Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Reservoir Operation Management

Metaheuristics are highly efficient optimization methods that are widely used today. However, the performance of one method cannot be generalized and must be examined in each class of problems. The hybrid algorithm of particle swarm optimization and grey wolf optimizer (HPSOGWO) is new swarm-based metaheuristic with several advantages, such as simple implementation and low memory consumption. This study uses HPSOGWO for reservoir operation optimization. Real-coded genetic algorithm (RGA) and gravitational search algorithm (GSA) have been used as efficient methods in reservoir optimization management for comparative analysis between algorithms through two case studies. In the first case study, four benchmark functions were minimized, in which results revealed that HPSOGWO was more competitive compared with other algorithms and can produce high-quality solutions. The second case study involved minimizing the deficit between downstream demand and release from the Hammam Boughrara reservoir located in Northwest Algeria. A constrained optimization model with non-linear objective function was applied. Based on the average solutions, HPSOGWO performed better compared with RGA and was highly competitive with GSA. In addition, the reliability, resiliency, and vulnerability indices of the reservoir operation, which was derived from the three algorithms, were nearly similar to one another, which justified the usability of HPSOGWO in this field.