In the study of pedestrian evacuation dynamics, pedestrian aggressiveness is important to consider as it potentially impacts evacuation efficiency. A representative example is the ‘faster-is-slower’ phenomenon. In reality, a crowd is a mixture of patient and impatient pedestrians, rather than a collection of like individuals. The impact of such heterogeneity has been reported in previous studies; however, these studies are all based on handcrafted solutions. In this study, we propose a heuristic approach for incorporating a genetic algorithm into the floor field cellular automata model to investigate the optimal initial pedestrian evacuation condition. Pedestrian aggressiveness is represented by two different approaches: the revised friction function and the persisting probability. To verify our genetic algorithm approach, we compared the results with the theoretical analysis and simulation results based on handcrafted solutions. The first contribution of this study is that the initial condition, a mixture of patient and impatient pedestrians, affects evacuation efficiency. Another contribution of this study is the heuristic approach incorporating a genetic algorithm. Our approach exerts the computational efficiency advantage of the floor field cellular automata model.

在行人疏散动态研究中，行人的攻击性很重要，因为它可能影响疏散效率。一个代表性的例子是“越快越慢”现象。实际上，人群是耐心和不耐烦的行人的混合体，而不是相似个体的集合。这种异质性的影响在以前的研究中已有报道；然而，这些研究都是基于手工制作的解决方案。在这项研究中，我们提出了一种启发式方法，将遗传算法结合到地板场元胞自动机模型中，以研究最佳的初始行人疏散条件。行人攻击性由两种不同的方法表示：修正的摩擦函数和持续概率。为了验证我们的遗传算法方法，我们将结果与基于手工解决方案的理论分析和仿真结果进行了比较。本研究的第一个贡献是初始条件，即耐心和不耐烦行人的混合，会影响疏散效率。这项研究的另一个贡献是结合了遗传算法的启发式方法。我们的方法发挥了地板场元胞自动机模型的计算效率优势。