Cloud Model Glowworm Swarm Optimization Algorithm for Functions Optimization

  • Qiang Zhou
  • Yongquan Zhou
  • Xin Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)


For basic artificial glowworm swarm optimization algorithm has a slow convergence and easy to fall into local optimum, and the cloud model has excellent characteristics with uncertainty knowledge representation, an artificial glowworm swarm optimization algorithm based on cloud model is presented by utilizing these characteristics. The algorithm selects an optimal value of each generation as the center point of the cloud model, compares with cloud droplets and then achieves the better search value of groups which can avoid falling into the local optimum and can speed up the convergence rate of the algorithm. Finally, we use the standard function to test the algorithm. And the test results show that the convergence and the solution accuracy of our proposed algorithm have been greatly improved compared with the basic artificial glowworm swarm optimization algorithm.


cloud model glowworm swarm optimization algorithm standard function optimal value 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Krishnan, K.N., Ghose, D.: Glowworm swarm optimization: a new method for optimizing multi-modal functions. Computational Intelligence Studies 1, 93–119 (2009)Google Scholar
  2. 2.
    Krishnanand, K.N.: Glowworm swarm optimization: a multimodal function optimization paradigm with applications to multiple signal source localization tasks. Indian Institute of Science, Indian (2007)Google Scholar
  3. 3.
    Krishnanand, K.N., Ghose, D.: A glowworm swarm optimization based multi-robot system for signal source localization. In: Liu, D., Wang, L., Tan, K.C. (eds.) Design and Control of Intelligent Robotic Systems. SCI, vol. 177, pp. 49–68. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Krishnanand, K.N., Ghose, D.: Multiple mobile signal sources:a gloworm swarm optimizationapproach. In: Proc. of the 3rd Indian International Conference on Artificial Intelligence (2007)Google Scholar
  5. 5.
    Liu, J.-K., Zhou, Y.-Q.: A parallel artificial glowworm algorithm With a master-slave structure. Computer Engineering and Applications 48, 33–38 (2012)Google Scholar
  6. 6.
    Li, D.-Y., Yang, Z.-H.: Two-dimensional cloud model and its application in predicting. Journal of Computers 21, 961–969 (1998)Google Scholar
  7. 7.
    Li, D.-Y., Meng, H.-J., Shi, X.-M.: Membership clouds and membership cloud generators. Computer Research and Development 32, 16–20 (1995)Google Scholar
  8. 8.
    Ye, D.-Y., Lin, X.-J.: The cloud variability artificial bee colony algorithm. Computer Application 32, 2538–2541 (2010)Google Scholar
  9. 9.
    Zhang, G.-W., He, R., Liu, Y.: Evolutionary algorithm based on cloud model. Journal of Computers 31, 1082–1091 (2008)MathSciNetGoogle Scholar
  10. 10.
    Liu, J.-K., Zhou, Y.-Q.: A artificial glowworm algorithm of a maximum and minimum fluorescein value. Application Research of Computers 28, 3662–3665 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qiang Zhou
    • 1
  • Yongquan Zhou
    • 2
  • Xin Chen
    • 1
  1. 1.College of Information Science and EngineeringGuangxi University for NationalitiesNanningChina
  2. 2.Guangxi Key Laboratory of Hybrid Computation and IC Design AnalysisNanningChina

Personalised recommendations