Coot Optimization Algorithm on Training Artificial Neural Networks

Abstract

In recent years, significant advancements have been made in artificial neural network models and they have been applied to a variety of real-world problems. However, one of the limitations of artificial neural networks is that they can getting stuck in local minima during the training phase, which is a consequence of their use of gradient descent-based techniques. This negatively impacts the generalization performance of the network. In this study, it is proposed a new hybrid artificial neural network model called COOT-ANN, which uses the coot optimization algorithm firstly for optimizing artificial neural networks parameters, a metaheuristic-based approach. The COOT-ANN model does not get stuck in local minima during the training phase due to the use of metaheuristic-based optimization algorithm. The results of the study demonstrate that the proposed method is quite successful in terms of accuracy, cross-entropy, F1-score, and Cohen's Kappa metrics when compared to gradient descent, scaled conjugate gradient, and Levenberg-Marquardt optimization techniques.