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

摘要

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.