Skip to main content
SpringerLink
Log in

An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Artificial bee colony (ABC) algorithm developed by Karaboga is a nature inspired metaheuristic based on honey bee foraging behavior. It was successfully applied to continuous unconstrained optimization problems and later it was extended to constrained design problems as well. This paper introduces an upgraded artificial bee colony (UABC) algorithm for constrained optimization problems. Our UABC algorithm enhances fine-tuning characteristics of the modification rate parameter and employs modified scout bee phase of the ABC algorithm. This upgraded algorithm has been implemented and tested on standard engineering benchmark problems and the performance was compared to the performance of the latest Akay and Karaboga’s ABC algorithm. Our numerical results show that the proposed UABC algorithm produces better or equal best and average solutions in less evaluations in all cases.

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

Similar content being viewed by others

References

  • Akay, B., & Karaboga, D. (2010). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0393-4 (Published online).

  • Aydin, M. E. (2010), Coordinating metaheuristic agents with swarm intelligence. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0435-y (Published online).

  • Baykasoglu A., Ozbakir L., Tapkan P. (2007) Artificial bee colony algorithm and its application to generalized assignment problem. In: Chan F. T. S, Tiwari M. K. (eds) Swarm intelligence, focus on ant and particle swarm optimization. I-Tech Education and Publishing, Vienna, pp 113–144

    Google Scholar 

  • Chang, F. C., & Huang, H. C. (2010). A refactoring method for cache-efficient swarm intelligence algorithms. Information Sciences doi:10.1016/j.ins.2010.02.025 (Article in press).

  • Cheshmehgaz, H. R., Desa, M. I., & Wibowo, A. (2011). A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0584-7 (Published online).

  • Deb K. (2000) An efficient constraint-handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2–4): 311–338

    Article  Google Scholar 

  • Dorigo M., Gambardella L. M. (1997) Ant colonies for the travelling salesman problem. Biosystems 43(2): 73–81

    Article  Google Scholar 

  • Gaitonde, V. N., & Karnik, S. R. (2010). Minimizing burr size in drilling using artificial neural network (ann)-particle swarm optimization (pso) approach. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0481-5 (Published online).

  • Haddad O. B., Afshar A., Marino M. A. (2006) Honey-bees mating optimization (hbmo) algorithm: A new heuristic approach for water resources optimization. Water Resources Management 20(5): 661–680

    Article  Google Scholar 

  • Hamida, S. B., & Schoenauer, M. (2002). Aschea: New results using adaptive segregational constraint handling. In Proceedings of the congress on evolutionary computation 2002 (CEC’2002) (pp. 884–889). IEEE Service Center.

  • He S., Prempain E., Wu Q. (2004) An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization 36(5): 585–605

    Article  Google Scholar 

  • Jovanovic R., Tuba M. (2011) An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem. Applied Soft Computing 11(8): 5360–5366

    Article  Google Scholar 

  • Jun-Qing Li Q. K. P, Xie S. X. (2010) A hybrid variable neighborhood search algorithm for solving multi-objective flexible job shop problems. Computer Science and Information Systems 7(4): 907–930

    Article  Google Scholar 

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

  • Karaboga D., Akay B. (2011) A modified artificial bee colony (abc) algorithm for constrained optimization problems. Applied Soft Computing 11(3): 3021–3031

    Article  Google Scholar 

  • Karaboga, D., & Basturk, B. (2007a). Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In LNAI 4529: IFSA’07 proceedings of the 12th international fuzzy systems association world congress on foundations of fuzzy logic and soft computing (pp. 789–798). Springer.

  • Karaboga D., Basturk B. (2007) A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm. Journal of Global Optimization 39(3): 459–471

    Article  Google Scholar 

  • Karaboga D., Basturk B. (2008) On the performance of artificial bee colony (abc) algorithm. Applied Soft Computing 8(1): 687–697

    Article  Google Scholar 

  • Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE international conference on neural networks (pp. 1942–1948). Piscataway, NJ: IEEE Service Center.

  • Koziel S., Michalewicz Z. (1999) Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evolutionary Computation 7(1): 19–44

    Article  Google Scholar 

  • Lu, M., & Romanowski, R. (2011). Multi-contextual ant colony optimization of intermediate dynamic job shop problems. The International Journal of Advanced Manufacturing Technology 1–15. doi:10.1007/s00170-011-3634-6 (Published online).

  • Mezura-Montes, E., & Coello Coello, C. A. (2005). Useful infeasible solutions in engineering optimization with evolutionary algorithms. In MICAI 2005: Advances in artificial intelligence of lecture notes in computer science (pp. 652–662). Springer.

  • Mezura-Montes E., Miranda-Varela C. A. (2005) A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation 9(1): 1–17

    Article  Google Scholar 

  • Mezura-Montes E., Coello Coello E. M., Gomez-Ramon R. (2010) Differential evolution in constrained numerical optimization: An empirical study. Information Sciences 180(22): 4223–4262

    Article  Google Scholar 

  • Pan D., Liu Z. (2011) An improved particle swarm optimization algorithm. In: Deng H., Miao D., Wang F. L., Lei J. (eds) Emerging research in artificial intelligence and computational intelligence, communications in computer and information science. Springer, Berlin, pp 550–556

    Chapter  Google Scholar 

  • Parsopoulos, K., & Vrahatis, M. (2005). Unified particle swarm optimization for solving constrained engineering optimization problems. In ICNC 2005: Advances in natural computation, volume 3612/2005 of LCNS (pp. 582–591). Springer.

  • Pasandideh, S. H. R., Niaki, S. T. A., & Hajipour, V. (2011). A multi-objective facility location model with batch arrivals: Two parameter-tuned meta-heuristic algorithms. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0592-7 (Published online).

  • Pham, D. T., Kog, E., Ghanbarzadeh, A., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In IPROMS 2006 proceeding 2nd international virtual conference on intelligent production machines and systems (pp. 454–459). Elsevier.

  • Puranik P., Bajaj P., Abraham A., Palsodkar P., Deshmukh A. (2011) Human perception-based color image segmentation using comprehensive learning particle swarm optimization. Journal of Information Hiding and Multimedia Signal Processing 2(3): 227–235

    Article  Google Scholar 

  • Ray T., Liew K. (2003) Society and civilization: An optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation 7(4): 386–396

    Article  Google Scholar 

  • Runarsson T. P., Yao X. (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on evolutionary computation 4(3): 284–294

    Article  Google Scholar 

  • Runarsson T. P., Yao X. (2005) Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man, and Cybernetics Part C-Applications and Reviews 35(2): 233–243

    Article  Google Scholar 

  • Srinivasan, D., & Seow, T. (2003). Particle swarm inspired evolutionary algorithm (ps-ea) for multiobjective optimization problems. In: The 2003 congress on evolutionary computation—CEC 2003 (pp. 2292–2297). IEEE Press.

  • Wang Y., Zhang B., Chen Y. (2011) Robust airfoil optimization based on improved particle swarm optimization method. Applied Mathematics and Mechanics (English Edition) 32(10): 1245–1254

    Article  Google Scholar 

  • Yang, X. S. (2005). Engineering optimizations via nature-inspired virtual bee algorithms. In Artificial intelligence and knowledge engineering applications: A bioinspired approach, LNCS (Vol. 3562, pp. 317–323). Springer.

  • Zavala, A. E. M., Hernandez, A., & Diharce, E. R. V. (2005). Constrained optimization via particle evolutionary swarm optimization algorithm (peso). In GECCO ’05 Proceedings of the 2005 conference on genetic and evolutionary computation (pp. 209–216). ACM Press.

Download references

Author information

Authors and Affiliations

  1. Faculty of Mathematics, University of Belgrade, Studentski trg 16, 11000, Belgrade, Serbia

    Ivona Brajevic

  2. Faculty of Computer Science, Megatrend University Belgrade, Bulevar umetnosti 29, 11070, Belgrade, Serbia

    Milan Tuba

Authors
  1. Ivona Brajevic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Milan Tuba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Milan Tuba.

Additional information

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

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brajevic, I., Tuba, M. An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. J Intell Manuf 24, 729–740 (2013). https://doi.org/10.1007/s10845-011-0621-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-011-0621-6

Keywords

Search

Navigation