GA and ACO-based Hybrid Approach for Continuous Optimization
Zhiqiang Chen, Ronglong Wang
Available Online August 2015.
- https://doi.org/10.2991/msam-15.2015.81How to use a DOI?
- component; hybird; GA; ACO; continuous Optimization
- 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.
- 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 BT - 2015 International Conference on Modeling, Simulation and Applied Mathematics PB - Atlantis Press 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 -