Swarm optimized organizing map (SWOM): A swarm intelligence based optimization of self-organizing map
This work studies the optimization of SOM algorithm in terms of reducing its training time by the use of a swarm intelligence method, i.e. particle swarm optimization (PSO). Our novel algorithm optimizes SOM with PSO and reduces computational time of the training phase of SOM significantly. The performance of the algorithms has been tested with genomic datasets. biomedical datasets and an artificial dataset to show the efficiency of swarm optimized SOM, i.e. SWOM. The experimental comparison between SOM and SWOM, has demonstrated significant reduction in training time of SWOM with preservation of clustering quality. (C) 2009 Elsevier Ltd. All rights reserved.
optimization self-organizing map particle swarm optimization biomedical datasets computational cost genomic pattern finding large data training time reduction