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.
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Oshime, T., Saito, T., Torikai, H. (2006). ART-Based Parallel Learning of Growing SOMs and Its Application to TSP. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_112
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DOI: https://doi.org/10.1007/11893028_112
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
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