基于改进蚁群算法的室内疏散路径优化

针对传统蚁群算法在解决室内疏散问题时存在收敛速度慢、容易陷入局部最优的缺陷问题，将火场的动态参数引入到蚁群算法中，对其路径选择策略、启发函数和信息素更新策略进行改进，为整个疏散群体求解更优的疏散路径。运用改进的蚁群算法对室内人员的疏散路径进行动态规划，考虑了路径的实时拥挤度，避免了疏散人员局部实现路径优化的瓶颈效应。将分析结果与基本蚁群算法的规划结果进行比较验证，研究结果显示，优化算法缩短了疏散时间和规划路径，提高了疏散效率和搜索速度。

Optimization of indoor evacuation path based on improved ant colony algorithm

Against the defect of using traditional ant colony algorithm to solve the problem of indoor evacuation, such as slow convergence, and local optimum, the dynamic parameters of fire field are introduced into the ant colony algorithm, and the path selection strategy, heuristic function and pheromone updating strategy are improved to find the better evacuation path for the whole evacuation group. The improved ant colony algorithm is used to dynamically plan the evacuation path of indoor personnel, considering the real- time congestion degree of the path, avoiding the bottleneck effect of local route optimization of evacuation personnel. The analysis results are compared with the planning results of basic ant colony algorithm. The research results show that the optimization algorithm shortens the evacuation time and planning path, and improves the evacuation efficiency and search speed.