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

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dc.contributor.authorBenvenga, Marco Antonio Campos
dc.contributor.authorNääs, Irenilza de Alencar
dc.contributor.authorLima, Nilsa Duarte da Silva
dc.contributor.authorPereira, Danilo Florentino [UNESP]
dc.contributor.institutionUniversidade Paulista
dc.contributor.institutionFederal University of Roraima
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T16:01:23Z
dc.date.available2023-07-29T16:01:23Z
dc.date.issued2022-12-01
dc.description.abstractBrazil 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.en
dc.description.affiliationGraduate Program in Production Engineering Universidade Paulista, R. Dr. Bacelar 1212
dc.description.affiliationDepartment of Animal Science Federal University of Roraima, BR 174, km 12, Monte Cristo, Boa Vista
dc.description.affiliationDepartment of Management Development and Technology School of Science and Engineering São Paulo State University—UNESP, Av. Domingos da Costa Lopes 780, São Paulo
dc.description.affiliationUnespDepartment of Management Development and Technology School of Science and Engineering São Paulo State University—UNESP, Av. Domingos da Costa Lopes 780, São Paulo
dc.format.extent1171-1183
dc.identifierhttp://dx.doi.org/10.3390/agriengineering4040073
dc.identifier.citationAgriEngineering, v. 4, n. 4, p. 1171-1183, 2022.
dc.identifier.doi10.3390/agriengineering4040073
dc.identifier.issn2624-7402
dc.identifier.scopus2-s2.0-85144736145
dc.identifier.urihttp://hdl.handle.net/11449/249503
dc.language.isoeng
dc.relation.ispartofAgriEngineering
dc.sourceScopus
dc.subjectcomputational intelligence
dc.subjectoptimization
dc.subjectproduction forecast
dc.subjectSAGAC
dc.titleHybrid Metaheuristic Algorithm for Optimizing Monogastric Growth Curve (Pigs and Broilers)en
dc.typeArtigo
unesp.author.orcid0000-0001-6441-1456[1]
unesp.author.orcid0000-0003-0663-9377[2]
unesp.author.orcid0000-0002-1284-7810[3]
unesp.author.orcid0000-0003-4602-8837[4]
unesp.departmentAdministração - Tupãpt

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