Accelerating consensus of self-driven swarm via a weighted model

In this paper, we study a weighted self-propelled agent system, wherein each agent's direction is affected by its spatial neighbors with different impacts. In the model, a tunable parameter α≥0 is introduced to weight the different impacts of spatial neighbors: if α=0, the agent's direction is updated by averaging all of neighbors directions and own direction, i.e., Vicsek model. Otherwise, with the increase of the value of α, the agent's direction is more affected by the agent who has small view angle between them. Interestingly, simulation results show that there exists an optimal α leading to the shortest convergence time. Thus, our findings provide a powerful mechanism for collective motions in biological and technological multiagent systems.