Advertisement

An Enhanced Butterfly Optimization Algorithm for Function Optimization

  • Sushmita SharmaEmail author
  • Apu Kumar Saha
  • Sukanta Nama
Conference paper
  • 10 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)

Abstract

Butterfly optimization algorithm (BOA), a newly developed optimization algorithm, has captured the researcher’s interest in solving real-life and engineering optimization problems due to its simplicity, efficiency and robustness. On the other hand, symbiosis organisms search algorithm has proved its efficiency in solving complex real-life optimization problem. In this paper, a new combinatorial optimization approach has been introduced combining the explorative advantages of BOA with the exploitative advantage of SOS to eliminate weak exploitation ability of BOA and weak exploration ability of SOS. Efficiency and robustness of the algorithm have been evaluated using twenty-five classical benchmark problems and compared with some state-of-the-art optimization algorithms. The compared results show that the proposed method to be superior and reliable than the other algorithms.

Keywords

Metaheuristics Hybrid algorithms Butterfly optimization algorithm Symbiosis organisms search SymBOA Benchmark functions 

References

  1. 1.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Xu, Y., Fan, P., Yuan, L.: A simple and efficient artificial bee colony algorithm. Math. Prob. Eng. 14, (2013).  https://doi.org/10.1155/2013/526315
  3. 3.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  4. 4.
    Cheng, M.Y., Prayogo, D.: Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98–112 (2014)CrossRefGoogle Scholar
  5. 5.
    Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput. 23, 715–734 (2019)CrossRefGoogle Scholar
  6. 6.
    Civicioglu, P.: Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219, 8121–8144 (2013)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Marco, D., Mauro, B., Thomas, S.: Ant colony optimization. Comput. Intell. Mag. 1, 28–39 (2006)CrossRefGoogle Scholar
  8. 8.
    Nama, S., Saha, A.K., Ghosh, S.: A hybrid symbiosis organisms search algorithm and its application to real world problems. Memetic Comput. 9.  https://doi.org/10.1007/s12293-016-0194-1
  9. 9.
    Nama, S., Saha, A.K., Ghosh, S.: Improved symbiotic organisms search algorithm for solving unconstrained function optimization. Decis. Sci. Lett. 5, 361–380 (2016).  https://doi.org/10.5267/j.dsl.2016.2.004CrossRefGoogle Scholar
  10. 10.
    Do, D., Lee, J.: A modified symbiotic organisms search (MSOS) algorithm for optimization of pin-jointed structures. Appl. Soft Comput. 61, 683–699 (2017)CrossRefGoogle Scholar
  11. 11.
    Wu, H., Zhou, Y.-Q., Luo, Q.: Hybrid symbiotic organisms search algorithm for solving 0–1 knapsack problem. Int. J. Bio-Inspired Comput. 12 (2018).  https://doi.org/10.1504/IJBIC.2018.093334
  12. 12.
    Arora, S., Singh, S., Yetilmezsoy, K.: A modified butterfly optimization algorithm for mechanical design optimization problems. J. Brazilian Soc. Mech. Sci. Eng. 40(1), 21 (2018)CrossRefGoogle Scholar
  13. 13.
    Arora, S., Singh, S.: An improved butterfly optimization algorithm for global optimization 8, 711–717 (2016)Google Scholar
  14. 14.
    Sharma, S., Saha, A.K.: m-MBOA: a novel butterfly optimization algorithm enhanced with mutualism scheme. Soft Comput. (2019).  https://doi.org/10.1007/s00500-019-04234-6CrossRefGoogle Scholar
  15. 15.
    Sharma, T.K., Pant, M.: Opposition-based learning embedded shuffled Frog-Leaping Algorithm. Soft Comput.Theor. Appl. 583, 853–861 (2017)Google Scholar
  16. 16.
    Jain, S., Swami, V., Kumar, S.: An improved Spider Monkey optimization algorithm. Soft Comput.Theor. Appl. 583(1), 73–81 (2017)Google Scholar
  17. 17.
    Sheth, P.D., Jagdeo, S.M., Umbarkar, A.J.: Teaching-learning-based optimization on Hadoop. Soft Comput.Theor. Appl. 583(1), 251–263 (2017)Google Scholar
  18. 18.
    Zwislocki, J.J.: Sensory Neuroscience: Four Laws of Psychophysics. Springer Science & Business Media, Berlin (2009)CrossRefGoogle Scholar
  19. 19.
    Stevens, S.S.: Psychophysics. Transaction Publishers, Piscataway (1975)Google Scholar
  20. 20.
    Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl. Based Syst. 96, 120–133 (2016)CrossRefGoogle Scholar
  21. 21.
    Rao, V., Jaya, R.: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Computat. 7, 19–34 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sushmita Sharma
    • 1
    Email author
  • Apu Kumar Saha
    • 1
  • Sukanta Nama
    • 1
  1. 1.Department of MathematicsNational Institute of Technology AgartalaAgartalaIndia

Personalised recommendations