Proceedings of the 2012 National Conference on Information Technology and Computer Science

A Hybrid Evolution Algorithm with Application Based on Chaos Genetic Algorithm and Particle Swarm Optimization

Authors
Yu Fu, Bing Wang, Shao-Hua Xu
Corresponding Author
Yu Fu
Available Online November 2012.
DOI
https://doi.org/10.2991/citcs.2012.163How to use a DOI?
Keywords
chaotic genetic algorithm, particle swarm optimization, hybrid evolution algorithm, algorithm design, continuous optimization
Abstract
Aiming at the complex function extreme value and non-linear system model parameters adjust, a hybrid optimization algorithm based on chaos GA combined with PSO is proposed in the paper. With application of applying experience of PSO, sharing information of GA, and traversing pathway of chaos, the adaptive switching of two algorithms are implemented through estimating the fitness and optimization efficiency, which may quickly obtain the global optimal solution. The proposed algorithm is applied to the function extreme optimizing and the parameter adjusting of fuzzy controller, and the experimental results show that the optimization ability of proposed algorithm is obviously superior to the single one, and that the integration of some intelligent optimization algorithms is a potential research direction.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2012 National Conference on Information Technology and Computer Science
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2012
ISBN
978-94-91216-39-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/citcs.2012.163How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yu Fu
AU  - Bing Wang
AU  - Shao-Hua Xu
PY  - 2012/11
DA  - 2012/11
TI  - A Hybrid Evolution Algorithm with Application Based on Chaos Genetic Algorithm and Particle Swarm Optimization
BT  - 2012 National Conference on Information Technology and Computer Science
PB  - Atlantis Press
SP  - 635
EP  - 639
SN  - 1951-6851
UR  - https://doi.org/10.2991/citcs.2012.163
DO  - https://doi.org/10.2991/citcs.2012.163
ID  - Fu2012/11
ER  -