ART-Based Parallel Learning of Growing SOMs and Its Application to TSP

  • Tetsunari Oshime
  • Toshimichi Saito
  • Hiroyuki Torikai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


This paper studies parallel learning of growing self-organizing maps ( GSOMs ) and its application to traveling sales person problems ( TSPs ). Input space of city positions are divided into subspaces automatically through adaptive resonance theory ( ART ) map. One GSOM is allocated to each subspace and grows following input data. After all the GSOMs grow sufficiently they are fused and we obtain a tour. The algorithm performance can be controlled by four parameters: the number of subspaces, insertion interval, learning coefficient and final number of cells. In basic experiments for a data-set of 929 cities we can find semi-optimal solution much faster than serial methods although there exist trade-off between tour length and execution time.


Input Space Travel Salesman Problem Adaptive Resonance Theory Tour Length Fuzzy Adaptive Resonance Theory 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tetsunari Oshime
    • 1
  • Toshimichi Saito
    • 1
  • Hiroyuki Torikai
    • 1
  1. 1.Hosei UniversityTokyoJapan

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