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A Novel Hybrid Artificial Bee Colony with Monarch Butterfly Optimization for Global Optimization Problems

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Modeling, Simulation, and Optimization

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

This article introduces a novel hybrid approach between two of the metaheuristic algorithms to solve global optimization problems. The proposed hybrid algorithm uses the butterfly adjusting operator in monarch butterfly optimization (MBO) algorithm as a mutation operator to replace the employee phase of the artificial bee colony (ABC) algorithm. The novel hybrid ABC/MBO (HAM) algorithm addresses the issues of trapping in local optimal solutions, slow convergence, and low precision by improving the balance between the characteristics of exploration and exploitation. The proposed HAM algorithm is validated on eight benchmark functions and is compared with ABC and MBO algorithms. The experimental results show that the HAM algorithm is clearly superior to both the standard ABC and MBO algorithms.

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References

  1. Sörensen K, Glover FW (2013) Metaheuristics. In: Encyclopedia of operations research and management science. Springer, New York, pp 960–970

    Google Scholar 

  2. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, New York

    Google Scholar 

  3. Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  4. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26(1):29–41

    Article  Google Scholar 

  5. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department

    Google Scholar 

  6. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  7. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Frome

    Google Scholar 

  8. Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: Nature & biologically inspired computing, 2009. NaBIC 2009. World congress on, IEEE

    Google Scholar 

  9. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  10. Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Applic 24(7–8):1867–1877

    Article  Google Scholar 

  11. Meng X et al (2014) A new bio-inspired algorithm: chicken swarm optimization. In: Advances in swarm intelligence. Springer, New York, pp 86–94

    Google Scholar 

  12. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  14. Wang G-G, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Applic 28(3):1–20

    Google Scholar 

  15. Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 65–74

    Chapter  Google Scholar 

  16. Kirkpatrick S, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  17. Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Metaheuristic application in structures and infrastructures. Elsevier, Waltham, Mass

    Google Scholar 

  18. Črepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv (CSUR) 45(3):35

    MATH  Google Scholar 

  19. Ghanem, Waheed Ali HM, Jantan A (2016) Novel multi-objective artificial bee Colony optimization for wrapper based feature selection in intrusion detection. Int J Adv Soft Comput Appls 8(1):70–81

    Google Scholar 

  20. Karaboga D, Ozturk C (2009) Neural networks training by artificial bee colony algorithm on pattern classification. Neural Network World 19(3):279

    Google Scholar 

  21. Ghanem WAHM, Jantan A (2014) Using hybrid artificial bee colony algorithm and particle swarm optimization for training feed-forward neural networks. J Theoret Appl Inf Technol 3:67

    Google Scholar 

  22. Bolaji ALA, khader AT, Al-Betar MA, Awadallah MA (2013) Artificial bee colony algorithm, its variants and applications: a survey. J Theoret Appl Inf Technol 47(2):434–459

    Google Scholar 

Download references

Acknowledgments

This work has been funded by Universiti Sains Malaysia, APEX (308/AIPS/ 415401), and also supported by the Fundamental Research Grant Scheme (FRGS) 203/PKOMP/6711426].

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Authors and Affiliations

  1. School of Computer Science, Universiti Sains Malaysia, Gelugor, Penang, Malaysia

    Waheed Ali H. M. Ghanem & Aman Jantan

  2. Faculty of Education-Saber, University of Aden, Aden, Yemen

    Waheed Ali H. M. Ghanem

  3. Faculty of Engineering, University of Aden, Aden, Yemen

    Waheed Ali H. M. Ghanem

Authors
  1. Waheed Ali H. M. Ghanem
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  2. Aman Jantan
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Corresponding author

Correspondence to Waheed Ali H. M. Ghanem .

Editor information

Editors and Affiliations

  1. Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Tronoh, Malaysia

    Pandian Vasant

  2. Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Nuevo León, Mexico

    Igor Litvinchev

  3. Panamerican University, Mexico

    José Antonio Marmolejo-Saucedo

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Ghanem, W.A.H.M., Jantan, A. (2018). A Novel Hybrid Artificial Bee Colony with Monarch Butterfly Optimization for Global Optimization Problems. In: Vasant, P., Litvinchev, I., Marmolejo-Saucedo, J. (eds) Modeling, Simulation, and Optimization . EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-70542-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-70542-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70541-5

  • Online ISBN: 978-3-319-70542-2

  • eBook Packages: EngineeringEngineering (R0)

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