Parameter Estimation of the Nonlinear Muskingum Model Using Parameter-Setting-Free Harmony Search

Abstract

Although phenomenon-mimicking algorithms, such as genetic algorithms, particle swarm optimization, and harmony search, have overcome the disadvantages of mathematical algorithms, such as the nonlinear least-squares method, segmented least-squares method, Lagrange multiplier method, a hybrid of pattern search and local search, and the Broyden-Fletcher-Goldfarb-Shanno technique, the algorithms have an inherent shortcoming. They require a tedious and skillful parameter-setting process for the algorithm parameters, such as the crossover rate, mutation rate, acceleration coefficients, harmony memory considering rate, and pitch-adjusting rate. Thus, this study proposes a novel parameter-setting-free technique interfaced with a harmony search algorithm and applies it to the parameter estimation of the nonlinear Muskingum model, which is an optimization problem with continuous decision variables. Results show that the proposed technique found good model parameter values while outperforming a classical harmony search algorithm with fixed algorithm parameter values.