An Improved PSO Algorithm Based on Chaos and Population Core

DOI

10.2991/icsecs-13.2013.26How to use a DOI?

Keywords

particle swarm optimization; population core; self-adaptive; position of mutation

Abstract

particle swarm optimization (PSO) algorithm is often trapped in local optima and low accuracy in convergence. Following an analysis of the cause of the premature convergence, a novel particle swarm optimization algorithm based on neighborhood explored and chaos is proposed, which is called PCC-PSO. Chaos is introduced to initialized the particle’s position to improve the diversity, the population core learning mechanism and global extreme mutation operator is also introduced to enhance the global search ability. Compared with other three improved algorithms, the PCC-PSO converges faster, prevents the premature convergence problem more effectively.

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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TY  - CONF
AU  - Zhongyong Wu
AU  - Lili Gan
PY  - 2013/09
DA  - 2013/09
TI  - An Improved PSO Algorithm Based on Chaos and Population Core
BT  - Proceedings of the 2013 International Conference on Software Engineering and Computer Science
PB  - Atlantis Press
SP  - 125
EP  - 127
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsecs-13.2013.26
DO  - 10.2991/icsecs-13.2013.26
ID  - Wu2013/09
ER  -