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摘要翻译：
近年来，Vicsek模型受到了越来越多的关注，并得到了很好的发展。然而，目前对收敛时间的深入分析还很少。本文研究了影响Vicsek模型收敛时间的主要因素。通过大量的数值模拟，我们发现在无噪声情况下，收敛时间尺度服从一个幂律:$r2ln，其中$r$和$n$是视界半径和粒子数。此外，为了加快收敛速度，我们提出了一个新的模型，其中每个粒子的速度是可变的。与标准Vicsek模型相比，收敛时间明显缩短。
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英文标题：
《Accelerating consensus of self-driven swarm via adaptive speed》
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作者：
Jue Zhang, Yang Zhao, Baomei Tian, Liqian Peng, Hai-Tao Zhang,
Bing-Hong Wang, and Tao Zhou
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最新提交年份：
2008
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分类信息：
一级分类：Physics 物理学
二级分类：Disordered Systems and Neural Networks 无序系统与神经网络
分类描述：Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜；随机、非周期和准周期系统的性质；无序介质中的传输；本地化；由缺陷和无序介导的现象；神经网络
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一级分类：Physics 物理学
二级分类：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
In resent years, Vicsek model has attracted more and more attention and been well developed. However, the in-depth analysis on the convergence time are scarce thus far. In this paper, we study some certain factors that mainly govern the convergence time of Vicsek model. By extensively numerical simulations, we find the convergence time scales in a power law with $r^2\ln N$ in the noise-free case, where $r$ and $N$ are horizon radius and the number of particles. Furthermore, to accelerate the convergence, we propose a new model in which the speed of each particle is variable. The convergence time can be remarkably shortened compared with the standard Vicsek model.
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PDF链接：
https://arxiv.org/pdf/711.3896
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