Optimization of response surface and neural network models in conjugation with desirability function for estimation of nutritional needs of methionine, lysine, and threonine in broiler chickens

The optimization algorithm of a model may have significant effects on the final optimal values of nutrient requirements in poultry enterprises. In poultry nutrition, the optimal values of dietary essential nutrients are very important for feed formulation to optimize profit through minimizing feed cost and maximizing bird performance. This study was conducted to introduce a novel multi-objective algorithm, desirability function, for optimization the bird response models based on response surface methodology (RSM) and artificial neural network (ANN). The growth databases on the central composite design (CCD) were used to construct the RSM and ANN models and optimal values for 3 essential amino acids including lysine, methionine, and threonine in broiler chicks have been reevaluated using the desirable function in both analytical approaches from 3 to 16 d of age. Multi-objective optimization results showed that the most desirable function was obtained for ANN-based model (D = 0.99) where the optimal levels of digestible lysine (dLys), digestible methionine (dMet), and digestible threonine (dThr) for maximum desirability were 13.2, 5.0, and 8.3 g/kg of diet, respectively. However, the optimal levels of dLys, dMet, and dThr in the RSM-based model were estimated at 11.2, 5.4, and 7.6 g/kg of diet, respectively. This research documented that the application of ANN in the broiler chicken model along with a multi-objective optimization algorithm such as desirability function could be a useful tool for optimization of dietary amino acids in fractional factorial experiments, in which the use of the global desirability function may be able to overcome the underestimations of dietary amino acids resulting from the RSM model.