somo-m optimization algorithm with multiple winners

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ID: 250661
2012
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Abstract
Self-organizing map (SOM) neural networks have been widely applied in information sciences. In particular, Su and Zhao proposes in (2009) an SOM-based optimization (SOMO) algorithm in order to find a wining neuron, through a competitive learning process, that stands for the minimum of an objective function. In this paper, we generalize the SOM-based optimization (SOMO) algorithm to so-called SOMO-m algorithm with m winning neurons. Numerical experiments show that, for m>1, SOMO-m algorithm converges faster than SOM-based optimization (SOMO) algorithm when used for finding the minimum of functions. More importantly, SOMO-m algorithm with m≥2 can be used to find two or more minimums simultaneously in a single learning iteration process, while the original SOM-based optimization (SOMO) algorithm has to fulfil the same task much less efficiently by restarting the learning iteration process twice or more times.
Reference Key
wu2012discretesomo-m Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Wei Wu;Atlas Khan
Journal Journal of the American Heart Association
Year 2012
DOI
10.1155/2012/969104
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