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A Comprehensive Analysis of the Bat Algorithm

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Advances in Computer Science for Engineering and Education (ICCSEEA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 754))

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Abstract

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.

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References

  1. Yang, X.S.: A new metaheuristic bat-inspired algorithm. Nat. Inspired Coop. Strat. Optim. 284, 65–74 (2010)

    MATH  Google Scholar 

  2. Altringham, J.D.: Bats: Biology and Behaviour. Oxford University Press, New York (1996). p. 379

    Google Scholar 

  3. Virtual Library of Simulation Experiments: Test Functions and Datasets. http://www.sfu.ca/~ssurjano/index.html

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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). https://doi.org/10.5815/ijigsp.2015.07.08

    Article  Google Scholar 

  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). https://doi.org/10.5815/ijmecs.2014.08.03

    Article  Google Scholar 

  8. Reynolds, A.M., Rhodes, C.J.: The Levy flight paradigm: random search patterns and mechanisms. Ecology 90, 877–887 (2009)

    Article  Google Scholar 

  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. 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. 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). https://doi.org/10.5815/ijmecs.2014.12.08

    Article  Google Scholar 

  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). https://doi.org/10.5815/ijitcs.2014.06.03

    Article  Google Scholar 

  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). https://doi.org/10.5815/ijmecs.2013.12.02

    Article  Google Scholar 

  14. Fister Jr., I., Fister, D., Yang, X.-S.: A hybrid bat algorithm. Elektrotehnitski Vestnik 80(1–2), 1–7 (2013)

    Google Scholar 

  15. Yılmaz1, S., Kucuksille, E.U., Cengiz, Y.: Modified bat algorithm. Elektronika ir Electrotechnika 20(2), 36–43 (2014)

    Google Scholar 

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Authors and Affiliations

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

    Yury Zorin

Authors
  1. Yury Zorin
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Corresponding author

Correspondence to Yury Zorin .

Editor information

Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China

    Zhengbing Hu

  2. Mechanical Engineering Research Institute, Russian Academy of Sciences, Moscow, Russia

    Sergey Petoukhov

  3. Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”, Kiev, Ukraine

    Ivan Dychka

  4. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Ft. Lauderdale, Florida, USA

    Matthew He

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Zorin, Y. (2019). A Comprehensive Analysis of the Bat Algorithm. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-91008-6_28

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