DocumentCode
514916
Title
Notice of Retraction
Particle Swarm Optimization Based on an Improved Learning Strategy
Author
Xu Bai ; Yan-xia Ding
Author_Institution
Dept. of Network Eng., Hebei Normal Univ., Shijiazhuang, China
Volume
2
fYear
2010
fDate
6-7 March 2010
Firstpage
395
Lastpage
398
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.
Particle Swarm Optimization (PSO) is a recently proposed population-based evolutionary algorithm, which shows good performance in many optimization problems. In order to enhance the performance of PSO, this paper presents an improved PSO algorithm, namely LSPSO, by using a novel learning strategy. Experimental studies on six well-known benchmark functions show that the proposed approach shows good optimization performance and outperforms other three improved PSO algorithms on majority of test functions.
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.
Particle Swarm Optimization (PSO) is a recently proposed population-based evolutionary algorithm, which shows good performance in many optimization problems. In order to enhance the performance of PSO, this paper presents an improved PSO algorithm, namely LSPSO, by using a novel learning strategy. Experimental studies on six well-known benchmark functions show that the proposed approach shows good optimization performance and outperforms other three improved PSO algorithms on majority of test functions.
Keywords
evolutionary computation; particle swarm optimisation; PSO; benchmark functions; learning strategy improvement; particle swarm optimization; population based evolutionary algorithm; Benchmark testing; Computer science; Computer science education; Convergence; Educational technology; Evolutionary computation; Insects; Laboratories; Particle swarm optimization; Transistors; function optimization; learning strategy; particle swarm optimization (PSO);
fLanguage
English
Publisher
ieee
Conference_Titel
Education Technology and Computer Science (ETCS), 2010 Second International Workshop on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-6388-6
Type
conf
DOI
10.1109/ETCS.2010.68
Filename
5459894
Link To Document