GA and ACO-based Hybrid Approach for Continuous Optimization

DOI

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.

Copyright

© 2015, 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  - Zhiqiang Chen
AU  - Ronglong Wang
PY  - 2015/08
DA  - 2015/08
TI  - GA and ACO-based Hybrid Approach for Continuous Optimization
BT  - Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics
PB  - Atlantis Press
SP  - 358
EP  - 361
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-15.2015.81
DO  - 10.2991/msam-15.2015.81
ID  - Chen2015/08
ER  -