On Neural Networks Modeling Based on GA, PSO and GW Optimization Techniques

Training artificial neural networks (ANNs) using global optimization techniques is becoming an attractive area of research. In this paper, combined modeling techniques of ANNs with genetic algorithm (ANN-GA), particles warm optimization (ANN-PSO) and grey wolf optimization (ANN-GWO) will be presented. The performance of these three techniques will be investigated in terms of efficiency and effectiveness for solving practical modeling problem. It has been found the recently developed ANN-GWO has a comparable performance with respect to the other techniques. ANN-PSO showed higher rate of convergence, which makes it more practical for real-time applications.