Soft Computing

, Volume 21, Issue 20, pp 6085–6104 | Cite as

Shuffled artificial bee colony algorithm

Methodologies and Application


In this study, we have introduced a hybrid version of artificial bee colony (ABC) and shuffled frog-leaping algorithm (SFLA). The hybrid version is a two-phase modification process. In the first phase to increase the global convergence, the initial population is produced using randomly generated and chaotic system, and then in the second phase to balance two antagonist factors, i.e., exploration and exploitation capabilities, population is portioned into two groups (superior and inferior) based on their fitness values. ABC is applied to the first group, whereas SFLA is applied to the second group of population. The proposed version is named as Shuffled-ABC. The proposal is implemented and tested on constrained benchmark consulted from CEC 2006 and five chemical engineering problems where constraints are handled using penalty function methods. The simulated results illustrate the efficacy of the proposal.


Computational intelligence Optimization Artificial bee colony Shuffled frog-leaping algorithm Chemical engineering problems 



The authors are thankful to the Editor-in-Chief and anonymous referees for their valuable comments and suggestions.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Adjiman CS, Androulakis IP, Floudas CA (1998) A global optimization method, alphaBB, for general twice-differentiable constrained NLPs: II–implementation and computational results. Comput Chem Eng 22:1159–1179CrossRefGoogle Scholar
  2. Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37:5682–5687CrossRefGoogle Scholar
  3. Al-Salamah M (2015) Constrained binary artificial bee colony to minimize the makespan for single machine batch processing with non-identical job sizes. Appl Soft Comput 29:379–385CrossRefGoogle Scholar
  4. Alvarado-Iniesta A, Garcia-Alcaraz JL, Rodriguez-Borbon MI, Maldonado A (2013) Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm. Expert Syst Appl 40(12):4785–4790CrossRefGoogle Scholar
  5. Babaeizadeh S, Ahmad R (2016) An improved artificial bee colony algorithm for constrained optimization. Res J Appl Sci 11(1):14–22Google Scholar
  6. Barton R (1990) Chaos and fractals. Math Teach 83:524–529Google Scholar
  7. Brajevic I (2015) Crossover-based artificial bee colony algorithm for constrained optimization problems. Neural Comput Appl 26:1587–1601CrossRefGoogle Scholar
  8. Chidambaram C, Lopes HS (2010) An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching. Int J Nat Comput Res IJNCR 1(2):54–70. doi: 10.4018/jncr.2010040104 CrossRefGoogle Scholar
  9. Črepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):1–33. doi: 10.1145/2480741.2480752
  10. Das S, Biswas S, Kundu S (2013) Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization. Appl Soft Comput 13(12):4676–4694CrossRefGoogle Scholar
  11. Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338CrossRefMATHGoogle Scholar
  12. Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, CambridgeMATHGoogle Scholar
  13. Edgar TF, Himmelblau DM, Lasdon L (1998) Optimization of chemical processes, 2nd edn. Mcgraw-Hill, New YorkGoogle Scholar
  14. Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154MathSciNetCrossRefGoogle Scholar
  15. Fister I, Fister I Jr, Brest J, Zumer V (2012) Memetic articial bee colony algorithm for large-scale global optimization. In: Proceedings of IEEE CEC—2012, Brisbane, AustraliaGoogle Scholar
  16. Fister I, Perc M, Kamal SM (2015a) A review of chaos-based firefly algorithms. Appl Math Comput 252:155–165MathSciNetMATHGoogle Scholar
  17. Fister I, Strnad D, Yang X-S, Fister I Jr (2015b) Adaptation and hybridization in nature-inspired algorithms. In: Adaptation and Hybridization in Computational Intelligence. Springer, pp 3–50Google Scholar
  18. Floudas CA, Pardalos PM (1990) A collection of test problems for constrained global optimization algorithms. Lecture notes in computer science, vol 455. Springer, BerlinGoogle Scholar
  19. Goldberg DE (1989) Genetic algorithms in search. Optimization and machine learning, Addison-Wesley, BostonMATHGoogle Scholar
  20. Hansen (2006) Compilation of results on the 2005 CEC benchmark function set. May 4, 2006.
  21. Kang F, Li J, Li H (2013a) Artificial bee colony algorithm and pattern search hybridized for global optimization. Appl Soft Comput 13(4):1781–1791CrossRefGoogle Scholar
  22. Kang F, Li J, Ma Z (2013b) An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis. Eng Optim 45(2):207–223MathSciNetCrossRefGoogle Scholar
  23. Kang F, Xu Q, Li J (2016) Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl Math Model. doi: 10.1016/j.apm.2016.01.050 MathSciNetGoogle Scholar
  24. Kang F, Li J (2015) Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. J Comput Civ Eng. doi: 10.1061/(ASCE)CP.1943-5487.0000514, 04015040
  25. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Erciyes University, Technical Report-TR06, Kayseri, TurkeyGoogle Scholar
  26. Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012) Artificial bee colony programming for symbolic regression. Inf Sci 209(20):1–15CrossRefGoogle Scholar
  27. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57CrossRefGoogle Scholar
  28. Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Foundations of fuzzy logic and soft computing, 12th International Fuzzy Systems Association, World Congress, IFSA 2007 Lecture notes in artificial intelligence, vol 4529, pp 789–798Google Scholar
  29. Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization. In Proceedings of IFSA 2007. LNAI, vol 4529, pp 789–798Google Scholar
  30. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE international conference neural networks 4:1942–1948CrossRefGoogle Scholar
  31. Kıran MS, Fındık O (2015) A directed artificial bee colony algorithm. Appl Soft Comput 26:454–462CrossRefGoogle Scholar
  32. Liang JJ, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan PN, Coello CAC, Deb K (2006) Problem definitions and evaluation criteria for the CEC special session on constrained real-parameter optimization, Technical Report, Nanyang Technological University. Singapore.
  33. Li X, Yin M (2014) Self-adaptive constrained artificial bee colony for constrained numerical optimization. Neural Comput Appl. 24(3–4):723–734CrossRefGoogle Scholar
  34. Mezura-Montes E, Cetina-Domı’nguez O (2012) Empirical analysis of a modified artificial bee colony for constrained numerical optimization. Appl Math Comput 218(22):10943–10973MathSciNetMATHGoogle Scholar
  35. Mezura-Montes E, Veåazquez-Reyes J, Coello Coello CA (2006) Modified differential evolution for constrained optimization. In Proceedings of IEEE Congress on Evolutionary Computation, Canada, pp 25–32Google Scholar
  36. Munoz-Zavala AE, Hernandez-Aguirre A, Villa-Diharce ER, Botello-Rionda S (2006) PESO+ for constrained optimization. In: Proceedings of IEEE congress on evolutionary computation Canada, pp 231–238Google Scholar
  37. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67CrossRefGoogle Scholar
  38. Problem Definitions and Evaluation Criteria for the CEC (2006) Special session on constrained real-parameter optimization. Nanyang Technological University, SingaporeGoogle Scholar
  39. Sharma TK, Pant M, Neri F (2014) Changing factor based food sources in artificial bee colony. In Proceedings of IEEE symposium on swarm intelligence (SIS), 1–7, (2014) Orlando. Florida, USAGoogle Scholar
  40. Sharma TK, Pant M (2013) Enhancing the food locations in an artificial bee colony algorithm. Soft Comput 17(3):1939–1965CrossRefGoogle Scholar
  41. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713CrossRefGoogle Scholar
  42. Subotic M (2011) Artificial bee colony algorithm with multiple onlookers for constrained optimization problems. In: Proceedings of the European computing conference, pp 251–256Google Scholar
  43. Taherdangkoo M (2014) Skull removal in MR images using a modified artificial bee colony optimization algorithm. Technol Health Care 22(5):775–784Google Scholar
  44. Xu Y, Fan P, Yuan L (2013) A simple and efficient artificial bee colony algorithm. Math Probl Eng 2013:1–9Google Scholar
  45. Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: Nature & biologically inspired computing, 2009. NaBIC 2009. World Congress on. IEEE, Coimbatore, pp 210–214Google Scholar
  46. Yang D, Liu Y, Li S, Li X, Ma L (2015) Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm. Mech Mach Theory 90:219–229CrossRefGoogle Scholar
  47. Zavala AEM, Aguirre AH, Diharce ERV (2005) Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In: Proceedings of the 2005 conference on genetic and evolutionary computation (GECCO’05), pp 209–216Google Scholar
  48. Zhang X, Fong KF, Yuen SY (2013) A novel artificial bee colony algorithm for HVAC optimization problems. HVAC&R Res 19(6):715–731Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Amity University RajasthanJaipurIndia
  2. 2.Indian Institute of Technology RoorkeeRoorkeeIndia

Personalised recommendations