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

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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.

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Notes

  1. Since the mutation operator of BBO is not used in our approach, we do not describe it here. Interested readers can refer to (Simon 2008a, b).

  2. The paired t-test determines whether two paired sets differ from each other in a significant way under the assumptions that the paired differences are independent and identically normally distributed (Goulden 1956).

  3. The source code of DE/EDA is available online at: http://cswww.essex.ac.uk/staff/qzhang/IntrotoResearch/HybridEDA.htm.

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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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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, People’s Republic of China

    Wenyin Gong & Zhihua Cai

  2. Department of Computer Science, The University of Western Ontario, London, Canada

    Charles X. Ling

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  1. Wenyin Gong
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  2. Zhihua Cai
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  3. Charles X. Ling
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Correspondence to Wenyin Gong.

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Gong, W., Cai, Z. & Ling, C.X. DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15, 645–665 (2010). https://doi.org/10.1007/s00500-010-0591-1

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