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Artificial Bee Colony Algorithm Based on Neighboring Information Learning

  • Laizhong CuiEmail author
  • Genghui Li
  • Qiuzhen Lin
  • Jianyong Chen
  • Nan Lu
  • Guanjing Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

Artificial bee colony (ABC) algorithm is one of the most effective and efficient swarm intelligence algorithms for global numerical optimization, which is inspired by the intelligent foraging behavior of honey bees and has shown good performance in most case. However, due to its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. In order to solve this concerning issue, in this paper, we propose a novel artificial bee colony algorithm based on neighboring information learning (called NILABC), in which the employed bees and onlooker bees search candidate food source by learning the valuable information from the best food source among their neighbors. Furthermore, the size of the neighbors is linearly increased with the evolutionary process, which is used to ensure the employed bees and onlooker bees obtain the guidance from the best solution in local area at the early stage and the best solution in the global area at the late stage. Through the comparison of NILABC with the basic ABC and some other variants of ABC on 22 benchmark functions, the experimental results demonstrate that NILABC is better than the compared algorithms on most cases in terms of solution quality, robustness and convergence speed.

Keywords

Evolutionary algorithm Artificial bee colony algorithm Neighboring information learning Ranking-based probability selection Global numerical optimization 

Notes

Acknowledgement

This work is supported by the National Natural Science Foundation of China under Grants 61402291, 61402294, and 61170283, National High-Technology Research and Development Program (863 Program) of China under Grant 2013AA01A212, Ministry of Education in the New Century Excellent Talents Support Program under Grant NCET-12-0649, Foundation for Distinguished Young Talents in Higher Education of Guangdong, China under Grant 2013LYM_0076 and 2014KQNCX129, Major Fundamental Research Project in the Science and Technology Plan of Shenzhen under Grants JCYJ20140828163633977 and JCYJ20140418181958501.

References

  1. 1.
    Chuang, Y.C., Chen, C.T., Hwang, C.: A real-code genetic algorithm with a direction-based crossover operator. Inform. Sci. 305, 320–348 (2015)CrossRefGoogle Scholar
  2. 2.
    Cui, L.Z., Li, G.H., Lin, Q.Z., Chen, J.Y., Lu, N.: Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput. Oper. Res. 67, 155–173 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)CrossRefGoogle Scholar
  4. 4.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Opt. 39, 459–471 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)CrossRefGoogle Scholar
  6. 6.
    Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217, 3166–3173 (2010)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Xiang, Y., Peng, Y.M., Zhong, Y.B., Chen, Z.Y., Lu, X.W., Zhong, X.J.: A particle swarm inspired multi-elite artificial bee colony algorithm for real-parameter optimization. Comput. Optim. Appl. 57, 493–516 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Banharnsakun, A., Achalakul, T., Sirinaovakul, B.: The best-so-far selection in artificial bee colony algorithm. Appl. Soft. Comput. 11, 2888–2901 (2010)CrossRefGoogle Scholar
  9. 9.
    Gao, W.F., Liu, S.Y., Huang, L.L.: A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans. Cybern. 43, 1011–1024 (2013)CrossRefGoogle Scholar
  10. 10.
    Karaboga, D., Gorkemli, B.: A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl. Soft Comput. 23, 227–238 (2014)CrossRefGoogle Scholar
  11. 11.
    Wang, H., Wu, Z.J., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.S.: Multi-strategy ensemble artificial bee colony algorithm. Inform. Sci. 279, 587–603 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Kiran, M.S., Hakli, H., Guanduz, M., Uguz, H.: Artificial bee colony algorithm with variable search strategy for continuous optimization. Inform. Sci. 300, 140–157 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Kang, F., Li, J.J., Ma, Z.Y.: Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Inform. Sci. 12, 3508–3531 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Kang, F., Li, J.J., Li, H.J.: Artificial bee colony algorithm and pattern search hybridized for global optimization. Appl. Soft Comput. 13, 1781–1791 (2013)CrossRefGoogle Scholar
  15. 15.
    Gao, W.F., Liu, S.Y.: A modified artificial bee colony algorithm. Comput. Oper. Res. 39, 687–697 (2012)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Laizhong Cui
    • 1
    Email author
  • Genghui Li
    • 1
  • Qiuzhen Lin
    • 1
  • Jianyong Chen
    • 1
  • Nan Lu
    • 1
  • Guanjing Zhang
    • 2
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  2. 2.E-Techco Information Technologies Co., Ltd.ShenzhenChina

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