A comparative study of different optimization algorithms for the optimum operation of the Mahabad dam reservoir

Abstract

In this study, the performance of the Differential Evolution (DE) Algorithm, the Genetic Algorithm (GA) and Teaching-Learning Based Optimization (TLBO) Algorithm are compared for the optimum operation of the Mahabad dam reservoir. The Reservoir supplies agricultural, environmental, municipal and industrial water demands of the area. The desktop reserve model is used for estimating the minimum environmental demand. Six alternative agricultural water management scenarios are proposed and a sensitivity analysis is performed on the agricultural demands under different scenarios. The results of the study indicate that both DE and GA algorithms performed nearly equally; however, the DE Algorithm reached its result more quickly with the reliability of 74.19 % compared to the results of the GA and TLBO. A 30 % decrease in agricultural demand in the fifth scenario with 87.56 % reliability provides the best results.

Keywords

• Optimal operation
• Differential evolution algorithm
• Genetic algorithm
• Teaching-learning based optimization algorithm
• Environmental flow