Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers

This study presents the comparison of various evolutionary computation (EC) optimization techniques applied to train the noise-injected multi-layer perceptron neural networks used for estimation of longitudinal dispersion coefficient in rivers. The special attention is paid to recently developed variants of Differential Evolution (DE) algorithm. The most commonly used gradient-based optimization methods have two significant drawbacks: they cannot cope with non-differentiable problems and quickly converge to local optima. These problems can be avoided by the application of EC techniques. Although a great amount of various EC algorithms have been proposed in recent years, only some of them have been applied to neural network training – usually with no comparison to other methods. We restrict our comparison to the regression problem with limited data and noise injection technique used to avoid premature convergence and to improve robustness of the model. The optimization methods tested in the present paper are: Distributed DE with Explorative–Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization, Efficient Population Utilization Strategy Particle Swarm Optimization and Covariance Matrix Adaptation – Evolution Strategy.