Performance Assessment of Grasshopper Optimization Algorithm For Optimizing Coefficients of Sediment Rating Curve

One of the most common methods for estimating suspended sediment of rivers is sediment rating curve. for better estimation of the amount of suspended sediment based on the sediment curve rating equation, it is possible to optimize its coefficients. one of the methods used for optimizing the coefficients of the sediment curve rating equation is taking advantage of meta-heuristic algorithms. the main objective of this research is the use of grasshopper optimisation algorithm to optimize the relationship between discharge and sediment discharge and comparison the results of this model with genetic algorithms and particle swarm. with respect to the objective function, which minimizes the difference between the measured values of the sediment and the calculated values of that, the optimal values of these coefficients are determined. the results of this research indicated since the objective function, grasshopper optimisation algorithm compared with genetic algorithm and particle swarm optimization has a good performance. so that grasshopper optimisation algorithm with 7694507 values has the best performance in this problem and then pso and ga algorithms with 7702357 and 7703750 have a good performance and finally this value in sediment rating curve is equal to 9163544.