Is there any advantage in using Particle Swarm Optimization for clustering than K-Means?
K-means makes locally the optimal decision. Most of the time (in particular when this objective works reasonably well at all) this works quite well to find a good local optimum. I doubt that using an approach such as PSO gives you any advantage here - the problem is just too simple, and k-means has the speed advantage, while PSO is unlikely to find other optima.
And in particular the better k-means algorithms such as Hamerly, Elkan, ... are so fast, they'll be able to run with hundreds of random restarts in the time needed for just one PSO.