Comparison of Optimization Using Hybrid Genetic Agorithm-Backpropagation and Hybrid Particle Swarm Optimization-Backpropagation for Tide Level Forecasting

Abstract

Evolutionary computation or evolutionary algorithm has been used in many areas. For the last ten years, evolutionary computation became a powerful method to solve problems in the real world. Forecasting is a well-known method to determine the direction of the future for better results. Hybridization between the Genetic Algorithm and Particle Swarm Optimization to Backpropagation Neural Network are applied to forecast tide level data. The experiments based on a comparison of these two algorithms prove that Particle Swarm Optimization with Backpropagation Neural Network is exceeding Genetic Algorithm in measuring tide level forecasting.