Optimal operation of single and multi-reservoir systems via hybrid shuffled grey wolf optimization algorithm (SGWO)

Abstract

Reservoir operation is a key issue in the water resources system. In this paper, the Shuffled Grey Wolf Optimizer (SGWO), a hybrid optimization algorithm inspired by Shuffled Complex Evolution (SCE-UA) and Gray Wolf Optimizer (GWO) algorithms, is introduced. The main modification in the proposed algorithm is how it divides and shuffles the population to enhance the information exchange among the individuals. The performance of the SGWO algorithm is compared to famous evolutionary algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in solving mathematical benchmark functions and multiple types of reservoir operation optimization problems with different scales. Two hypothetical 4 and 10-reservoir system, and the Dez dam in Iran as a single reservoir system were selected as the case study in this research. The capability of the algorithms was compared in terms of accuracy of derived optimum objective function values, convergence speed, and stability of answers in different implementations. The results showed that the SGWO can reach considerably better results (0.3% to 26% better than the closest rival algorithms) using significantly lower number of function evaluations. It also showed the lowest standard deviation among other algorithms for all problems, which indicated the high reliability of this algorithm.