This research is intended to evaluate the Particle Swarm Optimization (PSO) algorithms for solving complex problems of water resources management. To achieve the goal, the standard particle swarm optimization algorithm and the modified method named Elitist-Mutation Particle Swarm Optimization (EMPSO) are used to determine optimal operating of a single reservoir system with 504 decision variables. The two methods were compared and contrasted with other meta-heuristic methods such as Genetic Algorithm (GA), and original and modified Ant Colony Optimization in continuous domains (ACO R ). The results indicated that the use of EMPSO in complex problems is remarkably superior to the PSO in terms of run time and the optimal value of objective function. Moreover, EMPSO was found comparable to other above stated meta-heuristic methods.