A Comprehensive Analysis of the Bat Algorithm

  • Yury Zorin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)


Optimization is one of the most challenging problems that has received considerable attention over the last decade. The bio-inspired evolutionary optimization algorithms due to their robustness, simplicity and efficiency are widely used to solve complex optimization problems. The Bat algorithm is one of the most recent one from this category. Given that the original Bat algorithm is vulnerable to local optimum and unsatisfactory calculation accuracy, the paper presents detailed analysis of its main stages and a measure of their influence on the algorithm performance. In particular, the global best solution acceptance condition, the way a new solution is generated by random flight and the local search procedure implementation have been studied. The ways to overcome the original algorithm’s flaws have been suggested. Their effectiveness has been proved by numerous computational experiments.


Optimization problem Metaheuristic algorithm Bat algorithm Lévy flight 


  1. 1.
    Yang, X.S.: A new metaheuristic bat-inspired algorithm. Nat. Inspired Coop. Strat. Optim. 284, 65–74 (2010)zbMATHGoogle Scholar
  2. 2.
    Altringham, J.D.: Bats: Biology and Behaviour. Oxford University Press, New York (1996). p. 379Google Scholar
  3. 3.
    Virtual Library of Simulation Experiments: Test Functions and Datasets.
  4. 4.
    Farahani, S.M., Abshouri, A.A., Nasiri, B., Meybodi, M.R.: A Gaussian firefly algorith. Int. J. Mach. Learn. Comput. 1(5), 448–453 (2011)CrossRefGoogle Scholar
  5. 5.
    dos Santos Coelho, L., Mariani, V.C.: Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst. Appl. 34, 1905–1913 (2008)CrossRefGoogle Scholar
  6. 6.
    Dhal, K.G., Quraishi, I., Das, S.: A chaotic Lévy flight approach in bat and firefly algorithm for gray level image enhancement. Int. J. Image Graph. Signal Process. (IJIGSP) 7(7), 69–76 (2015). Scholar
  7. 7.
    Abdel-Raouf, O., Abdel-Baset, M., El-Henawy, I.: An improved chaotic bat algorithm for solving integer programming problems. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 6(8), 18–24 (2014). Scholar
  8. 8.
    Reynolds, A.M., Rhodes, C.J.: The Levy flight paradigm: random search patterns and mechanisms. Ecology 90, 877–887 (2009)CrossRefGoogle Scholar
  9. 9.
    Zorin, Y.: A metaheuristic algorithm for multimodal functions optimization. In: Proceedings of the International Scientific Conference Intellectual information analysis IIA 2015, Kyiv, Ukraine on 20–22 May, pp. 88–92 (2015)Google Scholar
  10. 10.
    Zorin, Y.: An improved cuckoo search algorithm. In: System Analysis and Information Technology SAIT 2016, Kyiv, Ukraine on 30 May–2 June, pp. 48–49 (2016)Google Scholar
  11. 11.
    Roy, S., Biswas, S., Chaudhuri, S.S.: Nature-inspired swarm intelligence and its applications. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 6(12), 55–65 (2014). Scholar
  12. 12.
    Abdel-Raouf, O., Abdel-Baset, M., El-henawy, I.: Chaotic firefly algorithm for solving definite integral. Int. J. Inf. Techn. Comput. Sci. (IJITCS) 6(6), 19–24 (2014). Scholar
  13. 13.
    Roy, S., Chaudhuri, S.S.: Cuckoo search algorithm using Lèvy flight: a review. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 5(12), 10–15 (2013). Scholar
  14. 14.
    Fister Jr., I., Fister, D., Yang, X.-S.: A hybrid bat algorithm. Elektrotehnitski Vestnik 80(1–2), 1–7 (2013)Google Scholar
  15. 15.
    Yılmaz1, S., Kucuksille, E.U., Cengiz, Y.: Modified bat algorithm. Elektronika ir Electrotechnika 20(2), 36–43 (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Technical University of Ukraine ‘‘Igor Sikorsky Kyiv Polytechnic Institute’’KyivUkraine

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