Soft Computing

, Volume 21, Issue 22, pp 6605–6632 | Cite as

Novel migration operators of biogeography-based optimization and Markov analysis

  • Weian Guo
  • Lei Wang
  • Chenyong Si
  • Yongwei Zhang
  • Hongjun Tian
  • Junjie Hu
Methodologies and Application


Biogeography-based optimization (BBO) is a nature-inspired optimization algorithm and has been developed in both theory and practice. In canonical BBO, migration operator is crucial to affect algorithm’s performance. In migration operator, a good solution has a large probability to be selected as an immigrant, while a poor solution has a large probability to be selected as an emigrant. The features in an emigrant will be completely replaced by the features in the corresponding immigrant. Hence, the migration operator in canonical BBO causes a significant deterioration of population diversity, and therefore, the algorithm’s performance worsens. In this paper, we propose three novel migration operators to enhance the exploration ability of BBO. To present a mathematical proof, Markov analysis is conducted to confirm the advantages of the proposed migration operators over existing ones. In addition, a number of benchmark tests are carried out to empirically assess the performance of the proposed migration operators, on both low-dimensional and high-dimensional numerical optimization problems. The comparison results demonstrate that the proposed migration operators are feasible and effective to enhance BBO’s performance.


Biogeography-based optimization Nature-inspired optimization algorithm Population diversity Migration operator Markov analysis 



We much appreciate the help from the editors and the reviewers. They give us many useful comments to improve the quality of this paper. This work is sponsored by the National Natural Science Foundation of China under Grant No. 61503287, the Fundamental Research Funds for the Central Universities (Young Talents Program in Tongji University), Program for New Century Excellent Talents in University of Ministry of Education of China, Ph.D. Programs Foundation of Ministry of Education of China (20100072110038), Shanghai University Young Teachers’ Training Program (ZZslg15087), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Sino-German College of Applied SciencesTongji UniversityShanghaiChina
  2. 2.Department of Electronics and InformationTongji UniversityShanghaiChina
  3. 3.Shanghai-Humburg CollegeUniversity of Shanghai for Science and TechnologyShanghaiChina
  4. 4.College of Electronics and InformationJiangsu University of Science and TechnologyZhenjiangChina
  5. 5.Department of Electrical Engineering, Center for Electric Power and EnergyTechnical University of DenmarkCopenhagenDenmark

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