Enhancing the convergence efficiency of a self-propelled agent system via a weighted model

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.