A weighted adaptive-velocity self-organizing model and its high-speed performance

This paper proposed an improved adaptive-velocity self-organizing model as a prospective candidate in order to enhance high-speed convergence and accelerate convergence. Moreover, weights are assigned to reinforce convergence under super high-speed circumstances. Convergence performance is assessed via group polarization, convergence ratio and convergent time. As verified by numerical experiments, superior high-speed performance and fast convergence are achieved simultaneously in the improved adaptive-velocity model. The weighted adaptive model prominently improved super high-speed performance with short convergent time and low energy consumption. Then, the parameter space of the weighted adaptive flocking model is investigated.