Novel migration operators of biogeography-based optimization and Markov analysis

Weian Guo, Lei Wang, Chenyong Si, Yongwei Zhang, Hongjun Tian, Junjie Hu

Research output: Contribution to journalJournal articleResearchpeer-review


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.
Original languageEnglish
JournalSoft Computing
Issue number22
Pages (from-to)6605-6632
Publication statusPublished - 2016


  • Software
  • Geometry and Topology
  • Theoretical Computer Science
  • Biogeography-based optimization
  • Markov analysis
  • Migration operator
  • Nature-inspired optimization algorithm
  • Population diversity
  • Algorithms
  • Benchmarking
  • Ecology
  • Heuristic algorithms
  • Mathematical operators
  • Biogeography-based optimizations
  • Optimization algorithms
  • Optimization

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