Notice of RetractionParallel Particle Swarm Optimization Based on PAM

PAM (Partitioning Around Medoids) was one of the first k-medoids algorithms. It attempts to determine k partitions for n objects. In the parallel particle swarm optimization, the number of particle is generally not too much. Therefore, PAM is used to divide the swarm is a best choises. This can make not only the location of particles within the same sub-swarm be in the relative concentrative, but also the particles be relatively easy to learn. Since the limited time will be spent on the most effective search, therefore, the search efficiency can also be significantly improved. The parallel algorithms are improved according to the characteristics of them. Only in certain conditions are communications carried on, so that ineffective communications can be avoided to reduce the time spent for them. The Simulation results confirm that the algorithms have a high convergence speed and convergence accuracy.