Cloud Droplets Evolutionary Algorithm on Reciprocity Mechanism for Function Optimization

  • Lei Wang
  • Wei Li
  • Rong Fei
  • Xinghong Hei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)


For the problems of solving difficult problems in evolutionary algorithms such as easily falling into local optimum, premature convergence because of selective pressure, a complex and larger calculation and a lower accuracy of the solution, this paper proposes cloud droplets evolutionary model on reciprocity mechanism (CDER). The main idea of CDER is to simulate the phase transition of the cloud in nature which has vapor state, liquid state and solid state, and to combine the basic ideas of evolutionary computation to realize the population evolution. The condensation growth and collision growth of cloud droplets correspond to the competitive evolution and reciprocal evolution of species in nature. Experiments on solving the function optimization problems show that this model can enhance the individual competition and survival ability, guarantee the population diversity, accelerate the convergence speed and improve the solution precision through the iterative process of competition mechanism and reciprocity mechanism.


reciprocity mechanism competition mechanism cloud droplets evolutionary algorithms phase transition 


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  1. 1.
    De Yi, L., Chang Yu, L.: Study on the Universality of the Normal Cloud Model. Engineering Sciences 6, 28–33 (2004)Google Scholar
  2. 2.
    De Yi, L., Chang Yu, L., Yi, D., Xu, H.: Artificial Intelligence with Uncertainty. Journal of Software 15, 1583–1592 (2004)zbMATHGoogle Scholar
  3. 3.
    Guang Wei, Z., Rui, H., Yu, L., De Yi, L., Gui Sheng, C.: An Evolutionary Algorithm Based on Cloud Model. Chinese Journal of Computers 31, 1082–1091 (2008)Google Scholar
  4. 4.
    Pennisi, E.: On the Origin of Cooperation. Science 325, 1196–1199 (2009)CrossRefGoogle Scholar
  5. 5.
    Nowak, M.A.: Five Rules for the Evolution of Cooperation. Science 12, 1560–1563 (2006)CrossRefGoogle Scholar
  6. 6.
    Thompson, J.N., Cunningham, B.M.: Geographic Structure and Dynamics of Coevolutionary Selection. Nature 417, 735–738 (2002)CrossRefGoogle Scholar
  7. 7.
    Haldane, J.B.S.: The Causes of Evolution. Longmans Green & Co., London (1932)Google Scholar
  8. 8.
    Hamilton, W.D.: The Genetical evolution of social behaviour. Journal of Theoretical Biology 7, 17–52 (1964)CrossRefGoogle Scholar
  9. 9.
    Axelrod, R., Hamilton, W.D.: The Evolution of Cooperation. Science 211, 1390–1396 (1981)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Nowak, M.A., Sigmund, K.: Evolution of Indirect Reciprocity by Image Scoring. Nature 393, 573–577 (1998)CrossRefGoogle Scholar
  11. 11.
    Traulsen, A., Nowak, M.A.: Evolution of Cooperation by Multilevel Selection. Proceedings of the National Academy of Sciences of United States of America, 10952–10955 (2006)Google Scholar
  12. 12.
    Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-Based Differential Evolution. IEEE Transactions on Evolutionary Computation 12, 64–79 (2008)CrossRefGoogle Scholar
  13. 13.
    De Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Function Optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, Honolulu, USA, pp. 699–704 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lei Wang
    • 1
  • Wei Li
    • 1
  • Rong Fei
    • 1
  • Xinghong Hei
    • 1
  1. 1.Faculty of Computer Science and EngineeringXi’an University of TechnologyChina

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