Skip to main content
SpringerLink
Log in

Artificial bee colony algorithm with strategy and parameter adaptation for global optimization

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The artificial bee colony (ABC) algorithm has been successfully applied to solve a wide range of real-world optimization problems. However, the success of ABC in solving a specific problem crucially depends on appropriately choosing the foraging strategies and its associated parameters. In this paper, we propose a strategy and parameter self-adaptive selection ABC algorithm (SPaABC), in which both employed bees search strategies and their associated control parameter values are gradually self-adaptive by learning from their previous experiences in generating promising solutions. In order to verify the performance of our approach, SPaABC algorithm is compared to many recently related algorithms on eighteen benchmark functions. Experimental results indicate that the proposed algorithm achieves competitive performance on most test instances.

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

Similar content being viewed by others

Notes

  1. The code was based on http://mf.erciyes.edu.tr/abc.

References

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

    Article  Google Scholar 

  2. Akay B, Karaboga D (2015) A survey on the applications of artificial bee colony in signal, image, and video processing. SIViP 9(4):967–990

    Article  Google Scholar 

  3. Gao KZ et al (2015) A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert Syst Appl 42(21):7652–7663

    Article  Google Scholar 

  4. Ozturk C, Hancer E, Karaboga D (2015) Improved clustering criterion for image clustering with artificial bee colony algorithm. Pattern Anal Appl 18(3):587–599

    Article  MathSciNet  Google Scholar 

  5. Karaboga N, Kockanat S, Dogan H (2013) The parameter extraction of the thermally annealed Schottky barrier diode using the modified artificial bee colony. Appl Intell 38(3):279–288

    Article  Google Scholar 

  6. Akay B, Karaboga D (2009) Parameter tuning for the artificial bee colony algorithm. In: Computational collective intelligence. Semantic web, social networks and multiagent systems. Springer, Berlin, pp 608–619

  7. Ozturk C, Hancer E, Karaboga D (2015) A novel binary artificial bee colony algorithm based on genetic operators. Inf Sci 297:154–170

    Article  MathSciNet  Google Scholar 

  8. Wang B (2015) A novel artificial bee colony algorithm based on modified search strategy and generalized opposition-based learning. J Intell Fuzzy Syst Appl Eng Technol 28(3):1023–1037

    MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  10. Zhang X, Yuen SY (2013) Improving artificial bee colony with one-position inheritance mechanism. Memet Comput 5(3):187–211

    Article  Google Scholar 

  11. Diwold K, Aderhold A, Scheidler A et al (2011) Performance evaluation of artificial bee colony optimization and new selection schemes. Memet Comput 3(3):149–162

    Article  MATH  Google Scholar 

  12. Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173

    MathSciNet  MATH  Google Scholar 

  13. Banharnsakun A, Achalakul T, Sirinaovakul B (2011) The best-so-far selection in artificial bee colony algorithm. Appl Soft Comput 11(2):2888–2901

    Article  Google Scholar 

  14. Gao W, Liu S (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697

    Article  MATH  Google Scholar 

  15. dos Santos Coelho L, Alotto P (2011) Gaussian artificial bee colony algorithm approach applied to Loney’s solenoid benchmark problem. IEEE Trans Magn 47(5):1326–1329

    Article  Google Scholar 

  16. Karaboga D, Akay B (2009) Artificial bee colony (ABC), harmony search and bees algorithms on numerical optimization. In: Innovative production machines and systems virtual conference

  17. Gao W, Liu S, Huang L (2013) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybern 43(3):1011–1024

    Article  Google Scholar 

  18. Subotic M, Tuba M, Stanarevic N (2011) Different approaches in parallelization of the artificial bee colony algorithm. Int J Math Models Methods Appl Sci 5(4):755–762

    Google Scholar 

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

  20. Luo J, Xiao XH, Fu L et al (2012) Modified artificial bee colony algorithm based on segmental-search strategy. Control Decis 27(9):1402–1405

    Google Scholar 

  21. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417

    Article  Google Scholar 

  22. Gao W, Liu S (2011) Improved artificial bee colony algorithm for global optimization. Inf Process Lett 111(17):871–882

    Article  MathSciNet  MATH  Google Scholar 

  23. Bhattacharya P, Khan A, Sarkar SK (2014) A global routing optimization scheme based on ABC algorithm. In: Advanced computing, networking and informatics, vol 2. Springer, Berlin, pp 189–197

  24. Subotic M, Tuba M (2014) Parallelized multiple swarm artificial bee colony algorithm (MS-ABC) for global optimization. Stud Inform Control 23(1):117–126

    Article  Google Scholar 

  25. Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227–238

    Article  Google Scholar 

  26. 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 

  27. Kenndy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948

  28. Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI, Berkeley

    MATH  Google Scholar 

  29. Liu J, Zhong W, Jiao L (2007) An organizational evolutionary algorithm for numerical optimization. IEEE Trans Syst Man Cybern Part B Cybern 37(4):1052–1064

    Article  Google Scholar 

  30. Ratnaweera A, Halgamuge S, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evolut Comput 8(3):240–255

    Article  Google Scholar 

  31. Liang JJ, Qin AK, Suganthan PN et al (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3):281–295

    Article  Google Scholar 

  32. Zhan ZH, Zhang J, Li Y et al (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39(6):1362–1381

    Article  Google Scholar 

  33. Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: The 2005 IEEE Congress on evolutionary computation, vol 2. IEEE, pp 1785–1791

  34. Brest J, Greiner S, Boskovic B et al (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10(6):646–657

    Article  Google Scholar 

  35. Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13(5):945–958

    Article  Google Scholar 

  36. Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37(8):5682–5687

    Article  Google Scholar 

  37. Kang F, Li J, Ma Z (2011) Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Inf Sci 181(16):3508–3531

    Article  MathSciNet  MATH  Google Scholar 

  38. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102

    Article  Google Scholar 

  39. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: World Congress on nature and biologically inspired computing. NaBIC 2009. IEEE, pp 210–214

  40. Li X, Wang J, Yin M (2014) Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput Appl 24(6):1233–1247

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation Program of China (61572116, 61572117, 61502089), the National key Technology R&D Program of the Ministry of Science and Technology (2015BAH09F02), and the Special Fund for Fundamental Research of Central Universities of Northeastern University (N150408001, N150404009).

Author information

Authors and Affiliations

  1. Northeastern University, Shenyang, China

    Bin Zhang, Tingting Liu, Changsheng Zhang & Peng Wang

Authors
  1. Bin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tingting Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Changsheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changsheng Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, B., Liu, T., Zhang, C. et al. Artificial bee colony algorithm with strategy and parameter adaptation for global optimization. Neural Comput & Applic 28 (Suppl 1), 349–364 (2017). https://doi.org/10.1007/s00521-016-2348-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2348-y

Keywords

Search

Navigation