieved between the Adaptiveant-colony-algorithm-based Evacuation Route Optimization Algorithm and the smoke trend information of fire detection alarm system. Case application was performed on a multiple-storey building, and the optimization results were compared with the traditional Max-Min Ant Colony Algorithm. The case study results illustrate that when parameters are assigned as α = 2,β = 5 and ρ = 0.1, the Adaptive-ant-colony-based Evacuation Route Optimization Algorithm can well improve the search efficiency, the problem of local optimization and stagnation of traditional optimization algorithms are overcome to a good extent. It shows that the improved Evacuation Route Optimization Algorithm is fit for the dynamic optimization of evacuation route in large scale public buildings and is well integrated with the intelligent evacuation system of buildings.