Evaluation of Grasshopper Optimization Algorithm for Optimal Operation of Surface Water Reservoirs with Reliability Constraints

The use of metaheuristic optimization algorithms in the optimal management of surface water reservoirs systems has expanded considerably in recent years. The reason for this can be seen in the diversity and number of constraints of these systems, a large number of decision variables, and the multiplicity of objectives of these systems. In this research, the Grasshopper optimization algorithm as one of the new metaheuristic optimization algorithms, has been used for the management of reservoir dams. Dez Reservoir Dam has been selected as a case study for two separate simple operation and hydropower production problems (60, 120, 240 and 480 variables). In addition to the usual constraints on reservoir operation, the time reliability, volume reliability constraints as well as the provision of at least a percentage of the monthly requirement in the months with unmet demands are included in this research. The results of this study showed that in different scenarios, the Grasshopper optimization algorithm was able to improve the optimal solution by about 0.5 to 12%. However, as the number of decision variables increases, the efficiency of this algorithm is reduced compared to the genetic algorithm. Regarding the stability of the solutions, in most cases the Grasshopper optimization algorithm yielded better results. In terms of runtime, the grasshopper algorithm converged about 20 to 50 percent faster than the genetic algorithm. The results of comparing the grasshopper optimization algorithm with genetic algorithm indicate the high capability of this algorithm in solving the reservoir operation optimization problem.