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Chaotic Brain Storm Optimization Algorithm

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Swarm intelligence algorithms are stochastic optimization algorithms that are very successfully used for hard optimization problems. Brain storm optimization is a recent swarm intelligence algorithm that has been proven successful in many applications but is still not researched enough. Many swarm intelligence algorithm have been recently improved by introduction of chaotic maps that better than random sequences contributed to search quality. In this paper we propose an improvement of the brain storm optimization algorithm by introducing chaotic maps. Two one-dimensional chaotic maps were incorporated into the original brain storm optimization algorithm. The proposed algorithms were tested on 15 standard benchmark functions from CEC 2013 and compared to the original brain storm optimization algorithm and particle swarm optimization. Our proposed chaos based methods obtained better results where for this set of benchmark functions circle maps were superior.

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References

  1. Cao, Z., Shi, Y., Rong, X., Liu, B., Du, Z., Yang, B.: Random grouping brain storm optimization algorithm with a new dynamically changing step size. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9140, pp. 357–364. Springer, Cham (2015). doi:10.1007/978-3-319-20466-6_38

    Chapter  Google Scholar 

  2. Chen, J., Cheng, S., Chen, Y., Xie, Y., Shi, Y.: Enhanced brain storm optimization algorithm for wireless sensor networks deployment. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9140, pp. 373–381. Springer, Cham (2015). doi:10.1007/978-3-319-20466-6_40

    Chapter  Google Scholar 

  3. Chen, J., Wang, J., Cheng, S., Shi, Y.: Brain storm optimization with agglomerative hierarchical clustering analysis. In: Tan, Y., Shi, Y., Li, L. (eds.) ICSI 2016. LNCS, vol. 9713, pp. 115–122. Springer, Cham (2016). doi:10.1007/978-3-319-41009-8_12

    Google Scholar 

  4. Gandomi, A., Yang, X.S., Talatahari, S., Alavi, A.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  5. Gandomi, A.H., Yang, X.S.: Chaotic bat algorithm. J. Comput. Sci. 5(2), 224–232 (2014)

    Article  MathSciNet  Google Scholar 

  6. Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  7. Gong, C.: Chaotic adaptive fireworks algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2016. LNCS, vol. 9712, pp. 515–525. Springer, Cham (2016). doi:10.1007/978-3-319-41000-5_51

    Google Scholar 

  8. Mitic, M., Vukovic, N., Petrovic, M., Miljkovic, Z.: Chaotic fruit fly optimization algorithm. Knowl.-Based Syst. 89, 446–458 (2015)

    Article  Google Scholar 

  9. Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21515-5_36

    Chapter  Google Scholar 

  10. Strumberger, I., Bacanin, N., Tuba, M.: Enhanced firefly algorithm for constrained numerical optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2120–2127. IEEE (2017)

    Google Scholar 

  11. Strumberger, I., Bacanin, N., Tuba, M.: Hybridized krill herd algorithm for large-scale optimization problems. In: 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 473–478. IEEE (2017)

    Google Scholar 

  12. Sun, C., Duan, H., Shi, Y.: Optimal satellite formation reconfiguration based on closed-loop brain storm optimization. IEEE Comput. Intell. Mag. 8(4), 39–51 (2013)

    Article  Google Scholar 

  13. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13495-1_44

    Chapter  Google Scholar 

  14. Tuba, E., Alihodzic, A., Tuba, M.: Multilevel image thresholding using elephant herding optimization algorithm. In: 14th International Conference on Engineering of Modern Electric Systems (EMES), pp. 240–243. IEEE (2017)

    Google Scholar 

  15. Tuba, E., Mrkela, L., Tuba, M.: Support vector machine parameter tuning using firefly algorithm. In: 26th International Conference Radioelektronika, pp. 413–418. IEEE (2016)

    Google Scholar 

  16. Tuba, E., Tuba, M., Beko, M.: Support vector machine parameters optimization by enhanced fireworks algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2016. LNCS, vol. 9712, pp. 526–534. Springer, Cham (2016). doi:10.1007/978-3-319-41000-5_52

    Google Scholar 

  17. Tuba, E., Tuba, M., Dolicanin, E.: Adjusted fireworks algorithm applied to retinal image registration. Stud. Inform. Control 26(1), 33–42 (2017)

    Article  Google Scholar 

  18. Tuba, M., Bacanin, N.: Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems. Neurocomputing 143, 197–207 (2014)

    Article  Google Scholar 

  19. Tuba, M., Jovanovic, R.: Improved ACO algorithm with pheromone correction strategy for the traveling salesman problem. Int. J. Comput. Commun. Control 8(3), 477–485 (2013)

    Article  Google Scholar 

  20. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04944-6_14

    Chapter  Google Scholar 

  21. Yuan, X., Zhao, J., Yang, Y., Wang, Y.: Hybrid parallel chaos optimization algorithm with harmony search algorithm. Appl. Soft Comput. 17, 12–22 (2014)

    Article  Google Scholar 

  22. Zambrano-Bigiarini, M., Clerc, M., Rojas, R.: Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2337–2344. IEEE (2013)

    Google Scholar 

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Acknowledgment

This research is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.

Author information

Authors and Affiliations

  1. Faculty of Mathematics, University of Belgrade, Belgrade, Serbia

    Eva Tuba

  2. Department of Technical Sciences, State University of Novi Pazar, Novi Pazar, Serbia

    Edin Dolicanin

  3. Graduate School of Computer Science, John Naisbitt University, Belgrade, Serbia

    Milan Tuba

Authors
  1. Eva Tuba
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  2. Edin Dolicanin
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  3. Milan Tuba
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Corresponding author

Correspondence to Milan Tuba .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Tuba, E., Dolicanin, E., Tuba, M. (2017). Chaotic Brain Storm Optimization Algorithm. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_60

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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