A Distributed Immune Algorithm for Solving Optimization Problems

  • Mariusz Oszust
  • Marian Wysocki
Part of the Studies in Computational Intelligence book series (SCI, volume 162)


The mammal immune system is a distributed multiagent system. Its properties of distributive control and self organization have created interest in using immune principles to solve complex engineering tasks such as decentralized robot control, pattern recognition, multimodal and combinatorial optimization. In this paper a new immunity-based algorithm for solving optimization problems is proposed. The algorithm differs from the representative immune algorithm CLONALG. The agents participating in distributed problem solving enrich their knowledge about the solution via communication with other agents. Moreover they are decomposed into groups of specialists that can modify only some decision variables and/or use their own method of local improvement of the solution. The empirical results confirming usability of the algorithm and its advantage over CLONALG are presented. Obtained estimates of the global optima of multimodal test functions and traveling salesperson problem (TSP) are closer to the theoretical solutions and require fewer tentative computations.


Decision Variable Multiagent System Clonal Selection Solve Optimization Problem Immune Algorithm 
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.
    Aarts, E., Korst, J.: Simulated Annealing and Boltzmann Machines. John Willey & Sons (1989)Google Scholar
  2. 2.
    De Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation 3, 239–251 (2002)CrossRefGoogle Scholar
  3. 3.
    Carter, J.H.: The Immune System as a Model for Pattern Recognition and Classification. Journal of the American Medical Informatics Association 1, 28–41 (2000)Google Scholar
  4. 4.
    Gutknecht, O., Ferber, J., Michel, F.: The MadKit Agent Platform Architecture. Rapport De Recherche, LIRM, Universite Montpellier, France (2000)Google Scholar
  5. 5.
    Jerne, N.K.: The Immune System. Scientific American 229(1), 52–60 (1973)CrossRefGoogle Scholar
  6. 6.
    Lau, H.Y.K., Wong, V.W.K.: An Immunity-Based Distributed Multiagent-Control Framework. IEEE Transactions on Systems, Man, and Cybernetics - part A: Systems and Humans 1, 91–108 (2006)CrossRefGoogle Scholar
  7. 7.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolutionary Programs. Springer, Heidelberg (1996)Google Scholar
  8. 8.
    Sathyanath, S., Sahin, F.: AISIMAM An Artificial Immune System Based Intelligent Multi Agent Model and its Application to a Mine Detection Problem. In: Proc. ICARIS (2002)Google Scholar
  9. 9.
    Villalobos-Arias, M., Coello Cello, C.A., Hernandez Lerma, O.: Convergence Analysis of Multiobjective Artificial Immune Algorithm. Proc. ICARIS, 226–235 (2004)Google Scholar
  10. 10.
    Wierzchon, S.T.: Artificial Immune Systems. Theory and Applications (in Polish). AOW EXIT, Warszawa (2001)Google Scholar
  11. 11.
    Wierzchon, S.T.: Multimodal optimization with artificial immune system. In: Klopotek, M.A., Michalewicz, Z., Wierzchon, S.T. (eds.) Intelligent Information Systems. Physica-Verlag (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mariusz Oszust
    • 1
  • Marian Wysocki
    • 1
  1. 1.Department of Computer and Control EngineeringRzeszow University of TechnologyRzeszowPoland

Personalised recommendations