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Modified Biogeography-Based Optimization with Local Search Mechanism

Author

Listed:
  • Quanxi Feng
  • Sanyang Liu
  • Qunying Wu
  • GuoQiang Tang
  • Haomin Zhang
  • Huazhou Chen

Abstract

Biogeography-based optimization (BBO) is a new effective population optimization algorithm based on the biogeography theory with inherently insufficient exploration capability. To address this limitation, we proposed a modified BBO with local search mechanism (denoted as MLBBO). In MLBBO, a modified migration operator is integrated into BBO, which can adopt more information from other habitats, to enhance the exploration ability. Then, a local search mechanism is used in BBO to supplement with modified migration operator. Extensive experimental tests are conducted on 27 benchmark functions to show the effectiveness of the proposed algorithm. The simulation results have been compared with original BBO, DE, improved BBO algorithms, and other evolutionary algorithms. Finally, the performance of the modified migration operator and local search mechanism are also discussed.

Suggested Citation

  • Quanxi Feng & Sanyang Liu & Qunying Wu & GuoQiang Tang & Haomin Zhang & Huazhou Chen, 2013. "Modified Biogeography-Based Optimization with Local Search Mechanism," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-24, December.
    • Handle: RePEc:hin:jnljam:960524
    DOI: 10.1155/2013/960524
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    Cited by:

    1. Mona A. S. Ali & Fathimathul Rajeena P. P. & Diaa Salama Abd Elminaam, 2022. "A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem," Mathematics, MDPI, vol. 10(15), pages 1-34, July.

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