Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks
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Date
2015-04
Authors
Abusnaina, Ahmed A. A
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
Developing efficient training method for Neural Networks (NN) in terms of high accuracy
is a challenge. In addition, training NN is still highly-time consuming. The Mussels
Wandering Optimization (MWO) is a recent metaheuristic optimization algorithm inspired
ecologically by mussels movement behavior. The major objective of this thesis is to
achieve better performance in terms of convergence training time and classification
accuracy for pattern classification by proposing new supervised training methods for
Artificial Neural Networks (ANN) based on the MWO algorithm. Increasing the
performance, especially in terms of classification accuracy led to an adapted version of the
MWO; known as Enhanced-MWO (E-MWO) algorithm. Both the original and the
enhanced MWO algorithms are then applied for supervised training of Spiking Neural
Networks (SNN). The merits of the proposed methods are validated empirically by using a
set of benchmark problems, whereas comparisons are conducted against other common
rival training methods. Results show that the MWO-based methods are in average 12 folds
faster than other rival methods in terms of training time. In terms of classification accuracy,
the MWO is on par with other methods with average accuracy of 80.18%, while the EMWO
outperforms other rival methods in 3 problems significantly, and on par with the
other methods in classifying the rest 5 problems using ANN with average accuracy of
86.0%. The E-MWO outperforms other methods significantly in classifying 6 problems out
of 8 problems using SNN as a classifier.
Description
Please contact ‪Ahmad Abusnaina‬ ‪‬ <abusnaina@ymail.com> for full text content.
Keywords
Artificial Neural Networks (ANN)