Advertisement

An Improved Artificial Bee Colony Algorithm Based on Gaussian Mutation and Chaos Disturbance

  • Xiaoya Cheng
  • Mingyan Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

Abstract

Artificial Bee Colony (ABC) algorithm is a novel bio-inspired swarm intelligence approach which is competitive with other population-based algorithms and has the advantage of using fewer control parameters. However, basic ABC is easy to be prematurely convergent and be trapped into local optimum. In the later iteration, algorithm has low convergent speed and population diversity seriously decreases. In this paper, Gaussian mutation and chaos disturbance are introduced into ABC to overcome the shortcomings above. Applications of improved ABC algorithm on four benchmark optimization functions show marked improvement in performance over the basic ABC.

Keywords

Artificial Bee Colony (ABC) algorithm Gaussian mutation Chaos disturbance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Karaboga, D.: An Artificial Bee Colony (ABC) Algorithm for Numeric Function Optimization. In: IEEE Swarm Intelligence Symposium, pp. 181–184. IEEE Press, Indiana (2006)Google Scholar
  2. 2.
    Jiang, M., Yuan, D.: Artificial Fish Swarm Algorithm and Its Applications. Science Press, Beijing (2012)Google Scholar
  3. 3.
    Sonmez, M.: Artificial Bee Colony Algorithm for Optimization of Truss Structures. Applied Soft Computing Journal 10, 195–197 (2010)Google Scholar
  4. 4.
    Hsieh, T.J., Hsiao, H.F.: Forecasting Stock Markets using Wavelet Transforms and Recurrent Neural Networks: an Integrated System Based on Artificial Bee Colony Algorithm. Applied Soft Computing Journal 10, 156–162 (2010)Google Scholar
  5. 5.
    Karaboga, D.: A Novel Clustering Approach: Artificial Bee Colony (ABC) Algorithm. Applied Soft Computing 11, 652–657 (2011)CrossRefGoogle Scholar
  6. 6.
    Zh, C., Ouyang, D., Ning, J.: An Artificial Bee Colony Approach for Clustering. Expert Systems with Applications 37, 4761–4767 (2010)CrossRefGoogle Scholar
  7. 7.
    Chunfan, X.: Artificial Bee Colony (ABC) Optimized Edge Potential Function (EPF) Approach to Target Recognition for Low-altitude Aircraft. Pattern Recognition Letters 31, 1759–1772 (2010)CrossRefGoogle Scholar
  8. 8.
    Singh, A.: An Artificial Bee Colony Algorithm for the Leaf-constrained Minimum Spanning Tree Problem. Applied Soft Computing 9, 625–631 (2009)CrossRefGoogle Scholar
  9. 9.
    Karaboga, N.: A New Design Method Based on Artificial Bee Colony Algorithm for Digital IIR Filters. Journal of the Franklin Institute 346, 328–348 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Li, B., Zeng, J.: Self-adapting Search Space Chaos-artificial Bee Colony Algorithm. Application Research of Computers 27, 1331–1335 (2010)Google Scholar
  11. 11.
    Ding, H., Feng, Q.: Artificial Bee Colony Algorithm Based on Boltzmann Selection Policy. Computer Engineering and Applications 45, 53–55 (2009)Google Scholar
  12. 12.
    Kang, F., Li, J., Xu, Q.: Structural Inverse Analysis by Hybrid Simplex Artificial Neural Networks. In: 15th IEEE Proc. Signal Processing and Communications Applications, pp. 1–4. IEEE Press, SIU (2007)Google Scholar
  13. 13.
    Xu, C.: Chaotic Artificial Bee Colony Approach to Uninhabited Combat Air Vehicle (UCAV) Path Planning. Aerospace Science and Technology 26, 156–162 (2010)Google Scholar
  14. 14.
    Kang, F., Li, J., Li, H.: An Improved Artificial Bee Colony Algorithm. In: 2nd International Workshop on Intelligent Systems and Applications, pp. 15–21. IEEE Press, Wuhan (2010)Google Scholar
  15. 15.
    Bucolo, M., Caponetto, R., Fortuna, L., Frasca, M., Rizzo, A.: Does Chaos Work Better than Noise? Circuits and Systems Magazine 2, 4–19 (2002)CrossRefGoogle Scholar
  16. 16.
    Wang, L., Zheng, D., Lin, Q.: Survey on Chaotic Optimization Methods. Comput. Technol. Automat. 20, 1–5 (2001)Google Scholar
  17. 17.
    Li, C., Zhang, X.: Design of Pseudo-random Sequence Generator Based on Chaos Anti-control Tent Map. Journal of Computer Applications 28, 48–51 (2008)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaoya Cheng
    • 1
  • Mingyan Jiang
    • 1
  1. 1.School of Information Science and EngineeringShandong UniversityJinanChina

Personalised recommendations