Skip to main content
SpringerLink
Log in

Artificial Bee Colony Based on Adaptive Search Strategies and Elite Selection Mechanism

  • Conference paper
  • First Online:
International Conference on Neural Computing for Advanced Applications (NCAA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1869))

Included in the following conference series:

  • 340 Accesses

Abstract

In the field of optimization algorithms, artificial bee colony algorithm (ABC) shows strong search ability on many optimization problems. However, ABC still has a few shortcomings. It exhibits weak exploitation and slow convergence. In the late search stage, the original probability selection for onlooker bees may not work. Due to the above deficiencies, a modified ABC using adaptive search strategies and elite selection mechanism (namely ASESABC) is presented. Firstly, a strategy pool is created using three different search strategies. A tolerance-based strategy selection method is used to select a sound search strategy at each iteration. Then, to choose better solutions for further search, an elite selection means is utilized in the stage of onlooker bees. To examine the capability of ASESABC, 22 classical benchmark functions are tested. Results show ASESABC surpasses five other ABCs according to the quality of solutions.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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. Arora, S., Kaur, R.: An escalated convergent firefly algorithm. J. King Saud Univ. Comput. Inf. Sci. 34, 308–315 (2018)

    Google Scholar 

  2. Bajer, D., Zoric, B.: An effective refined artificial bee colony algorithm for numerical optimisation. Inf. Sci.: Int. J. 504, 221–275 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  3. Cui, L., Kai, Z., Li, G., Fu, X., Jian, L.: Modified Gbest-guided artificial bee colony algorithm with new probability model. Soft. Comput. 22(2), 1–27 (2018)

    Google Scholar 

  4. Cui, L., et al.: A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation. Inf. Sci. 367–368, 1012–1044 (2016)

    Article  Google Scholar 

  5. Cui, L., et al.: A ranking-based adaptive artificial bee colony algorithm for global numerical optimization. Inf. Sci. 417, 169–185 (2017)

    Article  MATH  Google Scholar 

  6. Gao, W., Chan, F., Huang, L., Liu, S.: Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood. Inf. Sci. 316, 180–200 (2015)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  8. Gao, W., Huang, L., Liu, S., Chan, F., Dai, C., Shan, X.: Artificial bee colony algorithm with multiple search strategies. Appl. Math. Comput. 271, 269–287 (2015)

    MathSciNet  MATH  Google Scholar 

  9. Gao, W., Liu, S., Huang, L.: Enhancing artificial bee colony algorithm using more information-based search equations. Inf. Sci. 270, 112–133 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  10. Wang, H., Wu, Z., Rahnamayan, S., Liu, Y., Ventresca, M.: Enhancing particle swarm optimization using generalized opposition-based learning. Inf. Sci. 181(20), 4699–4714 (2011)

    Article  MathSciNet  Google Scholar 

  11. Ji, J., Song, S., Tang, C., Gao, S., Tang, Z., Todo, Y.: An artificial bee colony algorithm search guided by scale-free networks. Inf. Sci. 473, 142–165 (2019)

    Article  Google Scholar 

  12. Karaboga, D.: An idea based on honey bee swarm for numerical optimization, Technical report - tr06 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  14. Kiran, M.S., Hakli, H., Gunduz, M., Uguz, H.: Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf. Sci. 300, 140–157 (2015)

    Article  MathSciNet  Google Scholar 

  15. Liang, Z., Hu, K., Zhu, Q., Zhu, Z.: An enhanced artificial bee colony algorithm with adaptive differential operators. Appl. Soft Comput. 58, 480–494 (2017)

    Article  Google Scholar 

  16. Peng, C.: Best neighbor-guided artificial bee colony algorithm for continuous optimization problems. Soft Comput.: Fusion Found., Methodol. Appl. 23(18) (2019)

    Google Scholar 

  17. Peng, H., Zhu, W., Deng, C., Wu, Z.: Enhancing firefly algorithm with courtship learning. Inf. Sci. 543, 18–42 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  18. Song, Q.: A high-efficiency adaptive artificial bee colony algorithm using two strategies for continuous optimization. Swarm Evol. Comput. 50, 100549 (2019)

    Article  Google Scholar 

  19. Tao, X., Li, X., Chen, W., Liang, T., Qi, L.: Self-adaptive two roles hybrid learning strategies-based particle swarm optimization. Inf. Sci. 578(8), 457–481 (2021)

    Article  MathSciNet  Google Scholar 

  20. Ty, A., et al.: Artificial bee colony algorithm with efficient search strategy based on random neighborhood structure. Knowl.-Based Syst. 241, 108306 (2022)

    Article  Google Scholar 

  21. Wang, F., Zhang, H., Li, K., Lin, Z., Yang, J., Shen, X.L.: A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inf. Sci. 436–437, 162–177 (2018)

    Article  MathSciNet  Google Scholar 

  22. Wang, H., Wang, W., Xiao, S., Cui, Z., Zhou, X.: Improving artificial bee colony algorithm using a new neighborhood selection mechanism. Inf. Sci. 527, 227–240 (2020)

    Article  MathSciNet  Google Scholar 

  23. Wang, H., et al.: Firefly algorithm with neighborhood attraction. Inf. Sci. 382–383, 374–387 (2016)

    Google Scholar 

  24. Wang, H., Wu, Z., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.S.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 279, 587–603 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  25. Xs, A., Ming, Z.A., Sx, B.: A multi-strategy fusion artificial bee colony algorithm with small population. Expert Syst. Appl. 142, 112921 (2020)

    Article  Google Scholar 

  26. Yu, W., Zhan, Z., Zhang, J.: Artificial bee colony algorithm with an adaptive greedy position update strategy. Soft. Comput. 22, 437–451 (2018)

    Article  Google Scholar 

  27. Yurtkuran, A., Emel, E.: An adaptive artificial bee colony algorithm for global optimization. Appl. Math. Comput. 271, 1004–1023 (2015)

    MathSciNet  MATH  Google Scholar 

  28. Zhang, X., Lin, Q.: Three-learning strategy particle swarm algorithm for global optimization problems. Inf. Sci.: Int. J. 593, 289–313 (2022)

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by Jiangxi Provincial Natural Science Foundation (Nos. 20212BAB202023 and 20212BAB202022).

Author information

Authors and Affiliations

  1. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Jiawen Liu, Jiali Wu, Hui Wang, Hai Zhang & Min Hu

  2. School of Business Administration, Nanchang Institute of Technology, Nanchang, 330099, China

    Wenjun Wang

Authors
  1. Jiawen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiali Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Min Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Wang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Chaohu University, Hefei, China

    Yinggen Ke

  3. Chongqing University, Chongqing, China

    Zhou Wu

  4. South China Normal University, Guangzhou, China

    Tianyong Hao

  5. Hefei University of Technology, Hefei, China

    Zhao Zhang

  6. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  7. Chaohu University, Hefei, China

    Yuanyuan Mu

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, J., Wang, W., Wu, J., Wang, H., Zhang, H., Hu, M. (2023). Artificial Bee Colony Based on Adaptive Search Strategies and Elite Selection Mechanism. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_22

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-5844-3_22

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5843-6

  • Online ISBN: 978-981-99-5844-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation