DocumentCode
1896858
Title
Notice of Retraction
Parallel Particle Swarm Optimization Based on PAM
Author
Jie Zhang ; Yuping Wang ; Junhong Feng
Author_Institution
Dept. of Comput. Sci. & Technol., Guangzhou Univ., Guangzhou, China
fYear
2010
fDate
25-26 Dec. 2010
Firstpage
1
Lastpage
4
Abstract
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.
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.
Keywords
convergence; parallel algorithms; particle swarm optimisation; k-medoids algorithm; parallel algorithms; parallel particle swarm optimization; partitioning around medoids; Algorithm design and analysis; Clustering algorithms; Computer science; Convergence; Particle swarm optimization; Partitioning algorithms; Tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on
Conference_Location
Wuhan
ISSN
2156-7379
Print_ISBN
978-1-4244-7939-9
Type
conf
DOI
10.1109/ICIECS.2010.5678158
Filename
5678158
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