SOMO-m Optimization Algorithm with Multiple Winners

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 f...

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Main Authors: Wei Wu, Atlas Khan
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2012/969104
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author Wei Wu
Atlas Khan
author_facet Wei Wu
Atlas Khan
author_sort Wei Wu
collection DOAJ
description 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.
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publishDate 2012-01-01
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spelling doaj-art-462b4ad8eb6444ab94d11dc3cefcb66d2025-08-20T03:36:14ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2012-01-01201210.1155/2012/969104969104SOMO-m Optimization Algorithm with Multiple WinnersWei Wu0Atlas Khan1Department of Applied Mathematics, Dalian University of Technology, 116024 Dalian, ChinaDepartment of Applied Mathematics, Dalian University of Technology, 116024 Dalian, ChinaSelf-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.http://dx.doi.org/10.1155/2012/969104
spellingShingle Wei Wu
Atlas Khan
SOMO-m Optimization Algorithm with Multiple Winners
Discrete Dynamics in Nature and Society
title SOMO-m Optimization Algorithm with Multiple Winners
title_full SOMO-m Optimization Algorithm with Multiple Winners
title_fullStr SOMO-m Optimization Algorithm with Multiple Winners
title_full_unstemmed SOMO-m Optimization Algorithm with Multiple Winners
title_short SOMO-m Optimization Algorithm with Multiple Winners
title_sort somo m optimization algorithm with multiple winners
url http://dx.doi.org/10.1155/2012/969104
work_keys_str_mv AT weiwu somomoptimizationalgorithmwithmultiplewinners
AT atlaskhan somomoptimizationalgorithmwithmultiplewinners