Notice of RetractionParallel Particle Swarm Optimization Based on PAM

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

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.