Advertisement

Large Scale Optimization Based on Co-ordinated Bacterial Dynamics and Opposite Numbers

  • Jaydeep Ghosh Chowdhury
  • Aritra Chowdhury
  • Arghya Sur
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7677)

Abstract

This work, named Large Scale Optimization based on co-ordinated Bacterial Dynamics and Opposite Numbers (LSCBO) presents a very fast algorithm to solve large scale optimization problems. The computational simplicity of the algorithm allows it to achieve admirable results. There are only three searching agents in the population, one being the primary bacterium and the other two are secondary bacteria. The proposed algorithm is employed on 7 benchmark functions of CEC2008 and it gives better results compared to the other well known contemporary algorithms present in the literature. The main reason for this is that the computational burden of the algorithm is significantly reduced.

Keywords

Evolutionary algorithms large scale optimization bacterial dynamics quorum sensing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Learning Robust Object Segmentation from User-Prepared Samples. WSEAS Transactions on Computers 4(9), 1163–1170 (2005)Google Scholar
  2. 2.
    Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Towards Incomplete Object Recognition. WSEAS Transactions on Systems 4(10), 1725–1732 (2005)Google Scholar
  3. 3.
    Sharif, B., Wang, G.G., El-Mekkawy, T.: Mode Pursing Sampling Method for Discrete Variable Optimization on Expensive Blackbox Functions. ASME Transactions, Journal of Mechanical Design 130, 021402-1–021402-11 (2008)CrossRefGoogle Scholar
  4. 4.
    Wang, L., Shan, S., Wang, G.G.: Mode-Pursuing Sampling Method for Global optimization on Expensive Black-box Functions. Journal of Engineering Optimization 36(4), 419–438 (2004)CrossRefGoogle Scholar
  5. 5.
    Zhao, S.Z., Suganthan, P.N., Das, S.: Self-adaptive Differential Evolution with Multi-trajectory Search for Large Scale Optimization. Soft Computing 15(11), 2175–2185 (2011)CrossRefGoogle Scholar
  6. 6.
    Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. on Evolutionary Computations, 398–417 (April 2009)Google Scholar
  7. 7.
    Li, X., Yao, X.: Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms. In: Proc. IEEE CEC, pp. 1546–1553 (May 2009)Google Scholar
  8. 8.
    Ros, R., Hansen, N.: A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN X. LNCS, vol. 5199, pp. 296–305. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jaydeep Ghosh Chowdhury
    • 1
  • Aritra Chowdhury
    • 1
  • Arghya Sur
    • 1
  1. 1.Department of Electronics and Telecommunication Engg.Jadavpur UniversityKolkataIndia

Personalised recommendations