A Modified Biogeography Based Optimization

  • Pushpa Farswan
  • Jagdish Chand Bansal
  • Kusum Deep
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 382)


Biogeography based optimization (BBO) has recently gain interest of researchers due to its efficiency and existence of very few parameters. The BBO is inspired by geographical distribution of species within islands. However, BBO has shown its wide applicability to various engineering optimization problems, the original version of BBO sometimes does not perform up to the mark. Poor balance of exploration and exploitation is the reason behind it. Migration, mutation and elitism are three operators in BBO. Migration operator is responsible for the information sharing among candidate solutions (islands). In this way, the migration operator plays an important role for the design of an efficient BBO. This paper proposes a new migration operator in BBO. The so obtained BBO shows better diversified search process and hence finds solutions more accurately with high convergence rate. The BBO with new migration operator is tested over 20 test problems. Results are compared with that of original BBO and Blended BBO. The comparison which is based on efficiency, reliability and accuracy shows that proposed migration operator is competitive to the present one.


Biogeography based optimization Blended BBO Migration operator 


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  1. 1.
    Bäck, T., Fogel, D.B., Michalewicz, Z.: Evolutionary computation 1: Basic algorithms and operators, vol. 1. CRC Press (2000)Google Scholar
  2. 2.
    Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Computing 6(1), 31–47 (2014)CrossRefGoogle Scholar
  3. 3.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic programming: an introduction, vol. 1. Morgan Kaufmann, San Francisco (1998)zbMATHCrossRefGoogle Scholar
  4. 4.
    Davis, L., et al.: Handbook of genetic algorithms, vol. 115. Van Nostrand Reinhold, New York (1991)Google Scholar
  5. 5.
    Dorigo, M., Stützle, T.: Ant colony optimization (2004)Google Scholar
  6. 6.
    Du, D., Simon, D., Ergezer, M.: Biogeography-based optimization combined with evolutionary strategy and immigration refusal. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 997–1002. IEEE (2009)Google Scholar
  7. 7.
    Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm intelligence. Elsevier (2001)Google Scholar
  8. 8.
    Farswan, P., Bansal, J.C.: Migration in biogeography-based optimization. In: Das, K.N., Deep, K., Pant, M., Bansal, J.C., Nagar, (eds.) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol. 336, pp. 389–401. Springer, India (2015) Google Scholar
  9. 9.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  10. 10.
    Gomez, F.J., Miikkulainen, R.: Robust non-linear control through neuroevolution. Computer Science Department, University of Texas at Austin (2003)Google Scholar
  11. 11.
    Gong, W., Cai, Z., Ling, C.X.: De/bbo: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Computing 15(4), 645–665 (2010)CrossRefGoogle Scholar
  12. 12.
    Gong, W., Cai, Z., Ling, C.X., Li, H.: A real-coded biogeography-based optimization with mutation. Applied Mathematics and Computation 216(9), 2749–2758 (2010)zbMATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department (2005)Google Scholar
  14. 14.
    Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2010)Google Scholar
  15. 15.
    Lohokare, M.R., Pattnaik, S.S., Panigrahi, B.K., Das, S.: Accelerated biogeography-based optimization with neighborhood search for optimization. Applied Soft Computing 13(5), 2318–2342 (2013)CrossRefGoogle Scholar
  16. 16.
    Ma, H.-P., Ruan, X.-Y., Pan, Z.-X.: Handling multiple objectives with biogeography-based optimization. International Journal of Automation and Computing 9(1), 30–36 (2012)CrossRefGoogle Scholar
  17. 17.
    Ma, H., Simon, D.: Blended biogeography-based optimization for constrained optimization. Engineering Applications of Artificial Intelligence 24(3), 517–525 (2011)CrossRefGoogle Scholar
  18. 18.
    Simon, D.: Biogeography-based optimization. IEEE Transactions on Evolutionary Computation 12(6), 702–713 (2008)CrossRefGoogle Scholar
  19. 19.
    Simon, D., Omran, M.G.H., Clerc, M.: Linearized biogeography-based optimization with re-initialization and local search. Information Sciences 267, 140–157 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)zbMATHMathSciNetCrossRefGoogle Scholar
  21. 21.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Pushpa Farswan
    • 1
  • Jagdish Chand Bansal
    • 1
  • Kusum Deep
    • 2
  1. 1.South Asian UniversityNew DelhiIndia
  2. 2.Indian Institute of Technology RoorkeeRoorkeeIndia

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