A modified Vicsek model for self-propelled agents with exponential neighbor weight and restricted visual field

Large flocks of wild beings can have coordinated motion with neither leading center nor global information. The Vicsek model and its new versions explained such collective behavior of self-propelled agents with the mechanism of un-weighted influence of neighbors and global visual field (GVF), which may not always fit the reality. In the present work, we take both exponential (λ$\lambda$) neighbor weight (ENW) and restricted visual field (RVF) into account, and investigate the combinatory effect of them. Based on this mechanism, in most cases of our simulation, stationary direction consensus _Va$V_a$ exhibits a maximum or decreases with VF$VF$, and a minimum convergence time _Tmin$T_{min}$ appears in RVF, which demonstrates that GVF is not necessary for coordinated motion, while RVF matched ENW would be reasonable. Moreover, we simulated various recipes for optimizing _Va$V_a$ and T$T$ in unfavorable cases, and found critical points separating inverse λ$\lambda$-dependent variations of T$T$. The present work may be useful in improving the efficiency of direction consensus of large flocks of artificial individuals besides the understanding of biological collective motion in nature.