Advertisement

Time-based information sharing approach for employed foragers of artificial bee colony algorithm

  • Selcuk Aslan
Methodologies and Application
  • 63 Downloads

Abstract

Collective foraging and information sharing behaviors of honey bees have lead to emerge different swarm intelligence-based optimization techniques. Within these swarm intelligence-based optimization techniques, Artificial Bee Colony (ABC) algorithm has a special position due to its less control parameters, robust, phase-divided and easily implementable structures. Although standard workflow of ABC algorithm is capable of producing optimal or near optimal solutions for numerous problems, there are still some intelligent operations that are not directly modeled for the ABC algorithm in order to maintain the reduced complexity of the implementation and small number of control parameters. In this study, ABC algorithm is tried to be powered with a more realistic dancing approach called time-based information sharing, for short tb, model. The proposed model is integrated into the workflow of the standard ABC algorithm and its well-known variants. Experimental studies carried out on both classical and bound constrained single-objective CEC2015 benchmark functions showed that the proposed model in which the dancing durations of the employed bees are determined by the fitness values of the memorized sources significantly improved the performance of the standard and other variants of the ABC algorithm.

Keywords

ABC algorithm Employed bees Time-based information sharing 

Notes

Compliance with ethical standards

Conflicts of interest

The author declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Akay B, Karaboga D (2010) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014.  https://doi.org/10.1007/s10845-010-0393-4 Google Scholar
  2. Akay B, Karaboga D (2015) A survey on the applications of artificial bee colony in signal, image, and video processing. Signal, Image Video Process 9(4):967–990.  https://doi.org/10.1007/s11760-015-0758-4 Google Scholar
  3. Awadallah MA, Al-Betar MA, Bolaji AL, Alsukhni EM, Al-Zoubi H (2018) Natural selection methods for artificial bee colony with new versions of onlooker bee. Soft Comput pp 1–40Google Scholar
  4. Badem H, Basturk A, Caliskan A, Yuksel ME (2017) A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited-memory bfgs optimization algorithms. Neurocomputing 266:506–526Google Scholar
  5. Batbat T, Ozturk C (2016) Protein structure prediction with discrete artificial bee colony algorithm. Int J Inf Technol 9(3):263Google Scholar
  6. Bolaji AL, Khader AT, Al-Betar MA, Awadallah MA (2013) Artificial bee colony algorithm, its variants and applications: a survey. J Theor Appl Inf Technol 47(2):434–459Google Scholar
  7. Celik M, Koylu F, Karaboga D (2016) Coabcminer: an algorithm for cooperative rule classification system based on artificial bee colony. Int J Artif Intell Tools 25(01):1–50.  https://doi.org/10.1142/S0218213015500281 Google Scholar
  8. Chen Q, Liu B, Zhang Q, Liang J, Suganthan P, Qu B (2015) Problem definitions and evaluation criteria for cec 2015 special session on bound constrained single-objective computationally expensive numerical optimization. In: Evolutionary computation (CEC), 2015 IEEE congress on, pp. 84–88.  https://doi.org/10.1109/CEC.2011.5949602
  9. Duan Hb, Xu Cf, Xing ZH (2010) A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems. Int J Neural Syst 20(01):39–50Google Scholar
  10. Gao W, Liu S, Huang L (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753MathSciNetzbMATHGoogle Scholar
  11. Gao WF, Huang LL, Liu SY, Dai C (2015) Artificial bee colony algorithm based on information learning. IEEE Trans Cybern 45(12):2827–2839Google Scholar
  12. Gao Wf, Liu Sy, Huang Ll (2013) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybern 43(3):1011–1024Google Scholar
  13. Harfouchi F, Habbi H, Ozturk C, Karaboga D (2017) Modified multiple search cooperative foraging strategy for improved artificial bee colony optimization with robustness analysis. Soft Comput 22(19):6371–6394Google Scholar
  14. Bansal JC, Sharma H, Jadon SS (2013) Artificial bee colony algorithm: a survey. Int J Adv Intell 5:123–159Google Scholar
  15. Kang F, Li J, Li H, Ma Z, Xu Q (2010) An improved artificial bee colony algorithm. In: 2010 2nd international workshop on intelligent systems and applications (ISA), pp 1–4. IEEE (2010)Google Scholar
  16. Kang F, Li J, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87(13):861–870Google Scholar
  17. Karaboga D, Akay B (2007) Artificial bee colony algorithm for training feed forward neural networks. In: IEEE 15th signal processing and communication applications conference, pp 1–4. IEEEGoogle Scholar
  18. Karaboga D, Akay B (2009) A survey: algorithms simulating bee swarm intelligence. Artif Intell Rev 31:68–85Google Scholar
  19. Karaboga D, Aslan S (2016) A discrete artificial bee colony algorithm for detecting transcription factor binding sites in dna sequences. Genet Mol Res 15(02):1–11.  https://doi.org/10.4238/gmr.15028645 Google Scholar
  20. 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.  https://doi.org/10.1007/s10898-007-9149-x MathSciNetzbMATHGoogle Scholar
  21. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (abc) algorithm. Appl Soft Comput 8(1):687–697.  https://doi.org/10.1016/j.asoc.2007.05.007 Google Scholar
  22. Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital iir filters. J Frankl Inst 364(04):328–348MathSciNetzbMATHGoogle Scholar
  23. Luo J, Wang Q, Xiao X (2013) A modified artificial bee colony algorithm based on converge-onlookers approach for global optimization. Appl Math Comput 219(20):10253–10262MathSciNetzbMATHGoogle Scholar
  24. Malathy P, Shunmugalatha A (2017) Application of swarm based intelligent computing algorithms for dynamic evaluation of maximum loadability of transmission network. J Comput Sci 21:201–222Google Scholar
  25. Mernik M, Liu SH, Karaboga D, repinek M (2015) On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation. Inf Sci 291:115–127.  https://doi.org/10.1016/j.ins.2014.08.040 MathSciNetzbMATHGoogle Scholar
  26. Mini S, Udgata S.K, Sabat S.K (2010) Sensor deployment in 3-d terrain using artificial bee colony algorithm. In: International conference on swarm, evolutionary, and memetic computing, pp 424–431. SpringerGoogle Scholar
  27. Ozturk C, Aslan S (2016) A new artificial bee colony algorithm to solve the multiple sequence alignment problem. Int J Data Min Bioinf 14(4):332–353Google Scholar
  28. Ozturk C, Karaboga D (2011) Hybrid artificial bee colony algorithm for neural network training. In: 2011 IEEE congress on evolutionary computation (CEC), pp 84–88. IEEEGoogle Scholar
  29. Ramesh R, Gomathy C, Vaishali D et al (2017) Bio inspired optimization for universal spatial image steganalysis. J Comput Sci 21:182–188Google Scholar
  30. Tran DC, Wu Z, Wang Z, Deng C (2015) A novel hybrid data clustering algorithm based on artificial bee colony algorithm and k-means. Chin J Electron 24(4):694–701Google Scholar
  31. TSai PW, Pan JS, Liao BY, Chu SC (2009) Enhanced artificial bee colony optimization. Int J Innov Comput, Inf Control 5(12):5081–5092Google Scholar
  32. Udgata S.K, Sabat S.L, Mini S (2009) Sensor deployment in irregular terrain using artificial bee colony algorithm. In: World congress on nature and biologically inspired computing, 2009. NaBIC, pp 1309–1314Google Scholar
  33. Xiang WL, An MQ (2013) An efficient and robust artificial bee colony algorithm for numerical optimization. Comput Oper Res 40(5):1256–1265MathSciNetzbMATHGoogle Scholar
  34. Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ondokuz Mayis UniversitySamsunTurkey

Personalised recommendations