Brazil is one of the world's biggest monogastric producers and exporters (of pig and broiler meat). Farmers need to improve their production planning through the reliability of animal growth forecasts. Predicting pig and broiler growth is optimizing production planning, minimizing the use of resources, and forecasting meat production. The present study aims to apply a hybrid metaheuristic algorithm (SAGAC) to find the best combination of values for the growth curve model parameters for monogastric farm animals (pigs and broilers). We propose a hybrid method to optimize the growth curve model parameters by combining two metaheuristic algorithms Simulated Annealing (SA) and Genetic Algorithm (GA), with the inclusion of a function to promote the acceleration of the convergence (GA + AC) of the results. The idea was to improve the coefficient of determination of these models to achieve better production planning and minimized costs. Two datasets with age (day) and average weight (kg) were obtained. We tested three growth curves: Gompertz, Logistic, and von Bertalanffy. After 300 performed assays, experimental data were tabulated and organized, and a descriptive analysis was completed. Results showed that the SAGAC algorithm provided better results than previous estimations, thus improving the predictive data on pig and broiler production consistency. Using SAGAC to optimize the growth parameter models for pigs and broilers led to optimizing the results with the nondeterministic polynomial time (NP-hardness) of the studied functions. All tuning of the growth curves using the proposed SAGAC method for broilers presented R2 above 99%, and the SAGAC for pigs showed R2 above 94% for the growth curve.

The present study aimed to apply the Simulated Annealing (SA) optimization algorithm to find the ideal control of broiler housing rearing environment at 21, 28, 35, and 42 days of growth. Data from four types of houses using environmental control and similar flock density were recorded weekly in the morning and afternoon, during two seasons (summer and winter). The variables related to environmental and air quality data (temperature, relative humidity, air velocity, ammonia, and carbon dioxide concentrations) were registered and organized into the database to provide a descriptive analysis. The ideal rearing conditions were established as a goal, and we used the Simulated Annealing optimization algorithm to process the data. Such an approach may be applied in the cases that the ideal condition of optimization has multiple objectives, and when each variable is the result of a process. The model was implemented considering the optimal controlled environmental condition that depends on the age of broilers. Results indicated that there was a large dispersion of the data collected from the environmental variables. The process suggested that the optimized functions lead to absolute values obtained by the algorithm for each of the environmental factors of the controlled environmental system, representing the optimal condition of the environment found for each broiler age, considering the interactions of the variables. The maximum optimization was prominent to 21 and 35-d old birds, representing 40-48% of the improvement of the process. 28 and 42-d old birds might benefit from the controlled environmental optimization process by up to 30%.

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