Skip to main content
SpringerLink
Log in

An improved optimization method based on krill herd and artificial bee colony with information exchange

  • Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

This study presents a robust optimization algorithm based on hybridization of krill herd (KH) and artificial bee colony (ABC) methods and the information exchange concept. The global optimal solutions found by the proposed hybrid KH and ABC (KHABC) algorithm are considered as a neighbor food source for onlooker bees in ABC. Thereafter, a local search is performed by the onlooker bees in order to find a better solution around the given neighbor food source. Both the methods—the KH and ABC—share the globally best solutions through the information exchange process between the krill and bees. Based on the results, the exchange process significantly improves exploration and exploitation of the hybrid method. Besides, a focused elitism scheme is introduced to enhance the performance of the developed algorithm. The validity of the KHABC method is verified using thirteen unconstrained benchmark functions, twenty-one CEC 2017 constrained real-parameter optimization problems, and ten CEC 2011 real world problems. The proposed method clearly demonstrates its ability to be a competitive optimization tool towards solving benchmark functions and real world problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Kundu S, Parhi DR (2016) Navigation of underwater robot based on dynamically adaptive harmony search algorithm. Memet Comput 8(2):125–146. doi:10.1007/s12293-016-0179-0

    Article  Google Scholar 

  2. Zhang Y, Liu J, Zhou M, Jiang Z (2016) A multi-objective memetic algorithm based on decomposition for big optimization problems. Memet Comput 8(1):45–61. doi:10.1007/s12293-015-0175-9

    Article  Google Scholar 

  3. Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. doi:10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  4. Beyer H, Schwefel H (2002) Nat Comput. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  5. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning, vol 412. Addison-Wesley, Boston

    MATH  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. doi:10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  7. Wang G-G, Deb S, Gao X-Z, Coelho LdS (2016) A new metaheuristic optimization algorithm motivated by elephant herding behavior. Int J of Bio Inspir Comput 8(6):394–409. doi:10.1504/IJBIC.2016.10002274

    Article  Google Scholar 

  8. Wang G-G (2016) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput. doi:10.1007/s12293-016-0212-3

    Article  Google Scholar 

  9. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. doi:10.1177/003754970107600201

    Article  Google Scholar 

  10. Wang G-G, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl. doi:10.1007/s00521-015-1923-y

    Article  Google Scholar 

  11. Kennedy J, Eberhart R (1995) Particle swarm optimization. Paper presented at the proceeding of the IEEE international conference on neural networks, Perth, Australia, 27 November–1 December

  12. Le MN, Ong Y-S, Jin Y, Sendhoff B (2009) Lamarckian memetic algorithms: local optimum and connectivity structure analysis. Memet Comput 1(3):175–190. doi:10.1007/s12293-009-0016-9

    Article  Google Scholar 

  13. Meuth R, Lim M-H, Ong Y-S, Wunsch DC (2009) A proposition on memes and meta-memes in computing for higher-order learning. Memet Comput 1(2):85–100. doi:10.1007/s12293-009-0011-1

    Article  Google Scholar 

  14. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845. doi:10.1016/j.cnsns.2012.05.010

    Article  MathSciNet  MATH  Google Scholar 

  15. Črepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms. ACM Comput Surv 45(3):1–33. doi:10.1145/2480741.2480752

    Article  MATH  Google Scholar 

  16. Wang G-G, Guo L, Gandomi AH, Hao G-S, Wang H (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34. doi:10.1016/j.ins.2014.02.123

  17. Wang G-G, Gandomi AH, Yang X-S, Alavi AH (2016) A new hybrid method based on krill herd and cuckoo search for global optimization tasks. Int J of Bio Inspir Comput 8(5):286–299. doi:10.1504/IJBIC.2016.10000414

    Article  Google Scholar 

  18. Mukherjee A, Mukherjee V (2015) Solution of optimal power flow using chaotic krill herd algorithm. Chaos Solitons Fractals 78:10–21. doi:10.1016/j.chaos.2015.06.020

    Article  MathSciNet  Google Scholar 

  19. Wang G-G, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363–370. doi:10.1016/j.neucom.2013.08.031

    Article  Google Scholar 

  20. Bolaji ALa, Al-Betar MA, Awadallah MA, Khader AT, Abualigah LM (2016) A comprehensive review: krill herd algorithm (KH) and its applications. Appl Soft Compt 49:437–446. doi:10.1016/j.asoc.2016.08.041

    Article  Google Scholar 

  21. Bolaji AL, Khader AT, Al-Betar MA, Awadallah MA (2014) University course timetabling using hybridized artificial bee colony with hill climbing optimizer. J Comput Sci 5(5):809–818. doi:10.1016/j.jocs.2014.04.002

    Article  Google Scholar 

  22. Kıran SM, Gündüz M (2013) A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems. Appl Soft Compt 13(4):2188–2203. doi:10.1016/j.asoc.2012.12.007

    Article  Google Scholar 

  23. Awadallah MA, Bolaji ALa, Al-Betar MA (2015) A hybrid artificial bee colony for a nurse rostering problem. Appl Soft Compt 35:726–739. doi:10.1016/j.asoc.2015.07.004

    Article  Google Scholar 

  24. Bullinaria JA, AlYahya K (2014) Artificial bee colony training of neural networks: comparison with back-propagation. Memet Comput 6(3):171–182. doi:10.1007/s12293-014-0137-7

    Article  Google Scholar 

  25. Li JQ, Pan QK, Duan PY (2016) An improved artificial bee colony algorithm for solving hybrid flexible flowshop with dynamic operation skipping. IEEE Trans Cybern 46(6):1311–1324. doi:10.1109/TCYB.2015.2444383

    Article  Google Scholar 

  26. Krüger TJ, Davidović T, Teodorović D, Šelmić M (2016) The bee colony optimization algorithm and its convergence. Int J Bio Inspir Comput 8(5):340–354. doi:10.1504/IJBIC.2016.079573

    Article  Google Scholar 

  27. Hussein WA, Sahran S, Sheikh Abdullah SNH (2017) The variants of the bees algorithm (BA): a survey. Artif Intell Rev 47(1):67–121. doi:10.1007/s10462-016-9476-8

    Article  Google Scholar 

  28. Zhang Y, Wu L (2012) Artificial bee colony for two dimensional protein folding. Adv Electr Eng Syst 1(1):19–23

    Google Scholar 

  29. Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24(3–4):853–871. doi:10.1007/s00521-012-1304-8

    Article  Google Scholar 

  30. Wang G-G, Deb S, Gandomi AH, Zhang Z, Alavi AH (2016) Chaotic cuckoo search. Soft Comput 20(9):3349–3362. doi:10.1007/s00500-015-1726-1

    Article  Google Scholar 

  31. Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2013) Hybridizing harmony search with biogeography based optimization for global numerical optimization. J Comput Theor Nanos 10(10):2318–2328. doi:10.1166/jctn.2013.3207

    Article  Google Scholar 

  32. Li Z-Y, Yi J-H, Wang G-G (2015) A new swarm intelligence approach for clustering based on krill herd with elitism strategy. Algorithms 8(4):951–964. doi:10.3390/a8040951

    Article  MathSciNet  MATH  Google Scholar 

  33. Wang G-G, Chang B, Zhang Z (2015) A multi-swarm bat algorithm for global optimization. In: 2015 IEEE congress on evolutionary computation (CEC 2015), Sendai, Japan, May 25–28, 2015. IEEE, pp 480–485. doi:10.1109/CEC.2015.7256928

  34. Wang G-G, Lu M, Zhao X-J (2016) An improved bat algorithm with variable neighborhood search for global optimization. Paper presented at the 2016 IEEE congress on evolutionary computation (IEEE CEC 2016), Vancouver, 25–29 July, 2016

  35. Wang G-G, Deb S, Gandomi AH, Alavi AH (2016) Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing 177:147–157. doi:10.1016/j.neucom.2015.11.018

  36. Das S, Suganthan P (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur Univ., Nanyang Technol. Univ., Kolkata, India

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of Jiangsu Province (No. BK20150239) and National Natural Science Foundation of China (No. 61503165).

Author information

Authors and Affiliations

  1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China

    Heqi Wang

  2. School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Jiao-Hong Yi

Authors
  1. Heqi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiao-Hong Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiao-Hong Yi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Yi, JH. An improved optimization method based on krill herd and artificial bee colony with information exchange. Memetic Comp. 10, 177–198 (2018). https://doi.org/10.1007/s12293-017-0241-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-017-0241-6

Keywords

Search

Navigation