基于自适应蚁群算法的地铁人员疏散模型

Subway Personnel Evacuation Model Based on Adaptive Ant Colony Algorithm

针对地铁突发事件人员疏散模型与路径优化问题,提出基于神经网络自适应蚁群算法,通过神经网络的自适应能力动态计算通行难易系数,为蚁群信息素浓度更新和启发函数提供分流指引,有效避免蚁群因算法提早收敛拥堵于同一路径。通过仿真实验证明,新算法可以有效解决初始化蚁群路径选择的盲目性和随机性,能够在最短时间内为密集人群场所提供高效、安全的疏散方案。

Aiming at the problem of emergency evacuation model and route optimization of subway, an adaptive ant colony algorithm,base on neural network,is put forward in this paper,which uses the adaptive ability of neural network to calculate passing difficulty,provides hierarchical guidance for the pheromone concentration updating and heuristic function of ant colony to effectively avoid the problems of all ants congestion in the same path.Finally,the simulation experiments show that the new algorithm can solve the blindness and randomness of the initial ant colony path selection and provide a safe evacuation plan in shortest time for dense crowd.