Application and comparison of several intelligent algorithms on Muskingum Routing Model

摘要

In order to solve the problem of linearization, complexity and poor accuracy for parameter estimate of Muskingum Routing Model at present, this paper introduces three modern intelligent algorithms — Genetic Algorithm (GA), Simulated Annealing Algorithm (SA) and Particle Swarm Optimization Algorithm (PSO) for the parameter calibration of Muskingum model. Through specific simulation, the results of five methods are produced. Then according to the calculation, comparison and analysis of five methods comprehensively, it is found that the results of three modern intelligent algorithms are fit significantly and better than traditional methods.