Cities are in a period of rapid urban development and high-rise buildings are constantly emerging. The characteristics of a fire in a high-rise building are the rapid spread of the fire, the difficulty of fighting the fire, and the difficulty of evacuation. Intelligent fire evacuation requires dynamic planning of paths in fire field, it is necessary to automatically adjust the evacuation route in the building according to the real-time information of the fire. In this paper, an improved bidirectional ant colony algorithm is proposed to optimize fire evacuation routes. In order to improve the global search capability of the algorithm, a bidirectional search strategy with the A* algorithm is designed for the ant colony algorithm, the blindness of the algorithm is reduced in the initial search, the pheromone update strategy is improved, and the convergence speed of the algorithm is increased. The fire scene information is combined with the steering penalty coefficient to improve the algorithm’s evaporation coefficient, heuristic function and transition probability, avoid the risk of falling into the local optimum, improve the search efficiency of the algorithm and the smoothness of the path, and effectively avoid areas affected by the fire. The effectiveness of the algorithm is verified by simulation.

中文翻译：

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双向ACO智能消防疏散路线优化

城市正处于城市快速发展时期，高层建筑不断涌现。高层建筑火灾的特点是火势蔓延迅速、扑救难、疏散难。智能消防疏散需要动态规划火场路径，需要根据火灾的实时信息自动调整建筑物内的疏散路线。本文提出一种改进的双向蚁群算法来优化火灾疏散路线。为了提高算法的全局搜索能力，针对蚁群算法设计了A*算法的双向搜索策略，降低了算法在初始搜索时的盲目性，改进了信息素更新策略，提高了算法的收敛速度。火灾现场信息与转向惩罚系数相结合，提高算法的蒸发系数、启发函数和转移概率，避免陷入局部最优的风险，提高算法的搜索效率和路径的平滑度，有效地避开受火灾影响的区域。通过仿真验证了算法的有效性。并有效避开受火灾影响的区域。通过仿真验证了算法的有效性。并有效避开受火灾影响的区域。通过仿真验证了算法的有效性。