Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5569
Title: Self-adaptive mussels wandering optimization algorithm with application for artificial neural network training
Authors: Abusnaina, Ahmed A.
Abdullah, Rosni
Kattan, Ali
Keywords: Neural networks (Computer science)
Self-adaptive software
Mathematical optimization
Heuristic algorithms
Signal processing
Artificial intelligence
Issue Date: Feb-2018
Publisher: De Gruyter
Abstract: The mussels wandering optimization (MWO) is a recent population-based metaheuristic optimization algorithm inspired ecologically by mussels’ movement behavior. The MWO has been used successfully for solving several optimization problems. This paper proposes an enhanced version of MWO, known as the enhanced-mussels wandering optimization (E-MWO) algorithm. The E-MWO aims to overcome the MWO shortcomings, such as lack in explorative ability and the possibility to fall in premature convergence. In addition, the E-MWO incorporates the self-adaptive feature for setting the value of a sensitive algorithm parameter. Then, it is adapted for supervised training of artificial neural networks, whereas pattern classification of real-world problems is considered. The obtained results indicate that the proposed method is a competitive alternative in terms of classification accuracy and achieve superior results in training time.
URI: http://hdl.handle.net/20.500.11889/5569
Appears in Collections:Fulltext Publications (BZU Community)

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