  Statistical evidence of the influence of neighborhood topology on the
performance of particle swarm optimization (PSO) algorithms has been shown in
many works. However, little has been done about the implications could have the
percolation threshold in determining the topology of this neighborhood. This
work addresses this problem for individuals that, like robots, are able to
sense in a limited neighborhood around them. Based on the concept of
percolation threshold, and more precisely, the disk percolation model in 2D, we
show that better results are obtained for low values of radius, when
individuals occasionally ask others their best visited positions, with the
consequent decrease of computational complexity. On the other hand, since
percolation threshold is a universal measure, it could have a great interest to
compare the performance of different hybrid PSO algorithms.
