Evacuation path optimization (EPO) is a crucial problem in crowd and disaster management. With the consideration of dynamic evacuee velocity, the EPO problem becomes nondeterministic polynomial-time hard (NP-Hard). Furthermore, since not only one single evacuation path but multiple mutually restricted paths should be found, the crowd evacuation problem becomes even challenging in both solution spatial encoding and optimal solution searching. To address the above challenges, this article puts forward an ant colony evacuation planner (ACEP) with a novel solution construction strategy and an incremental flow assignment (IFA) method. First, different from the traditional ant algorithms, where each ant builds a complete solution independently, ACEP uses the entire colony of ants to simulate the behavior of the crowd during evacuation. In this way, the colony of ants works cooperatively to find a set of evacuation paths simultaneously and thus multiple evacuation paths can be found effectively. Second, in order to reduce the execution time of ACEP, an IFA method is introduced, in which fractions of evacuees are assigned step by step, to imitate the group-based evacuation process in the real world so that the efficiency of ACEP can be further improved. Numerical experiments are conducted on a set of networks with different sizes. The experimental results demonstrate that ACEP is promising.

中文翻译：

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蚁群疏散规划器：具有增量流量分配的蚁群系统，用于多路径人群疏散

疏散路径优化 (EPO) 是人群和灾害管理中的一个关键问题。考虑到动态疏散速度，EPO 问题变为非确定性多项式时间困难 (NP-Hard)。此外，由于不仅应该找到一条单一的疏散路径，而且应该找到多条相互限制的路径，因此人群疏散问题在解决方案空间编码和最优解决方案搜索方面变得更加具有挑战性。针对上述挑战，本文提出了一种蚁群疏散规划器（ACEP），其具有新颖的解决方案构建策略和增量流分配（IFA）方法。首先，不同于传统的蚂蚁算法，每只蚂蚁独立构建一个完整的解决方案，ACEP使用整个蚂蚁群体来模拟人群在疏散过程中的行为。这样，蚁群协同工作，同时寻找一组疏散路径，从而可以有效地找到多条疏散路径。其次，为了减少ACEP的执行时间，引入IFA方法，逐步分配撤离人员的分数，以模仿现实世界中基于群体的撤离过程，从而进一步提高ACEP的效率改进。数值实验是在一组不同大小的网络上进行的。实验结果表明ACEP是有前途的。模仿现实世界中基于群体的疏散过程，从而进一步提高ACEP的效率。数值实验是在一组不同大小的网络上进行的。实验结果表明ACEP是有前途的。模仿现实世界中基于群体的疏散过程，从而进一步提高ACEP的效率。数值实验是在一组不同大小的网络上进行的。实验结果表明ACEP是有前途的。