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根据蚁群算法的特点,将其应用到大型公共建筑火灾的智能逃生路径规划研究中,为了针对火灾发生时各种复杂的危险因素,降低人员伤亡的可能性。文章引入了蚂蚁体力值的概念,使蚂蚁在寻找最优路径时同时也考虑到风险因素。研究结果表明,改进后的蚁群算法不仅能更好的适应危险环境,在收敛性上也能满足实时计算的需求。
According to the characteristics of ant colony algorithm, it is applied to the research of intelligent escape path planning for large-scale public building fire. In order to reduce the risk of casualties due to various complicated risk factors in the event of a fire. The article introduced the concept of ants physical value, so that ants looking at the optimal path also take into account the risk factors. The results show that the improved ant colony algorithm can not only adapt to the dangerous environment better, but also satisfy the demand of real-time computation in convergence.