DocumentCode
522789
Title
Notice of Retraction
Research on improved Particle Swarm Optimization algorithm based on space-division and layered search
Author
Xin Xie ; Peng Wu ; Ying Wu ; Honglei Chen ; Heng Zhang
Author_Institution
Sch. of Inf. Eng., East China Jiao Tong Univ., Nanchang, China
Volume
1
fYear
2010
fDate
10-11 May 2010
Firstpage
222
Lastpage
225
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.
To improve the efficiency of particle swarm optimization, this paper proposed a novel improved particle swarm optimization algorithm. The basic idea is parallel mechanism and space-division and layered search. The main contributions include, divided whole search space into n subares. For certain generations, let the best subarea be the search space. This shrinked the search space to the solution space. Proposed two layers partition of particles, the lower and topper work well for globaland local search respectively. By experiments on four benchmark functions, shows that the new algorithm increases precision by 80.37% compared with island population model PSO.
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.
To improve the efficiency of particle swarm optimization, this paper proposed a novel improved particle swarm optimization algorithm. The basic idea is parallel mechanism and space-division and layered search. The main contributions include, divided whole search space into n subares. For certain generations, let the best subarea be the search space. This shrinked the search space to the solution space. Proposed two layers partition of particles, the lower and topper work well for globaland local search respectively. By experiments on four benchmark functions, shows that the new algorithm increases precision by 80.37% compared with island population model PSO.
Keywords
particle swarm optimisation; search problems; globaland local search; layered search; parallel mechanism; particle swarm optimization algorithm; space division; Particle swarm optimization; Photonics; Power engineering and energy; Improved particle swarm optimization; layered search; space-division;
fLanguage
English
Publisher
ieee
Conference_Titel
Optics Photonics and Energy Engineering (OPEE), 2010 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-5234-7
Type
conf
DOI
10.1109/OPEE.2010.5508147
Filename
5508147
Link To Document