Solving traveling salesman problems by artificial immune response

Authors:
Maoguo Gong;Licheng Jiao;Lining Zhang
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

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Abstract

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 Aldenotes 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.