Advertisement

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

  • Wen Liu
  • Tuqian Zhang
  • Yan Liu
  • Ningning Zhang
  • Hongyu Tao
  • Guoqing Fu
Article
  • 70 Downloads

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.

Keywords

Swarm intelligence Artificial bee colony (ABC) Bidirectional random optimization (BRO) Self-adaptive Particle swarm optimization (PSO) 

Notes

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.

References

  1. 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)CrossRefGoogle Scholar
  2. 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)Google Scholar
  3. 3.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization, Technical Report-TR06. Computer Engineering Department, Engineering Faculty, Erciyes University (2005)Google Scholar
  4. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)CrossRefGoogle Scholar
  6. 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. 7.
    Roeva, O.: Application of Artificial Bee Colony Algorithm for Model Parameter Identification. Springer, Cham (2018)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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 1998Google Scholar
  11. 11.
    Bharti, K.K., Singh, P.K.: Chaotic gradient artificial bee colony for text clustering. Soft. Comput. 20(3), 1113–1126 (2016)CrossRefGoogle Scholar
  12. 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. 13.
    Zhang, M., Ji, Z., Wang, Y.: Artificial bee colony algorithm with dynamic multi-population. Mod. Phys. Lett. B 31, 1740087 (2017)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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. 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)CrossRefGoogle Scholar
  17. 17.
    Ozturk, C., Hancer, E., Karaboga, D.: Dynamic clustering with improved binary artificial bee colony algorithm. Appl. Soft Comput. 28, 69–80 (2015)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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. 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)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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. 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. 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)Google Scholar
  26. 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

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wen Liu
    • 1
  • Tuqian Zhang
    • 2
    • 3
  • Yan Liu
    • 1
  • Ningning Zhang
    • 1
  • Hongyu Tao
    • 3
  • Guoqing Fu
    • 2
    • 3
  1. 1.Department of Electrical and Information EngineeringXinjiang Institute of EngineeringUrumqiChina
  2. 2.School of Computer Science and TechnologyDalian University of TechnologyDalianChina
  3. 3.School of Science and TechnologyXinjiang Agricultural UniversityUrumqiChina

Personalised recommendations