Skip to main content

Teaching-Learning-Based Artificial Bee Colony

  • Conference paper
  • First Online:
Book cover Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10941))

Included in the following conference series:

Abstract

This paper proposes a new hybrid metaheuristic algorithm called teaching-learning artificial bee colony (TLABC) for function optimization. TLABC combines the exploitation of teaching learning based optimization (TLBO) with the exploration of artificial bee colony (ABC) effectively, by employing three hybrid search phases, namely teaching-based employed bee phase, learning-based onlooker bee phase, and generalized oppositional scout bee phase. The performance of TLABC is evaluated on 30 complex benchmark functions from CEC2014, and experimental results show that TLABC exhibits better results compared with previous TLBO and ABC algorithms.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rao, R.V., Savsani, V.J., Vakharia, D.: Teachinglearning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43, 303–315 (2011)

    Article  Google Scholar 

  2. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  3. Rao, R., Savsani, V., Vakharia, D.: Teachinglearning-based optimization: an optimization method for continuous non-linear large scale problems. Inf. Sci. 183, 1–15 (2012)

    Article  Google Scholar 

  4. Zou, F., Wang, L., Hei, X., Chen, D., Yang, D.: Teachinglearning-based optimization with dynamic group strategy for global optimization. Inf. Sci. 273, 112–131 (2014)

    Article  Google Scholar 

  5. Chen, X., Yu, K., Du, W., Zhao, W., Liu, G.: Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy 99, 170–180 (2016)

    Article  Google Scholar 

  6. Yu, K., Wang, X., Wang, Z.: Constrained optimization based on improved teaching-learning-based optimization algorithm. Inf. Sci. 352, 61–78 (2016)

    Article  Google Scholar 

  7. Oliva, D., Cuevas, E., Pajares, G.: Parameter identification of solar cells using artificial bee colony optimization. Energy 72, 93–102 (2014)

    Article  Google Scholar 

  8. Xiang, Y., Peng, Y., Zhong, Y., Chen, Z., Lu, X., Zhong, X.: A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization. Comput. Optim. Appl. 57, 493–516 (2014)

    Article  MathSciNet  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  11. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 4–31 (2011)

    Article  Google Scholar 

  12. Zhou, X., Wu, Z., Wang, H., Rahnamayan, S.: Gaussian bare-bones artificial bee colony algorithm. Soft. Comput. 20(3), 907–924 (2016)

    Article  Google Scholar 

  13. Wang, H., Wu, Z., Rahnamayan, S., Liu, Y., Ventresca, M.: Enhancing particle swarm optimization using generalized opposition-based learning. Inf. Sci. 181, 4699–4714 (2011)

    Article  MathSciNet  Google Scholar 

  14. Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University, Singapore, Computational Intelligence Laboratory (2013)

    Google Scholar 

  15. Wu, Z.-S., Fu, W.-P., Xue, R.: Nonlinear inertia weighted teaching-learning-based optimization for solving global optimization problem. Comput. Intell. Neurosci., 87 (2015)

    Google Scholar 

  16. Zou, F., Wang, L., Hei, X., Chen, D.: Teachinglearning-based optimization with learning experience of other learners and its application. Appl. Soft Comput. 37, 725–736 (2015)

    Article  Google Scholar 

  17. Kazimipour, B., Omidvar, M.N., Li, X., Qin, A.K.: A novel hybridization of opposition-based learning and cooperative co-evolutionary for large-scale optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2833–2840. IEEE (2014)

    Google Scholar 

  18. Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192, 120–142 (2012)

    Article  Google Scholar 

  19. Garca, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the CEC2005 special session on real parameter optimization. J. Heuristics 15, 617–644 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by Natural Science Foundation of Jiangsu Province (Grant No. BK 20160540), China Postdoctoral Science Foundation (Grant No. 2016M591783), and National Natural Science Foundation of China (Grant No. 61703268).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xu Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, X., Xu, B. (2018). Teaching-Learning-Based Artificial Bee Colony. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93815-8_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93814-1

  • Online ISBN: 978-3-319-93815-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics