Discrete and Continuous Aspects of Nature Inspired Methods

  • Martin Macaš
  • Miroslav Burša
  • Lenka Lhotská
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


In nature, industry, medicine, social environment, simply everywhere we find a lot of data that bear certain information. A dictionary defines data as facts or figures from which conclusions may be drawn. Data can be classified as either numeric or nonnumeric. The structure and nature of data greatly affects the choice of analysis method. Under the term structure we understand the facts that the data might be not a single number but n-tuples of measurements. Structure is also very closely linked to the reason of data collection and method of measurement. The paper describes the similarities and differences of nature inspired methods and their natural counterparts in light of continuous and discrete properties. Different examples of nature inspired methods are inspected in terms of data, problem domains and inner structure and principles.


Particle Swarm Optimization Travelling Salesman Problem Discrete Particle Swarm Optimization Discrete Property Real Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    McCulloch, W.S., Pitts, W.H.: A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1945 (1995)Google Scholar
  3. 3.
    Boyd, R., Richardson, P.J.: Culture and Evolutionary Process. University of Chicago Press, Chicago (1985)Google Scholar
  4. 4.
    Clerk, M.: Discrete Particle Swarm Optimization: A Fuzzy Combinatorial Black Box (2000),
  5. 5.
    Pang, W., Wang, K.-p., Zhou, C.-g., Dong, L.-j.: Fuzzy Discrete Particle Swarm Optimization for Solving Travelling Salesman Problem, In: Proceedings of the Fourth International Conference on Computer and Information Technology (CIT 2004) (2004) Google Scholar
  6. 6.
    Hendtlass, T., Rodgers, T.: Discrete Evaluation and The Particle Swarm Algorithm. In: The 7th Asia-Pacific Conference on Complex Systems (2004)Google Scholar
  7. 7.
    Deneubourg, J.-L., Aron, S., Goss, S., Pasteels, J.-M.: The Self-Organizing Exploratory Pattern of the Argentine Ant. J. Insect Behavior 3 (1990)Google Scholar
  8. 8.
    Deneubourg, J.-L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The Dynamics of Collective Sorting: Robot-Like Ant and Ant-Like Robot. In: Meyer, J.A., Wilson, S.W. (eds.) Proceedings First Conference on Simulation of Adaptive Behavior: From Animals to Animats, pp. 356–365. MIT Press, Cambridge (1991)Google Scholar
  9. 9.
    Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: optimization by a Colony of Cooperating Agents. IEEE Trans. Syst. Man Cybern. B 26, 29–41 (1996)CrossRefGoogle Scholar
  10. 10.
    Bilchev, G., Parmee, I.C.: The Ant Colony Metaphor for Searchin Continuous Design Spaces. In: Fogarty, T.C. (ed.) AISB-WS 1995. LNCS, vol. 993, pp. 25–39. Springer, Heidelberg (1995)Google Scholar
  11. 11.
    Schoonderwoerd, R.O., Holland, J., Bruten, J., Rothkrantz, L.: Ant-Based Load Balancing in Telecommunications Networks. Adapt. Behav. 5, 169–207 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Martin Macaš
    • 1
  • Miroslav Burša
    • 1
  • Lenka Lhotská
    • 1
  1. 1.Gerstner LaboratoryCzech Technical University in PraguePrague 6Czech Republic

Personalised recommendations