Hybrid Metaheuristic Algorithm (Sagac) Used in Optimization of Vacuum Cooling Treatment of Postharvest Broccoli

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.

show abstract

Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers)

Benvenga

Nääs

Lima

et al. 2022

AgriEngineering

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Hybrid Metaheuristic Algorithm (Sagac) Used in Optimization of Vacuum Cooling Treatment of Postharvest Broccoli

Cited by 1 publication

References 6 publications

Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers)

Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers)

Contact Info

Product

Resources

About