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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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| 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. |
| format | Article |
| id | doaj-art-462b4ad8eb6444ab94d11dc3cefcb66d |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| 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 |