Hybrid Biogeography-Based Optimization Algorithms

  • Yujun ZhengEmail author
  • Xueqin Lu
  • Minxia Zhang
  • Shengyong Chen


In the previous chapters, we introduce how to improve the basic BBO algorithm by using local topologies and developing new migration operators. Besides improving the intrinsic structure and operators of the algorithm, another way to improve the algorithm is combining it with other heuristic algorithms. Based on its distinctive migration mechanisms, BBO has a good local exploitation ability (Simon, IEEE Trans. Evol. Comput. 12:702–713, 2008, [10]), but its global exploration ability is relatively poor. Thus, those hybrid BBO algorithms often introduce effective global exploration mechanisms of other heuristic algorithms, so as to better balance the global and local search. This chapter describes some typical hybrid BBO algorithms.


  1. 1.
    Chakraborty P, Roy GG, Das S, Jain D, Abraham A (2009) An improved harmony search algorithm with differential mutation operator. Fundam. Inf. 95:401–426. Scholar
  2. 2.
    Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68. Scholar
  3. 3.
    Gong W, Cai Z, Ling CX (2010) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput. 15:645–665. Scholar
  4. 4.
    Ilhem B, Amitava C, Patrick S, Mohamed AN (2011) Two-stage update biogeography-based optimization using differential evolution algorithm DBBO. Comput. Oper. Res. 38:1188–1198. Scholar
  5. 5.
    Liang JJ, Qu BY, Suganthan PN (2014) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical report, Computational Intelligence Laboratory, Zhengzhou UniversityGoogle Scholar
  6. 6.
    Ma H, Simon D (2011) Blended biogeography-based optimization for constrained optimization. Eng. Appl. Artif. Intell. 24:517–525. Scholar
  7. 7.
    Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188:1567–1579. Scholar
  8. 8.
    Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. IEEE Congr. Evol. Comput. 2:1785–1791. Scholar
  9. 9.
    Shi Y, Tan Y, Coello CAC (2014) Advances in swarm intelligence. Springer, BerlinGoogle Scholar
  10. 10.
    Simon D (2008) Biogeography-based optimization. IEEE Trans. Evol. Comput. 12:702–713. Scholar
  11. 11.
    Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11:341–359. Scholar
  12. 12.
    Tan Y, Li J, Zheng Z (2014) ICSI 2014 competition on single objective optimization. Technical report, Peking UniversityGoogle Scholar
  13. 13.
    Tan Y, Li J, Zheng Z (2015) Introduction and ranking results of the ICSI 2014 competition on single objective optimization. arXiv:1501.02128
  14. 14.
    Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. Advances in swarm intelligence, vol 6145. Lecture notes in computer science. Springer, Berlin, pp 355–364. Scholar
  15. 15.
    Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2013) Hybridizing harmony search with biogeography based optimization for global numerical optimization. J. Comput. Theor. Nanosci. 10:2312–2322. Scholar
  16. 16.
    Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3:82–102. Scholar
  17. 17.
    Zhang B, Zhang M, Zheng YJ (2014) A hybrid biogeography-based optimization and fireworks algorithm. In: Proceedings of IEEE congress on evolutionary computation, pp. 3200–3206.
  18. 18.
    Zhang B, Zheng YJ, Zhang MX, Chen SY (2017) Fireworks algorithm with enhanced fireworks interaction. IEEE/ACM Trans. Comput. Biol. Bioinform. 14(1):42–55. Scholar
  19. 19.
    Zheng S, Janecek A, Tan Y (2013) Enhanced fireworks algorithm. In: Proceedings of IEEE congress on evolutionary computation, pp. 2069–2077.
  20. 20.
    Zheng YJ, Ling HF, Xue JY (2014) Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeographic barriers and differentiations. Comput. Oper. Res. 50:115–127. Scholar
  21. 21.
    Zheng YJ, Ling HF, Wu XB, Xue JY (2014) Localized biogeography-based optimization. Soft Comput. 18:2323–2334. Scholar
  22. 22.
    Zheng Y, Wu X (2014) Evaluating a hybrid DE and BBO with self adaptation on icsi 2014 benchmark problems. Advances in swarm intelligence, vol 8795. Lecture notes in computer science. Springer, Berlin, pp 422–433. Scholar
  23. 23.
    Zheng YJ, Zhang MX, Zhang B (2014) Biogeographic harmony search for emergency air transportation. Soft Comput. 20:967–977. Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. and Science Press, Beijing 2019

Authors and Affiliations

  • Yujun Zheng
    • 1
    Email author
  • Xueqin Lu
    • 2
  • Minxia Zhang
    • 2
  • Shengyong Chen
    • 2
  1. 1.Hangzhou Institute of Service EngineeringHangzhou Normal UniversityHangzhouChina
  2. 2.College of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina

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