A Complex-Valued Encoding Satin Bowerbird Optimization Algorithm for Global Optimization

  • Sen Zhang
  • Yongquan ZhouEmail author
  • Qifang Luo
  • Mohamed Abdel-Baset
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)


The real-valued satin bowerbird optimization (SBO) is a novel bio-inspired algorithm which imitates the ‘male-attracts-the-female for breeding’ principle of the specialized stick structure mechanism of satin birds. SBO has achieved success in congestion management, accurate software development effort estimation. In this paper, a complex-valued encoding satin bowerbird optimization algorithm (CSBO) is proposed aiming to enhance the global exploration ability. The idea of complex-valued coding and finds the optimal one by updating the real and imaginary parts value. With Complex-valued coding increase the diversity of the population, and enhance the global exploration ability of the basic SBO algorithm. The proposed CSBO optimization algorithm is compared against SBO and other state-of-art optimization algorithms using 20 benchmark functions. Simulation results show that the proposed CSBO can significantly improve the convergence accuracy and convergence speed of the original algorithm.


Complex-valued encoding Satin bowerbird optimization Benchmark functions Motor parameter identification 



This work is supported by National Science Foundation of China under Grants No. 61563008, 61463007, and by Project of Guangxi Natural Science Foundation under Grant No. 2016GXNSFAA380264.


  1. 1.
    Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3, 95–99 (1988)CrossRefGoogle Scholar
  2. 2.
    Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12, 702–713 (2008)CrossRefGoogle Scholar
  4. 4.
    Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of machine learning, pp. 760–766. Springer, Boston (2011). Scholar
  5. 5.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1, 28–39 (2006)CrossRefGoogle Scholar
  6. 6.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010). Scholar
  8. 8.
    Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature Biologically Inspired Computing, NaBIC 2009. IEEE (2009)Google Scholar
  9. 9.
    Moosavi, S.H.S., Bardsiri, V.K.: Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng. Appl. Artif. Intell. 60, 1–15 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sen Zhang
    • 1
  • Yongquan Zhou
    • 1
    • 2
    Email author
  • Qifang Luo
    • 1
    • 2
  • Mohamed Abdel-Baset
    • 3
  1. 1.College of Information Science and EngineeringGuangxi University for NationalitiesNanningChina
  2. 2.Key Laboratory of Guangxi High Schools Complex System and Computational IntelligenceNanningChina
  3. 3.Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt

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