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

Oshime, Tetsunari; Saito, Toshimichi; Torikai, Hiroyuki

doi:10.1007/11893028_112

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2007

2011

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Cited by 6 publications

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“…Our aim is not finding an optimal solution of TSP but providing a trigger to develop flexible parallel processing systems based on the collaboration. Preliminary results along these lines can be found in [11]. Figure 1(a) illustrates an input space consisting of city positions.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Collaboration of Growing Self-Organizing Maps and Adaptive Resonance Theory Maps

Takanashi¹,

Torikai²,

Saito

2007

IEICE Transactions on Fundamentals of Electronics, Communicatio

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Collaboration of growing self-organizing maps (GSOM) and adaptive resonance theory maps (ART) is considered through traveling sales-person problems (TSP).The ART is used to parallelize the GSOM: it divides the input space of city positions into subspaces automatically. One GSOM is allocated to each subspace and grows following the input data. After all the GSOMs grow sufficiently they are connected and we obtain a tour. Basic experimental results suggest that we can find semi-optimal solution much faster than serial methods.

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Section: Introductionmentioning

confidence: 99%