Proceedings of the 2013 International Conference on Software Engineering and Computer Science

A Study of Particle Swarm Optimization with Considering More Local Best Particles

Authors
Yen-Ching Chang, Li-Chun Lai, Chin-Chen Chueh, Yongxuan Xu, Cheng-Hsueh Hsieh
Corresponding Author
Yen-Ching Chang
Available Online September 2013.
DOI
10.2991/icsecs-13.2013.24How to use a DOI?
Keywords
optimization; particle swarm; algorithm; particle swarm optimization
Abstract

The velocity updating formula of the standard particle swarm optimization (PSO) only considers two particles: the local best particle and the global best particle. The global best particle can be viewed as the optimal one of all local best particles. In order to improve the optimizing performance and exploit all existing resources as fully as possible, we further study other local best particles how to affect the results of optimization in this paper. Experimental results show that a suitable selection of the number of local best particles will result in higher performance.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2013 International Conference on Software Engineering and Computer Science
Series
Advances in Intelligent Systems Research
Publication Date
September 2013
ISBN
978-90786-77-82-6
ISSN
1951-6851
DOI
10.2991/icsecs-13.2013.24How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yen-Ching Chang
AU  - Li-Chun Lai
AU  - Chin-Chen Chueh
AU  - Yongxuan Xu
AU  - Cheng-Hsueh Hsieh
PY  - 2013/09
DA  - 2013/09
TI  - A Study of Particle Swarm Optimization with Considering More Local Best Particles
BT  - Proceedings of the 2013 International Conference on Software Engineering and Computer Science
PB  - Atlantis Press
SP  - 116
EP  - 119
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsecs-13.2013.24
DO  - 10.2991/icsecs-13.2013.24
ID  - Chang2013/09
ER  -