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$\alpha \ge 0$ is introduced to weight the different impacts of spatial neighbors: if α=0$\alpha =0$, 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$\alpha$, 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$\alpha$ leading to the shortest convergence time. Thus, our findings provide a powerful mechanism for collective motions in biological and technological multiagent systems.