Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics

GA and ACO-based Hybrid Approach for Continuous Optimization

Authors
Zhiqiang Chen, Ronglong Wang
Corresponding Author
Zhiqiang Chen
Available Online August 2015.
DOI
https://doi.org/10.2991/msam-15.2015.81How to use a DOI?
Keywords
component; hybird; GA; ACO; continuous Optimization
Abstract
This paper presents an hybrid algorithm based on genetic algorithm and ant colony optimization for continuous optimization, which combines the global exploration ability of the former with the local exploiting ability of the later. The proposed algorithm is evaluated on several benchmark functions. The simulation results show that the proposed algorithm performs quite well and outperforms classical ant colony optimization and genetic algorithm for continuous optimization, which efficiently balances two contradictory aspects of its performance: exploration and exploitation.
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Proceedings
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2015
ISBN
978-94-6252-104-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-15.2015.81How 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  - Zhiqiang Chen
AU  - Ronglong Wang
PY  - 2015/08
DA  - 2015/08
TI  - GA and ACO-based Hybrid Approach for Continuous Optimization
PB  - Atlantis Press
SP  - 358
EP  - 361
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-15.2015.81
DO  - https://doi.org/10.2991/msam-15.2015.81
ID  - Chen2015/08
ER  -