Artificial Bee Colony Algorithm Based on Uniform Local Search

  • Yan ZhangEmail author
  • Hu Peng
  • Changshou Deng
  • Xiaojing Wang
  • Haiyan Huang
  • Xujie Tan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)


Although Artificial Bee Colony (ABC) algorithm is simple and efficient, it also has some disadvantages too. For example, the ABC is good at exploration but poor at exploitation and easily falls into local optimum. In order to overcome these shortcomings and improve the efficiency of the algorithm, the Uniform Local Search Artificial Bee Colony (UGABC) algorithm has been proposed in this paper. The algorithm greatly improves the exploitation ability. For the purpose of comparison, we used four algorithms to experiment. The experimental results show that the UGABC has the best accuracy and the fastest convergence rate among four algorithms.


Artificial bee colony Uniform design Uniform local search Gbest 



This work was supported by The National Science Foundation of China (No. 61763019), The Natural Science Foundation of Heilongjiang Province (General Program: F2017019), The Science and Technology Plan Projects of Jiangxi Province Education Department (No. GJJ161072, No. GJJ161076, No. GJJ170953), The Education Planning Project of Jiangxi Province (No. 15YB138, No. 17YB211).


  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 4th IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Piscataway (1995)Google Scholar
  2. 2.
    Yang, X.S., Deb, S.: Cuckoo search via Levy flights. In: World Congress on IEEE Nature & Biologically Inspired Computing, pp. 210–214 (2009)Google Scholar
  3. 3.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. TR06, Computers Engineering Department, Engineering Faculty, Erciyes University, Kayseri (2005)Google Scholar
  4. 4.
    Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 2(14), 108–132 (2009)MathSciNetzbMATHGoogle 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.
    Peng, H., Wu, Z., Deng, C.: Enhancing differential evolution with communal learning and uniform local search. Chin. J. Electron. 26(4), 725–733 (2017)CrossRefGoogle Scholar
  7. 7.
    Zhu, G., Sam, K.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217(7), 3166–3173 (2010)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Wang, Y., Fang, K.: A note on uniform distribution and experimental design. Mon. J. Sci. 26(6), 485–489 (1981)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Fang, K.: Uniform design-number theory method in the application of experimental design. Acta Math. Appl. Sinica 04, 363–372 (1980)Google Scholar
  10. 10.
    Fang, K.: Uniform design. Tactical Missile Technol. 02, 56–69 (1994)Google Scholar
  11. 11.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)CrossRefGoogle Scholar
  12. 12.
    Peng, H., Wu, Z., Zhou, X., et al.: Bare-bones differential evolution algorithm based on trigonometry. J. Comput. Res. Dev. 12, 2776–2788 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yan Zhang
    • 1
    Email author
  • Hu Peng
    • 1
  • Changshou Deng
    • 1
  • Xiaojing Wang
    • 1
  • Haiyan Huang
    • 1
  • Xujie Tan
    • 1
  1. 1.School of Information Science and TechnologyJiujiang UniversityJiujiangChina

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