title: Enhancing the convergence efficiency of a self-propelled agent system via a weighted model.
abstract: We investigate a weighted self-propelled agent system, wherein each agent's direction is determined by its spatial neighbors' directions with exponential weights according to the neighbor numbers. In order to describe the fact that some agents with more neighbors might have larger influence on its neighbors, we introduce a scaling exponent of the neighbor number between 0 and infinity. When the exponent is equal to 1, the convergence efficiency is enhanced in our simulation. Furthermore, as the exponent increases, i.e., the effect of weight becomes stronger, the network of agents becomes easier to achieve direction consensus.
authors.affiliation: Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, People's Republic of China. jianxi.gao@gmail.com
authors.forename: Jianxi
authors.initials: J
authors.lastname: Gao
authors.forename: Zhuo
authors.initials: Z
authors.lastname: Chen
authors.forename: Yunze
authors.initials: Y
authors.lastname: Cai
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Xu
mesh_terms: D000818:Animals; D001522:Behavior, Animal; D007700:Kinetics; D008954:Models, Biological