Proceedings of the 2nd International Conference on Science and Social Research (ICSSR 2013)

An Improved Hybridizing Biogeography-Based Optimization with Differential Evolution for Global Numerical Optimization

Authors
Siling Feng, Qingxin Zhu, Xiujun Gong, Sheng Zhong
Corresponding Author
Siling Feng
Available Online July 2013.
DOI
https://doi.org/10.2991/icssr-13.2013.67How to use a DOI?
Keywords
Biogeography-Based Optimization; Differential evolution; Global numerical optimization
Abstract
Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solution. Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. In this paper, we applied an improved hybridization of BBO with DE approach, namely BBO/DE/GEN, for the global numerical optimization problems. BBO/DE/GEN combines the exploitation of BBO with the exploration of DE effectively and the migration operators of BBO were modified based on number of iteration to improve performance. And hence it can generate the promising candidate solutions. To verify the performance of our proposed BBO/DE/GEN, 6 benchmark functions with a wide range of dimensions and diverse complexities are employed. Experimental results indicate that our approach is effective and efficient. Compared with BBO and BBO/DE approaches, BBO/DE/GEN performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate.
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Proceedings
Part of series
Advances in Intelligent Systems Research
Publication Date
July 2013
ISBN
978-90-78677-75-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/icssr-13.2013.67How 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  - Siling Feng
AU  - Qingxin Zhu
AU  - Xiujun Gong
AU  - Sheng Zhong
PY  - 2013/07
DA  - 2013/07
TI  - An Improved Hybridizing Biogeography-Based Optimization with Differential Evolution for Global Numerical Optimization
PB  - Atlantis Press
SP  - 304
EP  - 307
SN  - 1951-6851
UR  - https://doi.org/10.2991/icssr-13.2013.67
DO  - https://doi.org/10.2991/icssr-13.2013.67
ID  - Feng2013/07
ER  -