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Soft Computing

, Volume 15, Issue 4, pp 645–665 | Cite as

DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

  • Wenyin Gong
  • Zhihua Cai
  • Charles X. Ling
Original Paper

Abstract

Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in many areas. Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solutions. In this paper, we propose a hybrid DE with BBO, namely DE/BBO, for the global numerical optimization problem. DE/BBO combines the exploration of DE with the exploitation of BBO effectively, and hence it can generate the promising candidate solutions. To verify the performance of our proposed DE/BBO, 23 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 other state-of-the-art DE approaches, DE/BBO performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate. In addition, the influence of the population size, dimensionality, different mutation schemes, and the self-adaptive control parameters of DE are also studied.

Keywords

Differential evolution Biogeography-based optimization Hybridization Global numerical optimization Exploration Exploitation 

Notes

Acknowledgments

The authors would like to thank Prof. Brest for providing the SADE code. They are also grateful to the area editor and the anonymous reviewers for their valuable comments and suggestions on this paper. This work was supported in part by the Fund for Outstanding Doctoral Dissertation of China University of Geosciences, China Scholarship Council under Grant No. 2008641008, and the National High Technology Research and Development Program of China under Grant No. 2009AA12Z117.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanPeople’s Republic of China
  2. 2.Department of Computer ScienceThe University of Western OntarioLondonCanada

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