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

DOI

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.

Copyright

© 2013, 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  - 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
BT  - Proceedings of the 2nd International Conference on Science and Social Research (ICSSR 2013)
PB  - Atlantis Press
SP  - 304
EP  - 307
SN  - 1951-6851
UR  - https://doi.org/10.2991/icssr-13.2013.67
DO  - 10.2991/icssr-13.2013.67
ID  - Feng2013/07
ER  -