Advertisement

A New Multi-strategy Ensemble Artificial Bee Colony Algorithm for Water Demand Prediction

  • Hui Wang
  • Wenjun WangEmail author
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)

Abstract

Artificial bee colony (ABC) is an efficient global optimizer, which has bee successfully used to solve various optimization problems. Recently, multi-strategy ensemble technique was embedded to ABC to make a good trade-off between exploration and exploitation. In this paper, a new multi-strategy ensemble ABC (NMEABC) is proposed. In our approach, each food source is assigned a probability to control the frequency of dimension perturbation. Experimental results show that NMEABC is superior to the original multi-strategy ensemble ABC (MEABC). Finally, NMEABC is applied to predict the water demand in Nanchang city. Simulation results demonstrate that NMEABC can achieve a good prediction accuracy.

Keywords

Artificial bee colony Swarm intelligence Multi-strategy Ensemble Water demand prediction 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgement

This work was supported by the Science and Technology Plan Project of Jiangxi Provincial Education Department (No. GJJ170994), the National Natural Science Foundation of China (No. 61663028), the Distinguished Young Talents Plan of Jiangxi Province (No. 20171BCB23075), the Natural Science Foundation of Jiangxi Province (No. 20171BAB202035), and the Open Research Fund of Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing (No. 2016WICSIP015).

References

  1. 1.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University, engineering Faculty, Computer Engineering Department (2005)Google Scholar
  2. 2.
    Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)CrossRefGoogle Scholar
  4. 4.
    Zhao, J., et al.: Artificial bee colony based on special central and adapt number of dimensions learning. J. Inf. Hiding Multimed. Sig. Process. 7(3), 645–652 (2016)Google Scholar
  5. 5.
    Panda, T.R., Swamy, A.K.: An improved artificial bee colony algorithm for pavement resurfacing problem. Int. J. Pavement Res. Technol. 11(5), 509–516 (2018)CrossRefGoogle Scholar
  6. 6.
    Sharma, N., Sharma, H., Sharma, A.: Beer froth artificial bee colony algorithm for job-shop scheduling problem. Appl. Soft Comput. 68, 507–524 (2018)CrossRefGoogle Scholar
  7. 7.
    He, Y., Xue, X.S., Zhang, S.M.: Using artificial bee colony algorithm for optimizing ontology alignment. J. Inf. Hiding Multimed. Sig. Process. 8(4), 766–773 (2017)Google Scholar
  8. 8.
    Cui, L.Z., et al.: A smart artificial bee colony algorithm with distance-fitness-based neighbor search and its application. Future Gener. Comput. Syst. 89, 478–493 (2018)CrossRefGoogle Scholar
  9. 9.
    Cui, L.Z., et al.: A ranking-based adaptive artificial bee colony algorithm for global numerical optimization. Inf. Sci. 417, 169–185 (2017)CrossRefGoogle Scholar
  10. 10.
    Kumar, A., Kumar, D., Jarial, S.K.: A review on artificial bee colony algorithms and their applications to data clustering. Cybern. Inf. Technol. 17(3), 3–28 (2017)MathSciNetGoogle Scholar
  11. 11.
    Wu, C.M., Fu, S.R., Li, T.T.: Research of the WSN routing based on artificial bee colony algorithm. J. Inf. Hiding Multimed. Sig. Process. 8(1), 120–126 (2017)Google Scholar
  12. 12.
    Tang, L.L., Li, Z.H., Pan, J.S., Wang, Z.F., Ma, K.Q., Zhao, H.N.: Novel artificial bee colony algorithm based load balance method in cloud computing. J. Inf. Hiding Multimed. Sig. Process. 8(2), 460–467 (2017)Google Scholar
  13. 13.
    Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217, 3166–3173 (2010)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Gao, W., Liu, S.: A modified artificial bee colony algorithm. Comput. Oper. Res. 39, 687–697 (2012)CrossRefGoogle Scholar
  15. 15.
    Wang, H., Wu, Z.J., Zhou, X.Y., Rahnamayan, S.: Accelerating artificial bee colony algorithm by using an external archive. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 517–521 (2013)Google Scholar
  16. 16.
    Wang, H., Wu, Z.J., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.S.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 279, 587–603 (2014)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wang, H., Wang, W.J., Cui, Z.H., Zhou, X.Y., Zhao, J., Li, Y.: A new dynamic firefly algorithm for demand estimation of water resources. Inf. Sci. 438, 95–106 (2018)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Akay, B., Karaboga, D.: A modified Artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192, 120–142 (2012)CrossRefGoogle Scholar
  19. 19.
    Wang, H., et al.: Firefly algorithm for demand estimation of water resources. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) ICONIP 2017. LNCS, vol. 10637, pp. 11–20. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70093-9_2CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent ProcessingNanchang Institute of TechnologyNanchangChina
  2. 2.School of Information EngineeringNanchang Institute of TechnologyNanchangChina
  3. 3.School of Business AdministrationNanchang Institute of TechnologyNanchangChina

Personalised recommendations

Cite paper

Buy options