Solving Traveling Salesman Problems by Artificial Immune Response

  • Maoguo Gong
  • Licheng Jiao
  • Lining Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


This paper introduces a computational model simulating the dynamic process of human immune response for solving Traveling Salesman Problems (TSPs). The new model is a quaternion (G, I, R, Al), where G denotes exterior stimulus or antigen, I denotes the set of valid antibodies, R denotes the set of reaction rules describing the interactions between antibodies, and Al denotes the dynamic algorithm describing how the reaction rules are applied to antibody population. The set of immunodominance rules, the set of clonal selection rules, and a dynamic algorithm TSP-PAISA are designed. The immunodominance rules construct an immunodominance set based on the prior knowledge of the problem. The antibodies can gain the immunodominance from the set. The clonal selection rules strengthen these superior antibodies. The experiments indicate that TSP-PAISA is efficient in solving TSPs and outperforms a known TSP algorithm, the evolved integrated self-organizing map.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Maoguo Gong
    • 1
  • Licheng Jiao
    • 1
  • Lining Zhang
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anChina

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