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Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)
Particle Swarm Optimization with Adaptive Parameters
Haier International Training Center, Qingdao, China
July 30-August 01
ISBN: 0-7695-2909-7
ASCII Text
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Dongyong Yang, Jinyin Chen, Naofumi Matsumoto, "Particle Swarm Optimization with Adaptive Parameters," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, vol. 1, pp. 616-621, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007.
 
 
BibTex
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@article{ 10.1109/SNPD.2007.47,
author = {Dongyong Yang and Jinyin Chen and Naofumi Matsumoto},
title = {Particle Swarm Optimization with Adaptive Parameters},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {1},
year = {2007},
isbn = {0-7695-2909-7},
pages = {616-621},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.47},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - Particle Swarm Optimization with Adaptive Parameters
SN - 0-7695-2909-7
SP616
EP621
A1 - Dongyong Yang,
A1 - Jinyin Chen,
A1 - Naofumi Matsumoto,
PY - 2007
KW - null
VL - 1
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
Dongyong Yang, Zhejiang University of Technology, China
Jinyin Chen, Zhejiang University of Technology, China
Naofumi Matsumoto, Ashikaga Institute of Technology, Japan
Particle swarm optimization is an effective evolution algorithm for global optimizing. Based on analysis of particle movements during evolution, parameter p is brought up to control the value of C1 and C2, which effects convergence rate of PSO. Aiming at solving different problems, corresponding p is adopted to improve performance. Particle confidence coefficient q is applied to weigh proper emphasize on itself best solution and global solution. Adaptive value of q is introduced to PSO to satisfy specific situation for each particle. Finally, performance of PSO with parameters p and q is testified by optimizing benchmark functions.
Citation:
Dongyong Yang, Jinyin Chen, Naofumi Matsumoto, "Particle Swarm Optimization with Adaptive Parameters," snpd, vol. 1, pp.616-621, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007
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