Finding overlapping communities from multimedia social networks is an interesting and important problem in data mining and recommender systems. However, extant overlapping community discovery with swarm intelligence often generates overlapping community structures with superfluous small communities. To deal with the problem, in this paper, an efficient algorithm (LEPSO) is proposed for overlapping communities discovery, which is based on line graph theory, ensemble learning, and particle swarm optimization (PSO). Specifically, a discrete PSO, consisting of an encoding scheme with ordered neighbors and a particle updating strategy with ensemble clustering, is devised for improving the optimization ability to search communities hidden in social networks. Then, a postprocessing strategy is presented for merging the finer-grained and suboptimal overlapping communities. Experiments on some real-world and synthetic datasets show that our approach is superior in terms of robustness, effectiveness, and automatically determination of the number of clusters, which can discover overlapping communities that have better quality than those computed by state-of-the-art algorithms for overlapping communities detection.