Vortex Particle Swarm Optimization in 2D Cases

This paper presents the performance of algorithm Vortex Particle Swarm Optimization (VPSO) using the well-known 2D test functions. The algorithm VPSO mimics living organism strategies such as foraging and predator avoidance. In this way, VPSO employ vorticity features in order to escape from local minima. The swarm convergence is given by translational movements and the exploration by a dispersion behavior of the swarm. These types of behavior are chosen via suitable parameter selection to switch between translational (convergence) and vortex-like movements (dispersion). The results show a good performance of VPSO when the swarm has local initialization.