A hybrid optimization method of Multi-Objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) classifier for hydrological model calibration

Abstract

The MOGA is used as automatic calibration method for a wide range of water and environmental simulation models.The task of estimating the entire Pareto set requires a large number of fitness evaluations in a standard MOGA optimization process. However, it's very time consuming to obtain a value of objective functions in many real engineering problems. We propose a unique hybrid method of MOGA and KNN classifier to reduce the number of actual fitness evaluations. The test results for multi-objective calibration show that the proposed method only requires about 30% of actual fitness evaluations of the MOGA. © Springer-Verlag Berlin Heidelberg 2004.