عنوان مقاله [English]
نویسندگان [English]چکیده [English]
In this paper presented the evaluation of particle swarm optimization for solving complex optimization water resources problems. The main problem with PSO is it’s prematurity. Therefore a new adjustable PSO-GA hybrid algorithm which combines PSO with genetic operators was proposed. The basis behind this is that such a hybrid approach is expected to have merits of PSO with those of GA. The main idea of GA is due to its genetic operators crossover and mutation. By applying crossover operation, information can be swapped between two particles to have the ability of flying to the new search area. The purpose of applying mutation to PSO is to increase the diversity of the population. For evaluating of the proposed algorithm the optimization of the hydropower operation of ‘‘Dez” single reservoir has been studied. The results of HPSOGA compare to PSO and GA indicated the proposed algorithm increases the flexibility and capability of PSO to generate strong-developing individuals that can achieve faster convergence rate to optimum point and it is very useful in solving optimization operation water resources.