Particle Swarm optimization with pbest Perturbations

摘要

Restarts are a popular remedy to address (premature) convergence in metaheuristics. In Particle Swarm optimization, it has been observed that swarms often “stall” as opposed to “converge”. A stall occurs when all of the forward progress that could occur is instead rejected as failed exploration. Since the swarm is in a good region of the search space with the potential to make more progress, a (random) restart could be counter productive. We instead introduce a method to address the stall mechanism. The introduction of perturbations to the pbest positions leads to significant improvements in the performance of standard Particle Swarm optimization.