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An improved Gbest guided artificial bee colony (IGGABC) algorithm for classification and prediction tasks
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posted on 2014-01-01, 00:00 authored by H Shah, T Herawan, R Ghazali, R Naseem, Maslina Abdul Aziz, Jemal AbawajyJemal AbawajyArtificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron (MLP). Normally Gbest Guided Artificial Bee Colony (GGABC) algorithm has strong exploitation process for solving mathematical problems, however the poor exploration creates problems like slow convergence and trapping in local minima. In this paper, the Improved Gbest Guided Artificial Bee Colony (IGGABC) algorithm is proposed for finding global optima. The proposed IGGABC algorithm has strong exploitation and exploration processes. The experimental results show that IGGABC algorithm performs better than that standard GGABC, BP and ABC algorithms for Boolean data classification and time-series prediction tasks.
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Title of book
Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part IVolume
8834Series
Lecture Notes in Computer ScienceChapter number
70Pagination
559 - 569Publisher
Springer International PublishingPlace of publication
Heidelberg, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319126364Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2014, SpringerExtent
77Editor/Contributor(s)
C Loo, K Yap, K Wong, A Teoh, K HuangUsage metrics
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