On how percolation threshold affects PSO performance
This work addresses performance optimization for PSO algorithms in robotics, but it is incremental as it builds on known topology effects.
The paper investigates how the percolation threshold influences neighborhood topology in particle swarm optimization (PSO), finding that low radius values improve results and reduce computational complexity for robots with limited sensing.
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.