optimal parameter estimation of linear muskingum model based on the modified honey bee mating optimization algorithm

the linear muskingum model is not only a simple conceptual but also effective method for flood routing. since the muskingum model parameters cannot be determined physically, the calibration of these parameters is the only challenge of this method. although various techniques have been proposed to estimate the related parameters, an accurate method with faster convergence rate is still required to improve the computational precision for parameter estimation. in this study, the modified honey bee mating optimization (mhbmo) is proposed for the estimation of the mentioned parameters. the utilized algorithm is applied for a case study from previous literature and the results are compared with the other proposed methods. the results indicate that not only the mhbmo algorithm converges so quickly but also it captures the best optimal parameters values.