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• W2917763764 abstract "We combine the velocity alignment and aggregation mechanisms to study the collective motion of active agents in noisy circumstances. The agents are located on a two-dimensional square plane, and the proportion of velocity alignment and aggregation interactions are, respectively, set to be $k$ and $1ensuremath{-}k$. In the case of $k=1$ our model is similar to the classical Vicsek model, while it degenerates to the view angle model for $k=0$. By tuning the intensity of the external noise $ensuremath{eta}$ and the proportional coefficient $k$, and carrying out extensive numerical simulations, we find that the system can exhibit diverse dynamic patterns widely observed in real biological systems. By means of finite-size scaling analysis, we confirm that the presence of the aggregation interaction affects not only the position of the critical noise ${ensuremath{eta}}_{c}$ (beyond which the agents display disordered motion) but also the type of the phase transition of the collective motion. In particular, under a weak external noise environment, the transition from disordered to ordered state by increasing $k$ (i.e., by decreasing the proportion of aggregation interaction) is found to be of first order. Besides, for moderate external noise, we also find the existence of the optimal proportion of the aggregation interaction for the system to achieve the highest degree of order. Our results highlights the important role of the aggregation interaction in the collective motion and may have promising potential applications in natural self-propelled particles and artificial multiagent systems." @default.
• W2917763764 created "2019-03-02" @default.
• W2917763764 creator A5019382289 @default.
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• W2917763764 date "2019-02-19" @default.
• W2917763764 modified "2023-10-12" @default.
• W2917763764 title "Collective motion patterns of self-propelled agents with both velocity alignment and aggregation interactions" @default.
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• W2917763764 doi "https://doi.org/10.1103/physreve.99.022609" @default.
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