一种用于火灾疏散路径动态规划的算法

针对火灾发生时现有的疏散路径不能根据火情实时更改，可能会将逃生人员引向着火现场从而引起更大危险的问题，提出了一种用于火灾疏散路径动态规划的新型改进蚁群算法(Novel Improved Ant Colony Algorithm，NIACA)。首先通过A*算法提高初始信息素浓度，接着提出受火灾因素影响的当量距离改进启发函数，然后改进信息素更新规则来加快蚂蚁最优路径搜索速度，最后对路径进行平滑策略处理。实验结果表明，与原始蚁群算法相比，本文算法降低了算法前期盲目性，动态搜索能力强，能避免算法陷入局部最优，在火灾发生时能够快速准确地规划疏散路径，将逃生人员快速安全疏散到远离火场的安全出口。

An algorithm for fire evacuation path dynamic planning

In case of fire, the existing evacuation route cannot be changed in real time according to the fire situation, which may lead the escape personnel to the fire site, resulting in greater danger. In this paper, a novel improved ant colony algorithm (NIACA) is proposed for fire evacuation path dynamic planning. Firstly, the initial pheromone concentration was improved by A* algorithm, then the equivalent distance improved heuristic function which was affected by fire factors was proposed, then pheromone updating rules were improved to speed up the optimal path search, and finally the path smoothing strategy was used to process the optimal path. The experimental results show that compared with the original ant colony algorithm, the proposed algorithm reduces the blindness in the early stage of the algorithm, has strong dynamic search ability, can help the algorithmavoid falling into local optimization, can quickly and accurately plan the evacuation path in case of fire, and can quickly and safely evacuate the escape personnel to the safe exit away from the fire site.