A Study of Acceleration Coefficients in Particle Swarm Optimization Algorithm Based on CPSO

Low convergence accuracy and the acceleration coefficient setting problem have always been the difficult and hot research points of particle swarm optimization algorithm. This paper introduces a composite particle swarm optimization CPSO based on the adaptive PSO and adaptive GA and applies CPSO in...

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Published in2010 2nd International Conference on Information Engineering and Computer Science pp. 1 - 4
Main Authors Pu Sun, Hua Sun, Wenquan Feng, Qi Zhao, Hongbo Zhao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Acceleration
Accuracy
Algorithm design and analysis
Convergence
Optimization
Particle swarm optimization
Training
Online AccessGet full text
ISBN1424479398
9781424479399
ISSN2156-7379
DOI10.1109/ICIECS.2010.5677729

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Abstract Low convergence accuracy and the acceleration coefficient setting problem have always been the difficult and hot research points of particle swarm optimization algorithm. This paper introduces a composite particle swarm optimization CPSO based on the adaptive PSO and adaptive GA and applies CPSO in the BP network training of turbo-pump fault diagnosis. In addition, the classical test function Rastrigrin is performed to test the performance of CPSO. The simulation results show that CPSO has obvious advantages over other optimization algorithms in terms of convergence accuracy and the law of acceleration coefficient setting is summed up through the analysis of the simulation results of acceleration coefficient distribution.
AbstractList Low convergence accuracy and the acceleration coefficient setting problem have always been the difficult and hot research points of particle swarm optimization algorithm. This paper introduces a composite particle swarm optimization CPSO based on the adaptive PSO and adaptive GA and applies CPSO in the BP network training of turbo-pump fault diagnosis. In addition, the classical test function Rastrigrin is performed to test the performance of CPSO. The simulation results show that CPSO has obvious advantages over other optimization algorithms in terms of convergence accuracy and the law of acceleration coefficient setting is summed up through the analysis of the simulation results of acceleration coefficient distribution.
Author Hongbo Zhao
Wenquan Feng
Pu Sun
Hua Sun
Qi Zhao
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  surname: Hongbo Zhao
  fullname: Hongbo Zhao
  organization: Sch. of Electron. & Inf. Eng., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China
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Snippet Low convergence accuracy and the acceleration coefficient setting problem have always been the difficult and hot research points of particle swarm optimization...
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SubjectTerms Acceleration
Accuracy
Algorithm design and analysis
Convergence
Optimization
Particle swarm optimization
Training
Title A Study of Acceleration Coefficients in Particle Swarm Optimization Algorithm Based on CPSO
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