Improved Vicsek model based on two exponential coupling functions

Xinbiao Lu (), Wen Wu, Changjiang Han (), Wentao Zhang (), Gaofan Zhan () and Xiaolong Wu ()
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Xinbiao Lu: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China
Wen Wu: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China
Changjiang Han: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China
Wentao Zhang: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China
Gaofan Zhan: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China
Xiaolong Wu: College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 07, 1-15

Abstract: In order to speed up the consensus of the Vicsek model, existing studies assign different coupling strengths according to the inter-particle Euclidean distance, which effectively reduces the convergence step. In this paper, we propose two new coupling functions, resulting in two improved Vicsek models. Improved Model I introduces the exponential Relu coupling function into the original Vicsek model, and Improved Model II introduces the exponential Swish coupling function into the original Vicsek model. Simulations show that both improved Vicsek models have fewer convergence steps than the original improved Vicsek model based on the coupling function. With different system parameters, the number of convergence steps is smaller than the original improved Vicsek model based on the coupling function. In addition, the number of convergence steps further decreases when the exponent increases and then gradually remains constant. The improved Vicsek model II also achieves better results in resisting noise.

Keywords: Relu coupling function; Swish coupling function; convergence steps; exponent (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124502565

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