Comparison of PSO and DE for Training Neural Networks

The use of computational resources required for Feed-Forward Artificial Neural Network (FFANN) training phase by means of classical techniques such as the back propagation learning rule can be prohibitive in some applications. A good training phase is needed for a high performance of a neural network. In searching for alternative methods for training phase of FFANN, some metaheuristic techniques have been used to do this task. This paper compares the performance of Particle Swarm Optimization (PSO) and Differential Evolution (DE) as training methods for FFANN under several well-known pattern recognition instances.