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Recent Advances on Soft Computing and Data Mining pp 183–194Cite as

Enhanced MWO Training Algorithm to Improve Classification Accuracy of Artificial Neural Networks

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 287))

Abstract

The Mussels Wandering Optimization (MWO) algorithm is a novel meta-heuristic optimization algorithm inspired ecologically by mussels’ movement behavior. The MWO algorithm has been used to solve linear and nonlinear functions and it has been adapted in supervised training of Artificial Neural Networks (ANN). Based on the latter application, the classification accuracy of ANN based on MWO training was on par with other algorithms. This paper proposes an enhanced version of MWO algorithm; namely Enhanced-MWO (E-MWO) in order to achieve an improved classification accuracy of ANN. In addition, this paper discusses and analyses the MWO and the effect of MWO parameters selection (especially, the shape parameter) on ANN classification accuracy. The E-MWO algorithm is adapted in training ANN and tested using well-known benchmarking problems and compared against other algorithms. The obtained results indicate that the E-MWO algorithm is a competitive alternative to other evolutionary and gradient-descent based training algorithms in terms of classification accuracy and training time.

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Author information

Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

    Ahmed A. Abusnaina & Rosni Abdullah

  2. IT Department, Ishik University, Erbil, Iraq

    Ali Kattan

Authors
  1. Ahmed A. Abusnaina
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  2. Rosni Abdullah
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  3. Ali Kattan
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Corresponding author

Correspondence to Ahmed A. Abusnaina .

Editor information

Editors and Affiliations

  1. Department of Information System Faculty of Comp. Sci. & Info. Tech., University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Faculty of Comp. Sci. and Info. Tech, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Rozaida Ghazali

  3. Faculty of Comp. Sci. and Info. Tech., Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Mustafa Mat Deris

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© 2014 Springer International Publishing Switzerland

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Abusnaina, A.A., Abdullah, R., Kattan, A. (2014). Enhanced MWO Training Algorithm to Improve Classification Accuracy of Artificial Neural Networks. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_18

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  • DOI: https://doi.org/10.1007/978-3-319-07692-8_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07691-1

  • Online ISBN: 978-3-319-07692-8

  • eBook Packages: EngineeringEngineering (R0)

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