This research proposes two-stage method, growing self-organizing map (GSOM) algorithm and bee colony optimization (BCO) based self-organizing map (BSOSOM), to improve SOM performance. In the first stage, GSOM is used to determine the SOM topology and then followed by BCOSOM to fine tune the SOM weights. The proposed BCOSOM algorithm is compared with other algorithms, PSO, BCO, SOM, PSOSOM, SOM+PSO, and SOM+BCO, using four benchmark data sets, Iris, Glass, Wine, and Vowel. The computational result indicates that BCOSOM algorithm is able to find a better solution than other algorithms. Furthermore, the proposed algorithm has been also employed to Group Technology to cluster components into part families for a medical manufacture in Indonesia

This research proposes two-stage method, growing self-organizing map (GSOM) algorithm and bee colony optimization (BCO) based self-organizing map (BSOSOM), to improve SOM performance. In the first stage, GSOM is used to determine the SOM topology and then followed by BCOSOM to fine tune the SOM weights. The proposed BCOSOM algorithm is compared with other algorithms, PSO, BCO, SOM, PSOSOM, SOM+PSO, and SOM+BCO, using four benchmark data sets, Iris, Glass, Wine, and Vowel. The computational result indicates that BCOSOM algorithm is able to find a better solution than other algorithms. Furthermore, the proposed algorithm has been also employed to Group Technology to cluster components into part families for a medical manufacture in Indonesia

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