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Abstract
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Article Information:
A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization
Yanhua Zhong and Changqing Yuan
Corresponding Author: Yanhua Zhong
Submitted: April 25, 2012
Accepted: May 16, 2012
Published: November 15, 2012 |
Abstract:
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Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the
shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms
declare that there has been better than the standard particle swarm. This study selects three kinds of
representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA
particle swarm optimization, quantum particle swarm optimization) and the standard particle swarm
optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed
number of iterations of the convergence speed and accuracy and the number of iterations under the fixed
convergence precision; analyzing these types of hybrid particle swarm optimization results and practical
performance. Test results show hybrid particle algorithm performance has improved significantly.
Key words: Differential evolutionary particle swarm optimization algorithm, function optimization, particle swarm optimization with GA algorithm, Quantum Particle Swarm Optimization (QPSO), , , ,
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Cite this Reference:
Yanhua Zhong and Changqing Yuan, . A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization. Research Journal of Applied Sciences, Engineering and Technology, (22): 4798-4804.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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