Optimal parameter settings by evolutionary algorithms for the greenhouse environment model

Optimal parameter settings of the nonlinear complicated model are implemented by an evolutionary algorithms. Depending on matter and energy equilibrium equation, a non,linear and complicated model about greenhouse environment was constructed that take into account the convection, conduction, radiation, plant transpiration, photosynthesis, water condensed from, the air and the equipment states. But it is a difficult problem, to optimize the parameters of the model. The available optimization algorithms are reviewed and compared. The operational parameters of the method found to be most efficient are then discussed. Results are presented for the optimization, of a complex whole-properly model, using this method. With the actual data the model was verified. The results show that the model is correct and the error of the model-forecasting environment is lesser and in allowable bound. So for the efficient optimization, of large complex models of complicated and nonlinear agricultural systems, evolutionary algorithms have proven to be the most efficient and robust of the currently available optimization, methods.