Advertisement

Modified and Hybridized Monarch Butterfly Algorithms for Multi-Objective Optimization

  • Ivana Strumberger
  • Eva Tuba
  • Nebojsa Bacanin
  • Marko Beko
  • Milan TubaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 923)

Abstract

This paper presents two improved versions of the monarch butterfly optimization algorithm adopted for solving multi-objective optimization problems. Monarch butterfly optimization is a relatively new swarm intelligence metaheuristic that proved to be robust and efficient method when dealing with NP hard problems. However, in the original monarch butterfly approach some deficiencies were noticed and we addressed these deficiencies by developing one modified, and one hybridized version of the original monarch butterfly algorithm. In the experimental section of this paper we show comparative analysis between the original, and improved versions of monarch butterfly algorithm. According to experimental results, hybridized monarch butterfly approach outperformed all other metaheuristics included in comparative analysis.

Keywords

Monarch butterfly optimization Swarm intelligence NP hardness Multi-objective Metaheuristics 

Notes

Acknowledgements

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

References

  1. 1.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar
  2. 2.
    Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Advances in Swarm Intelligence, LNCS, vol. 6145, pp. 355–364 (2010)Google Scholar
  3. 3.
    Wang, G.-G., Deb, S., Coelho, L.D.S.: Elephant herding optimization. In: Proceedings of the 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), pp. 1–5, December 2015Google Scholar
  4. 4.
    Tuba, E., Tuba, M., Simian, D., Jovanovic, R.: JPEG quantization table optimization by guided fireworks algorithm, vol. 10256, pp. 294–307. Springer International Publishing, Cham (2017)Google Scholar
  5. 5.
    Bacanin, N., Tuba, M.: Fireworks algorithm applied to constrained portfolio optimization problem. In: Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1242–1249, May 2015Google Scholar
  6. 6.
    Tuba, E., Tuba, M., Beko, M.: Node localization in ad hoc wireless sensor networks using fireworks algorithm. In: Proceedings of the 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 223–229, September 2016Google Scholar
  7. 7.
    Tuba, E., Tuba, M., Dolicanin, E.: Adjusted fireworks algorithm applied to retinal image registration. Stud. Inform. Control 26(1), 33–42 (2017)CrossRefGoogle Scholar
  8. 8.
    Tuba, E., Stanimirovic, Z.: Elephant herding optimization algorithm for support vector machine parameters tuning. In: Proceedings of the 2017 International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–5, June 2017Google Scholar
  9. 9.
    Alihodzic, A., Tuba, E., Capor-Hrosik, R., Dolicanin, E., Tuba, M.: Unmanned aerial vehicle path planning problem by adjusted elephant herding optimization. In: 25th Telecommunication Forum (TELFOR), pp. 1–4. IEEE (2017)Google Scholar
  10. 10.
    Tuba, E., Alihodzic, A., Tuba, M.: Multilevel image thresholding using elephant herding optimization algorithm. In: Proceedings of 14th International Conference on the Engineering of Modern Electric Systems (EMES), pp. 240–243, June 2017Google Scholar
  11. 11.
    Strumberger, I., Bacanin, N., Beko, M., Tomic, S., Tuba, M.: Static drone placement by elephant herding optimization algorithm. In: Proceedings of the 24th Telecommunications Forum (TELFOR), November 2017Google Scholar
  12. 12.
    Wang, G.-G., Deb, S., Cui, Z.: Monarch butterfly optimization. Neural Comput. Appl. 1–20 (2015)Google Scholar
  13. 13.
    Breed, G.A., Severns, P.M., Edwards, A.M.: Apparent power-law distributions in animal movements can arise from intraspecific interactions. J. Roy. Soc. Interface 12 (2015)Google Scholar
  14. 14.
    Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Stochastic Algorithms: Foundations and Applications, LNCS, vol. 5792, pp. 169–178 (2009)Google Scholar
  15. 15.
    Bacanin, N., Tuba, M.: Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint. Sci. World J. 2014, 16 (2014). Special issue Computational Intelligence and Metaheuristic Algorithms with Applications, Article ID 721521CrossRefGoogle Scholar
  16. 16.
    Tuba, M., Bacanin, N.: Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems. Neurocomputing 143, 197–207 (2014)CrossRefGoogle Scholar
  17. 17.
    Yang, X.-S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)CrossRefGoogle Scholar
  18. 18.
    Yang, X.-S.: Multiobjective firefly algorithm for continuous optimization. Eng. Comput. 29, 175–184 (2012)CrossRefGoogle Scholar
  19. 19.
    Ma, L., Hu, K., Zhu, Y., Chen, H.: Cooperative artificial bee colony algorithm for multi-objective RFID network planning. J. Netw. Comput. Appl. 42, 143–162 (2014)CrossRefGoogle Scholar
  20. 20.
    Deb, K.: Running performance metrics for evolutionary multi-objective optimization. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 2002), pp. 13–20 (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ivana Strumberger
    • 1
  • Eva Tuba
    • 1
  • Nebojsa Bacanin
    • 1
  • Marko Beko
    • 2
    • 3
  • Milan Tuba
    • 1
    Email author
  1. 1.Singidunum UniversityBelgradeSerbia
  2. 2.COPELABS, Universidade LusófonaLisbonPortugal
  3. 3.CTS/UNINOVALisbonPortugal

Personalised recommendations