ART-based parallel learning of growing SOMs and its application to TSP

Authors:
Tetsunari Oshime;Toshimichi Saito;Hiroyuki Torikai
Affiliations:
Hosei University, Tokyo, Japan;Hosei University, Tokyo, Japan;Hosei University, Tokyo, Japan
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

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.