Skip to main content
Log in

Shuffled artificial bee colony algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

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.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • 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 22:1159–1179

    Article  Google Scholar 

  • Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37:5682–5687

    Article  Google Scholar 

  • 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 29:379–385

    Article  Google Scholar 

  • 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):4785–4790

    Article  Google Scholar 

  • Babaeizadeh S, Ahmad R (2016) An improved artificial bee colony algorithm for constrained optimization. Res J Appl Sci 11(1):14–22

    Google Scholar 

  • Barton R (1990) Chaos and fractals. Math Teach 83:524–529

    Google Scholar 

  • Brajevic I (2015) Crossover-based artificial bee colony algorithm for constrained optimization problems. Neural Comput Appl 26:1587–1601

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

  • 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):4676–4694

    Article  Google Scholar 

  • Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338

    Article  MATH  Google Scholar 

  • Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, Cambridge

    MATH  Google Scholar 

  • Edgar TF, Himmelblau DM, Lasdon L (1998) Optimization of chemical processes, 2nd edn. Mcgraw-Hill, New York

    Google Scholar 

  • Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154

    Article  MathSciNet  Google Scholar 

  • 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, Australia

  • Fister I, Perc M, Kamal SM (2015a) A review of chaos-based firefly algorithms. Appl Math Comput 252:155–165

    MathSciNet  MATH  Google Scholar 

  • 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 3–50

  • Floudas CA, Pardalos PM (1990) A collection of test problems for constrained global optimization algorithms. Lecture notes in computer science, vol 455. Springer, Berlin

  • Goldberg DE (1989) Genetic algorithms in search. Optimization and machine learning, Addison-Wesley, Boston

    MATH  Google Scholar 

  • Hansen (2006) Compilation of results on the 2005 CEC benchmark function set. May 4, 2006. http://www.ntu.edu.sg/home/epnsugan/index_files/CEC-05/compareresults.pdf

  • Kang F, Li J, Li H (2013a) Artificial bee colony algorithm and pattern search hybridized for global optimization. Appl Soft Comput 13(4):1781–1791

    Article  Google Scholar 

  • 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):207–223

    Article  MathSciNet  Google Scholar 

  • 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

    MathSciNet  Google Scholar 

  • 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

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Erciyes University, Technical Report-TR06, Kayseri, Turkey

  • Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012) Artificial bee colony programming for symbolic regression. Inf Sci 209(20):1–15

    Article  Google Scholar 

  • Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  • 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 789–798

  • Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization. In Proceedings of IFSA 2007. LNAI, vol 4529, pp 789–798

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE international conference neural networks 4:1942–1948

    Article  Google Scholar 

  • Kıran MS, Fındık O (2015) A directed artificial bee colony algorithm. Appl Soft Comput 26:454–462

    Article  Google Scholar 

  • 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. http://www.ntu.edu.sg/home/EPNSugan

  • Li X, Yin M (2014) Self-adaptive constrained artificial bee colony for constrained numerical optimization. Neural Comput Appl. 24(3–4):723–734

    Article  Google Scholar 

  • 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):10943–10973

    MathSciNet  MATH  Google Scholar 

  • 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 25–32

    Google Scholar 

  • 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 231–238

  • Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67

    Article  Google Scholar 

  • Problem Definitions and Evaluation Criteria for the CEC (2006) Special session on constrained real-parameter optimization. Nanyang Technological University, Singapore

    Google Scholar 

  • 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, USA

  • Sharma TK, Pant M (2013) Enhancing the food locations in an artificial bee colony algorithm. Soft Comput 17(3):1939–1965

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Subotic M (2011) Artificial bee colony algorithm with multiple onlookers for constrained optimization problems. In: Proceedings of the European computing conference, pp 251–256

  • Taherdangkoo M (2014) Skull removal in MR images using a modified artificial bee colony optimization algorithm. Technol Health Care 22(5):775–784

  • Xu Y, Fan P, Yuan L (2013) A simple and efficient artificial bee colony algorithm. Math Probl Eng 2013:1–9

    Google Scholar 

  • 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 210–214

  • 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 90:219–229

    Article  Google Scholar 

  • 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 209–216

  • Zhang X, Fong KF, Yuen SY (2013) A novel artificial bee colony algorithm for HVAC optimization problems. HVAC&R Res 19(6):715–731

    Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tarun Kumar Sharma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, T.K., Pant, M. Shuffled artificial bee colony algorithm. Soft Comput 21, 6085–6104 (2017). https://doi.org/10.1007/s00500-016-2166-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2166-2

Keywords

Navigation