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SOMO-m Optimization Algorithm with Multiple Winners
Joint Authors
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-08-05
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wu, Wei& Khan, Atlas. 2012. SOMO-m Optimization Algorithm with Multiple Winners. Discrete Dynamics in Nature and Society،Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-512252
Modern Language Association (MLA)
Wu, Wei& Khan, Atlas. SOMO-m Optimization Algorithm with Multiple Winners. Discrete Dynamics in Nature and Society No. 2012 (2012), pp.1-13.
https://search.emarefa.net/detail/BIM-512252
American Medical Association (AMA)
Wu, Wei& Khan, Atlas. SOMO-m Optimization Algorithm with Multiple Winners. Discrete Dynamics in Nature and Society. 2012. Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-512252
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-512252