This paper introduces a hybrid evolutionary optimization algorithm as a tool for training an Artificial Neural Network used for production forecasting of solar energy PV plants. This hybrid technique is developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This procedure essentially represent a bio-inspired heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA), but also based on cultural and social behaviours derived from the analysis of the swarm intelligence and interaction among particles (PSO). Some simulation results are reported to highlight advantages and drawbacks of the proposed technique in order to suitably apply this algorithm to neural network applications in engineering problems.