[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Improved artificial bee colony algorithm based on self-adaptive random optimization strategy

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In order to effectively overcome the disadvantages of the traditional artificial bee colony (ABC) algorithm, i.e., its tendency to fall into local optima and low search speed, an improved ABC algorithm based on the self-adaptive random optimization strategy (SRABC) is proposed. First, the improved algorithm was derived from the self-adaptive method to update the new location of an ABC to improve the correlation within the bee colony. It converges swiftly and obtains the optimal solution for the benchmark function. Second, the bidirectional random optimization mechanism was used to restrain the search direction for the fitness function in order to improve the local search ability. Moreover, the particle swarm optimization algorithm regarded as the initial value of the SRABC algorithm was introduced at the initial stage of the improved ABC algorithm to increase the convergence rate, search precision and searchability, and greatly reduce the search space. Finally, simulation results for benchmark functions show that the proposed algorithm has obviously better performance regarding the search ability and convergence rate, which also prevents early maturing of algorithm.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Dorgo, M., Maniezzo, V., Colorni, A.: The ants system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)

    Article  Google Scholar 

  2. Kennedy, J., Ebethart, R.: Particle swarm optimization. In: Proceeding of IEEE International Conference on Neural Networks. IEEE Computer Society, Piscataway, pp. 1942–1948 (1995)

  3. Karaboga, D.: An idea based on honey bee swarm for numerical optimization, Technical Report-TR06. Computer Engineering Department, Engineering Faculty, Erciyes University (2005)

  4. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial Bee Colony(ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  5. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)

    Article  Google Scholar 

  6. Abraham, A., Jatoth, R.K., Rajasekhar, A.: Hybrid differential artificial bee colony algorithm. J. Comput. Theor. Nanosci. 9(2), 1–9 (2013)

    Google Scholar 

  7. Roeva, O.: Application of Artificial Bee Colony Algorithm for Model Parameter Identification. Springer, Cham (2018)

    Book  Google Scholar 

  8. Xu, C.F., Dun, H.B., Liu, F.: Chaotic artificial bee colony approach to uninhabited combat air vehicle (UCAV) path planning. Aerosp. Sci. Technol. 14(8), 535–541 (2010)

    Article  Google Scholar 

  9. Zhang, H., Peng, M., Wu, H., et al.: A strategy for intentional islanding of distribution networks based on node electrical relevance and artificial bee colony algorithm. IEEJ Trans. Electr. Electron. Eng. 13(2), 84–91 (2018)

    Article  Google Scholar 

  10. Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of IEEE World Congress on Evolutionary Computation, Anchorage, Alaska, USA, pp. 69–73, May 1998

  11. Bharti, K.K., Singh, P.K.: Chaotic gradient artificial bee colony for text clustering. Soft. Comput. 20(3), 1113–1126 (2016)

    Article  Google Scholar 

  12. Kishor, A., Chandra, M., Singh, P.K.: An astute artificial bee colony algorithm. J. Adv. Intell. Syst. Comput. 546, 153–162 (2017)

    Google Scholar 

  13. Zhang, M., Ji, Z., Wang, Y.: Artificial bee colony algorithm with dynamic multi-population. Mod. Phys. Lett. B 31, 1740087 (2017)

    Article  Google Scholar 

  14. Ding, M., Chen, H., Lin, N., et al.: Dynamic population artificial bee colony algorithm for multi-objective optimal power flow. Saudi J. Biol. Sci. 24(3), 703 (2017)

    Article  Google Scholar 

  15. Huo, F., Liu, Y., Wang, D., et al.: Bloch quantum artificial bee colony algorithm and its application in image threshold segmentation. Signal Image Video Process. 11(12), 1–8 (2017)

    Google Scholar 

  16. Kefayat, M., Ara, A.L., Niaki, S.A.N.: A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. Energy Convers. Manag. 92(3), 149–161 (2015)

    Article  Google Scholar 

  17. Ozturk, C., Hancer, E., Karaboga, D.: Dynamic clustering with improved binary artificial bee colony algorithm. Appl. Soft Comput. 28, 69–80 (2015)

    Article  Google Scholar 

  18. Gao, K.Z., Suganthan, P.N., Pan, Q.K., et al.: An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Syst. Appl. 65(C), 52–67 (2016)

    Article  Google Scholar 

  19. Li, J.Q., Pan, Q.K., Duan, P.Y.: An improved artificial bee colony algorithm for solving hybrid flexible flow shop with dynamic operation skipping. IEEE Trans. Cybern. 46(6), 1311–1324 (2016)

    Article  Google Scholar 

  20. Zhang, P., Li, J., Hu, X., et al.: Crossover-based artificial bee colony algorithm for constrained optimization problems. Neural Comput. Appl. 26(7), 1587–1601 (2017)

    Google Scholar 

  21. Xiang, W.L., Meng, X.L., Li, Y.Z., et al.: An improved artificial bee colony algorithm based on the gravity model. Inf. Sci. 429, 49–71 (2018)

    Article  Google Scholar 

  22. Kim, Y.H., Han, S.Y.: Topological shape optimization scheme based on the artificial bee colony algorithm. Int. J. Precis. Eng. Manuf. 18(10), 1393–1401 (2017)

    Article  Google Scholar 

  23. Wen Ming, M.A., Meng, X.W., Zhang, Y.J.: Bidirectional random walk search mechanism for unstructured P2P network. J. Softw. 23(4), 894–911 (2013)

    Google Scholar 

  24. Jia, Z., Si, X., Wang, T.: Optimum method for sea clutter parameter based on artificial bee colony. J. Cent. South Univ. (Sci. Technol.) 43(9), 3485–3489 (2012)

    Google Scholar 

  25. Sethi, D., Singhal, A.: Comparative analysis of a recommender system based on ant colony optimization and artificial bee colony optimization algorithms. In: International Conference on Computing, Communication and Networking Technologies, pp. 1–4. IEEE Computer Society (2017)

  26. Zhao, Z., Huang, W.: Improved artificial bee swarm algorithm and its application in optimal operation of wind-power generators. J. Cent. South Univ. (Sci. Technol.) 42(10), 3101–3104 (2011)

    Google Scholar 

Download references

Acknowledgements

Part of the results in this paper appeared in the Proceedings of the 9th International Symposium on Computational Intelligence and Design (ISCID), 2016. This work is supported by the Scientific Research Program of the Higher Education Institution of Xinjiang under Grant No. XJEDU2016I049, the Natural Science Foundation of Xinjiang Uygur Autonomous Region under Grant No. 2017D01B09, Youth Research start-up fund project of School of Science and Technology Xinjiang Agricultural University under Grant No. 2016KJKY006 and No. 2016KJKY007.

Author information

Authors and Affiliations

  1. Department of Electrical and Information Engineering, Xinjiang Institute of Engineering, Urumqi, 830091, China

    Wen Liu, Yan Liu & Ningning Zhang

  2. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China

    Tuqian Zhang & Guoqing Fu

  3. School of Science and Technology, Xinjiang Agricultural University, Urumqi, 830091, China

    Tuqian Zhang, Hongyu Tao & Guoqing Fu

Authors
  1. Wen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tuqian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ningning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hongyu Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guoqing Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wen Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, W., Zhang, T., Liu, Y. et al. Improved artificial bee colony algorithm based on self-adaptive random optimization strategy. Cluster Comput 22 (Suppl 2), 3971–3980 (2019). https://doi.org/10.1007/s10586-018-2558-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2558-4

Keywords

Search

Navigation