Application of ant colony algorithm for parameter optimization of water demand prediction model

Abstract

To improve forecast accuracy of water demand when using Projection Pursuit(PP) model which are high-dimensional,non-normality and nonlinear,an Ant Colony Algorithm(ACA) was used for the parameter optimization of the model.ACA was improved to self-adaptive control pheromone on the grids divided by definitional domains of the model parameters.A case for water demand prediction was emulated according to the improved ACA and PP model.Then prediction accuracy from the improved ACA was compared with the results from Artificial Immune Algorithm(AIA) and BP Artificial Neural Network(BPANN) model,respectively.It is shown that:1) the absolute relative errors of fitting accuracy are less than 2% from ACA and less than 10% from AIA and BPANN;2) the absolute relative errors of prediction accuracy are less than 6%,11% and 12% from ACA,AIA and BPANN,respectively;3) ACA can converge to global optimal solution with higher convergence rate.Therefore,the improved ACA for optimizing the parameters of PP water demand prediction model is significantly better than the AIA and BPANN.This method can be applied to other similar high-dimensional and nonlinear problems.