Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers)
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 resourc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/4/4/73 |
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| author | Marco Antonio Campos Benvenga Irenilza de Alencar Nääs Nilsa Duarte da Silva Lima Danilo Florentino Pereira |
| author_facet | Marco Antonio Campos Benvenga Irenilza de Alencar Nääs Nilsa Duarte da Silva Lima Danilo Florentino Pereira |
| author_sort | Marco Antonio Campos Benvenga |
| collection | DOAJ |
| description | 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 R<sup>2</sup> above 99%, and the SAGAC for pigs showed R<sup>2</sup> above 94% for the growth curve. |
| first_indexed | 2024-03-09T17:25:59Z |
| format | Article |
| id | doaj.art-65cd1927e86346c4bcbd7c12c78f2d47 |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-03-09T17:25:59Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-65cd1927e86346c4bcbd7c12c78f2d472023-11-24T12:43:05ZengMDPI AGAgriEngineering2624-74022022-11-01441171118310.3390/agriengineering4040073Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers)Marco Antonio Campos Benvenga0Irenilza de Alencar Nääs1Nilsa Duarte da Silva Lima2Danilo Florentino Pereira3Graduate Program in Production Engineering, Universidade Paulista, R. Dr. Bacelar 1212, São Paulo 04026-002, BrazilGraduate Program in Production Engineering, Universidade Paulista, R. Dr. Bacelar 1212, São Paulo 04026-002, BrazilDepartment of Animal Science, Federal University of Roraima, BR 174, km 12, Monte Cristo, Boa Vista 69300-000, BrazilDepartment of Management, Development and Technology, School of Science and Engineering, São Paulo State University—UNESP, Av. Domingos da Costa Lopes 780, Tupã, São Paulo 17602-496, BrazilBrazil 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 R<sup>2</sup> above 99%, and the SAGAC for pigs showed R<sup>2</sup> above 94% for the growth curve.https://www.mdpi.com/2624-7402/4/4/73computational intelligenceoptimizationproduction forecastSAGAC |
| spellingShingle | Marco Antonio Campos Benvenga Irenilza de Alencar Nääs Nilsa Duarte da Silva Lima Danilo Florentino Pereira Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers) AgriEngineering computational intelligence optimization production forecast SAGAC |
| title | Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers) |
| title_full | Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers) |
| title_fullStr | Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers) |
| title_full_unstemmed | Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers) |
| title_short | Hybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers) |
| title_sort | hybrid metaheuristic algorithm for optimizing monogastric growth curve pigs and broilers |
| topic | computational intelligence optimization production forecast SAGAC |
| url | https://www.mdpi.com/2624-7402/4/4/73 |
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