Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification
The Artificial Neural Networks Training (ANNT) process is an optimization problem of the weight set which has inspired researchers for a long time. By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks.Traditional neural net...
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my.uum.repo.240402018-04-29T01:42:19Z http://repo.uum.edu.my/24040/ Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification Al Nuaimi, Zakaria Noor Aldeen Mahmood Abdullah, Rosni QA75 Electronic computers. Computer science The Artificial Neural Networks Training (ANNT) process is an optimization problem of the weight set which has inspired researchers for a long time. By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks.Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima.Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues.Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. However, hybrid algorithms are also a fundamental concern in the optimization field, which aim to cumulate the advantages of different algorithms into one algorithm. In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application.The performance of the HPABC algorithm was investigated on four benchmark pattern-classification data sets and the results were compared with other algorithms.The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT.HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy. Universiti Utara Malaysia Press 2017 Article PeerReviewed application/pdf en http://repo.uum.edu.my/24040/1/JICT%2016%202%202017%20314%E2%80%93334.pdf Al Nuaimi, Zakaria Noor Aldeen Mahmood and Abdullah, Rosni (2017) Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification. Journal of Information and Communication Technology, 16 (2). pp. 314-334. ISSN 2180-3862 http://jict.uum.edu.my/index.php/previous-issues/151-journal-of-information-and-communication-technology-jict-vol-16-no-2-december-2017#A5 |
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QA75 Electronic computers. Computer science Al Nuaimi, Zakaria Noor Aldeen Mahmood Abdullah, Rosni Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification |
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The Artificial Neural Networks Training (ANNT) process is an optimization problem of the weight set which has inspired researchers for a long time. By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks.Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima.Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues.Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. However, hybrid algorithms are also a fundamental concern in the optimization field, which aim to cumulate the advantages of different algorithms into one algorithm. In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application.The performance of the HPABC algorithm was investigated on four benchmark pattern-classification data sets and the results were compared with other algorithms.The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT.HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy. |
format |
Article |
author |
Al Nuaimi, Zakaria Noor Aldeen Mahmood Abdullah, Rosni |
author_facet |
Al Nuaimi, Zakaria Noor Aldeen Mahmood Abdullah, Rosni |
author_sort |
Al Nuaimi, Zakaria Noor Aldeen Mahmood |
title |
Neural network training using hybrid particle-move
artificial bee colony algorithm for pattern classification |
title_short |
Neural network training using hybrid particle-move
artificial bee colony algorithm for pattern classification |
title_full |
Neural network training using hybrid particle-move
artificial bee colony algorithm for pattern classification |
title_fullStr |
Neural network training using hybrid particle-move
artificial bee colony algorithm for pattern classification |
title_full_unstemmed |
Neural network training using hybrid particle-move
artificial bee colony algorithm for pattern classification |
title_sort |
neural network training using hybrid particle-move
artificial bee colony algorithm for pattern classification |
publisher |
Universiti Utara Malaysia Press |
publishDate |
2017 |
url |
http://repo.uum.edu.my/24040/1/JICT%2016%202%202017%20314%E2%80%93334.pdf http://repo.uum.edu.my/24040/ http://jict.uum.edu.my/index.php/previous-issues/151-journal-of-information-and-communication-technology-jict-vol-16-no-2-december-2017#A5 |
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