Training of feedforward neural networks for data classification using hybrid particle swarm optimization, Mantegna Lévy flight and neighborhood search

Abstract

Artificial Neural networks (ANNs) are often applied to data classification problems. However, training ANNs remains a challenging task due to the large and high dimensional nature of search space particularly in the process of fine-tuning the best set of control parameters in terms of weight and bias. Evolutionary algorithms are proved to be a reliable optimization method for training the parameters. While a number of conventional training algorithms have been proposed and applied to various applications, most of them share the common disadvantages of local optima stagnation and slow convergence. In this paper, we propose a new evolutionary training algorithm referred to as LPSONS, which combines the velocity operators in Particle Swarm Optimization (PSO) with Mantegna Lévy distribution to produce more diverse solutions by dividing the population and generation between different sections of the algorithm. It further combines Neighborhood Search with Mantegna Lévy distribution to mitigate premature convergence and avoid local minima. The proposed algorithm can find optimal results and at the same time avoid stagnation in local optimum solutions as well as prevent premature convergence in training Feedforward Multi-Layer Perceptron (MLP) ANNs. Experiments with fourteen standard datasets from UCI machine learning repository confirm that the LPSONS algorithm significantly outperforms a gradient-based approach as well as some well-known evolutionary algorithms that are also based on enhancing PSO.