Abstract

PAM (Partitioning around Medoid) is introduced to divide the swarm into several different sub-populations. PAM is one of k-medoids clustering algorithms based on partitioning methods. It attempts to divide n objects into k partitions. This algorithm overcomes the drawbacks of being sensitive to the initial partitions in k-means algorithm. In the parallel PSO algorithms, the swarm needs to be divided into several different smaller swarms. This study can be excellently completed by PAM. The aim of clustering is that particles within the same sub-population are relative concentrative, so that they can be relatively easy to learn. The purposes of this strategy are that the limited time will be spent on the most effective search; therefore, the search efficiency can also be significantly improved. In order to explore the whole solution space evenly, uniform design is introduced to generate an initial population, in which the population members are scattered uniformly over the feasible solution space. In evolution, uniform design is also introduced to replace some worse individuals. Based on abovementioned these technologies, a novel algorithm, parallel multi-swarm PSO based on k-medoids and uniform design, is proposed. A difference between the proposed algorithm and the others is that PAM and uniform design are both firstly introduced to parallel PSO algorithms.

Keywords:

Multi-swarm, medoids, PAM, parallel, particle swarm optimization, uniform design,

References

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.