UUM Repository | Universiti Utara Malaysian Institutional Repository
FAQs | Feedback | Search Tips | Sitemap

Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification


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

[img] PDF
Restricted to Registered users only

Download (518kB) | Request a copy

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Swarm Intelligence, Artificial Neural Networks, Artificial Bee Colony Algorithm, Particle Swarm Optimization, Pattern-Classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Mrs. Norazmilah Yaakub
Date Deposited: 29 Apr 2018 01:42
Last Modified: 29 Apr 2018 01:42
URI: http://repo.uum.edu.my/id/eprint/24040

Actions (login required)

View Item View Item