Promoting collective motion of self-propelled agents by discarding short-range interactions
Han-Xin Yang and
Zhihai Rong
Physica A: Statistical Mechanics and its Applications, 2015, vol. 432, issue C, 180-186
Abstract:
We study the collective motion of self-propelled agents with the restricted view. The field of view of each agent is an annulus bounded by the outer radius r and inner radius αr, where α is a tunable parameter. We find that there exists an optimal value of α leading to the highest degree of direction consensus. This phenomenon indicates that there exists superfluous communication in the collective motion of self-propelled agents and short-range interactions hinder the direction consensus of the system. The value of optimal α decreases as the absolute velocity increases, while it increases as the outer radius r and the system size increase. For a fixed value of α, direction consensus is enhanced when the absolute velocity is small, the outer radius or the system size is large.
Keywords: Collective motion; Vicsek model; Restricted interaction (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437115002939
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:432:y:2015:i:c:p:180-186
DOI: 10.1016/j.physa.2015.03.031
Access Statistics for this article
Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis
More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().