Intelligent Systems for Public Health: A Multi-Agent System for Culturally Tailored Dietary Policy
Resumo
This study presents a multi-agent approach for culturally tailored dietary recommendation, aimed at the prevention of chronic non-communicable diseases. Based on real-world consumption data from the Brazilian Household Budget Survey (POF), the algorithm generates individualized meal plans that respect socioeconomic and cultural eating habits. Each individual is modeled as an autonomous agent with specific nutritional needs, dietary preferences, access to foods, and interacts socially through ally and enemy dynamics. The optimization process uses mutation, crossover, social interaction and selection to iteratively improve dietary recommendations while respecting individual dietary preferences. Results across hundreds of individuals demonstrated an average increase in nutritional adequacy from 68% to 96% in two optimization iterations. The approach also penalizes excessive nutrient intake, ensuring balanced recommendations. This method provides a scalable and explainable strategy to generate personalized dietary plans that are both nutritionally adequate and culturally sensitive for a real population, with potential applications in public health, policy planning, and nutrition education.
Palavras-chave:
Artificial Intelligence, Heuristic, Evolutionary Algorithm, Nutrition, Public Health
Referências
E. Verly, D. M. Marchioni, M. C. Araujo, E. D. Carli, D. C. R. S. d. Oliveira, E. M. Yokoo, R. Sichieri, and R. A. Pereira, “Evolution of energy and nutrient intake in brazil between 2008–2009 and 2017–2018,” Revista de Saúde Pública, vol. 55, no. Supl 1, p. 5s, 2021.
A. Mendoza-Velázquez, J. Lara-Arévalo, K. B. Siqueira, M. Guzmán-Rodríguez, and A. Drewnowski, “Affordable nutrient density in brazil: nutrient profiling in relation to food cost and nova category assignments,” Nutrients, vol. 14, no. 20, p. 4256, 2022.
G. J. Petot, C. Marling, and L. Sterling, “An artificial intelligence system for computer-assisted menu planning,” Journal of the American Dietetic Association, vol. 98, no. 9, pp. 1009–1014, 1998.
K. S. Coelho, E. B. Giuntini, O. D. Betazzi, M. A. Horst, J. da Silva Dias, B. D. G. de Melo Franco, E. W. de Menezes, F. M. Lajolo, and E. Purgatto, “Nutripersona: Conception of a computational tool for elaboration of personalized menu from a brazilian food composition database,” Journal of Food Composition and Analysis, vol. 123, p. 105582, 2023.
W. Raghupathi and V. Raghupathi, “An empirical study of chronic diseases in the united states: a visual analytics approach to public health,” International journal of environmental research and public health, vol. 15, no. 3, p. 431, 2018.
S. M. Fanelli, S. S. Jonnalagadda, J. L. Pisegna, O. J. Kelly, J. L. Krok-Schoen, and C. A. Taylor, “Poorer diet quality observed among us adults with a greater number of clinical chronic disease risk factors,” Journal of primary care & community health, vol. 11, p. 2150132720945898, 2020.
C. Türkmeno˘glu, A. S¸ . Etaner Uyar, and B. Kiraz, “Recommending healthy meal plans by optimising nature-inspired many-objective diet problem,” Health Informatics Journal, vol. 27, no. 1, p. 1460458220976719, 2021.
K. Zioutos, H. Kondylakis, and K. Stefanidis, “Healthy personalized recipe recommendations for weekly meal planning,” Computers, vol. 13, no. 1, p. 1, 2023.
S. G. Garille and S. I. Gass, “Stigler’s diet problem revisited,” Operations Research, vol. 49, no. 1, pp. 1–13, 2001.
M. R. S. Reis, “Programação linear e o problema da dieta,” Master’s thesis, Universidade Federal de Sergipe, 2023.
E. Verly-Jr, R. Sichieri, N. Darmon, M. Maillot, and F. M. Sarti, “Planning dietary improvements without additional costs for low-income individuals in brazil: linear programming optimization as a tool for public policy in nutrition and health,” Nutrition journal, vol. 18, pp. 1–12, 2019.
E. Verly-Jr, A. M. de Carvalho, D. M. L. Marchioni, and N. Darmon, “The cost of eating more sustainable diets: A nutritional and environmental diet optimisation study,” Global public health, vol. 17, no. 6, pp. 1073–1086, 2022.
E. Verly-Jr, A. da Silva Pereira, E. S. Marques, P. M. Horta, D. S. Canella, and D. B. Cunha, “Reducing ultra-processed foods and increasing diet quality in affordable and culturally acceptable diets: a study case from brazil using linear programming,” British Journal of Nutrition, vol. 126, no. 4, pp. 572–581, 2021.
P. Ducrot, C. Méjean, V. Aroumougame, G. Ibanez, B. Allès, E. Kesse-Guyot, S. Hercberg, and S. Péneau, “Meal planning is associated with food variety, diet quality and body weight status in a large sample of french adults,” International journal of behavioral nutrition and physical activity, vol. 14, pp. 1–12, 2017.
V. Espín, M. V. Hurtado, and M. Noguera, “Nutrition for elder care: a nutritional semantic recommender system for the elderly,” Expert Systems, vol. 33, no. 2, pp. 201–210, 2016.
T. Cioara, I. Anghel, I. Salomie, L. Barakat, S. Miles, D. Reidlinger, A. Taweel, C. Dobre, and F. Pop, “Expert system for nutrition care process of older adults,” Future Generation Computer Systems, vol. 80, pp. 368–383, 2018.
A. K. Sahoo, C. Pradhan, R. K. Barik, and H. Dubey, “Deepreco: deep learning based health recommender system using collaborative filtering,” Computation, vol. 7, no. 2, p. 25, 2019.
P. Forbes and M. Zhu, “Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation,” in Proceedings of the fifth ACM conference on Recommender systems, 2011, pp. 261–264.
O. Chávez-Bosquez, J. Marchi, and P. P. Parra, “Nutritional menu planning: A hybrid approach and preliminary tests.” Res. Comput. Sci., vol. 82, no. 1, pp. 93–104, 2014.
W. D. H. Wijekoon and S. Harshanath, “Meal preparation algorithm for diabetic patients using machine learning,” Sri Lankan Journal of Applied Sciences, vol. 1, no. 02, pp. 27–33, 2023.
K. R. Pawar, T. Ghorpade, and R. Shedge, “Constraint based recipe recommendation using forward checking algorithm,” in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016, pp. 1474–1478.
J.-M. Ramos-Pérez, G. Miranda, E. Segredo, C. León, and C. Rodríguez-León, “Application of multi-objective evolutionary algorithms for planning healthy and balanced school lunches,” Mathematics, vol. 9, no. 1, p. 80, 2020.
A. Marrero, E. Segredo, C. León, and C. Segura, “A memetic decomposition-based multi-objective evolutionary algorithm applied to a constrained menu planning problem,” Mathematics, vol. 8, no. 11, p. 1960, 2020.
B. L. R. Milagres, “Uma abordagem multi-objetivo do problema de planejamento de cardápios voltada para escolas de ensino básico de minas gerais.” Master’s thesis, Universidade Federal de Ouro Preto, 2023.
E. Kaldirim and Z. Kose, “Application of a multi-objective genetic algorithm to the modified diet problem,” in Genetic and Evolutionary Computation Conference (GECCO), vol. 6, 2006.
E. M. Porras, A. C. Fajardo, and R. P. Medina, “Solving dietary planning problem using particle swarm optimization with genetic operators,” in Proceedings of the 3rd international conference on machine learning and soft computing, 2019, pp. 55–59.
C. R. Xavier, J. G. R. Silva, G. R. Duarte, I. A. Carvalho, V. d. F. Vieira, and L. Goliatt, “An island-based hybrid evolutionary algorithm for caloric-restricted diets,” Evolutionary Intelligence, vol. 16, no. 2, pp. 553–564, 2023.
S. H. Amin, S. Mulligan-Gow, and G. Zhang, “Problem using a multiobjective approach under uncertainty,” Application of decision science in business and management, p. 181, 2020.
C. Türkmeno˘glu, A. S¸ . Uyar, and B. Kiraz, “Fuzzy inference based a posterior decision-making for multi-objective diet optimization problem,” Avrupa Bilim ve Teknoloji Dergisi, vol. 45, no. 2214-A, pp. 41–47, 2022.
H. Eghbali, M. A. Eghbali, and A. V. Kamyad, “Optimizing human diet problem based on price and taste using multi-objective fuzzy linear programming approach,” An International Journal of Optimization and Control: Theories & Applications (IJOCTA), vol. 2, no. 2, pp. 139–151, 2012.
Y. Shen, J. Wu, M. Ma, X. Du, H. Wu, X. Fei, and D. Niu, “Improved differential evolution algorithm based on cooperative multi-population,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108149, 2024.
B.-W. Xiang, Y.-X. Xiang, and T.-Y. Zhang, “Rabbit algorithm for global optimization,” Applied Mathematical Modelling, vol. 140, p. 115860, 2025.
A. Alizadeh, F. S. Gharehchopogh, M. Masdari, and A. Jafarian, “A hybrid multi-population optimization algorithm for global optimization and its application on stock market prediction,” Computational Economics, pp. 1–46, 2024.
J. Who and F. E. Consultation, “Diet, nutrition and the prevention of chronic diseases,” World Health Organ Tech Rep Ser, vol. 916, no. i-viii, pp. 1–149, 2003.
S. C. on the Scientific Evaluation of Dietary Reference Intakes, S. on Interpretation, U. of Dietary Reference Intakes, S. on Upper Reference Levels of Nutrients, P. on the Definition of Dietary Fiber, and P. on Macronutrients, Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. National Academies Press, 2005.
E. Cuevas, J. Gálvez, K. Avila, M. Toski, and V. Rafe, “A new metaheuristic approach based on agent systems principles,” Journal of Computational Science, vol. 47, p. 101244, 2020.
B. B. Gatto, M. A. Mollinetti, E. M. dos Santos, A. L. Koerich, and W. S. da Silva Junior, “A novel genetic algorithm approach for discriminative subspace optimization,” in Brazilian Conference on Intelligent Systems. Springer, 2024, pp. 64–79.
E. I. A. Alonso, K. V. Delgado, and F. C. B. dos Santos, “Combining clustering and genetic algorithms for portfolio optimization: A case study with b3 companies,” in Brazilian Conference on Intelligent Systems. Springer, 2024, pp. 141–156.
E. A. Melo, P. C. Jaime, and C. A. Monteiro, Guia alimentar para a população brasileira. Brasília: Ministério da Saúde, 2014.
A. Mendoza-Velázquez, J. Lara-Arévalo, K. B. Siqueira, M. Guzmán-Rodríguez, and A. Drewnowski, “Affordable nutrient density in brazil: nutrient profiling in relation to food cost and nova category assignments,” Nutrients, vol. 14, no. 20, p. 4256, 2022.
G. J. Petot, C. Marling, and L. Sterling, “An artificial intelligence system for computer-assisted menu planning,” Journal of the American Dietetic Association, vol. 98, no. 9, pp. 1009–1014, 1998.
K. S. Coelho, E. B. Giuntini, O. D. Betazzi, M. A. Horst, J. da Silva Dias, B. D. G. de Melo Franco, E. W. de Menezes, F. M. Lajolo, and E. Purgatto, “Nutripersona: Conception of a computational tool for elaboration of personalized menu from a brazilian food composition database,” Journal of Food Composition and Analysis, vol. 123, p. 105582, 2023.
W. Raghupathi and V. Raghupathi, “An empirical study of chronic diseases in the united states: a visual analytics approach to public health,” International journal of environmental research and public health, vol. 15, no. 3, p. 431, 2018.
S. M. Fanelli, S. S. Jonnalagadda, J. L. Pisegna, O. J. Kelly, J. L. Krok-Schoen, and C. A. Taylor, “Poorer diet quality observed among us adults with a greater number of clinical chronic disease risk factors,” Journal of primary care & community health, vol. 11, p. 2150132720945898, 2020.
C. Türkmeno˘glu, A. S¸ . Etaner Uyar, and B. Kiraz, “Recommending healthy meal plans by optimising nature-inspired many-objective diet problem,” Health Informatics Journal, vol. 27, no. 1, p. 1460458220976719, 2021.
K. Zioutos, H. Kondylakis, and K. Stefanidis, “Healthy personalized recipe recommendations for weekly meal planning,” Computers, vol. 13, no. 1, p. 1, 2023.
S. G. Garille and S. I. Gass, “Stigler’s diet problem revisited,” Operations Research, vol. 49, no. 1, pp. 1–13, 2001.
M. R. S. Reis, “Programação linear e o problema da dieta,” Master’s thesis, Universidade Federal de Sergipe, 2023.
E. Verly-Jr, R. Sichieri, N. Darmon, M. Maillot, and F. M. Sarti, “Planning dietary improvements without additional costs for low-income individuals in brazil: linear programming optimization as a tool for public policy in nutrition and health,” Nutrition journal, vol. 18, pp. 1–12, 2019.
E. Verly-Jr, A. M. de Carvalho, D. M. L. Marchioni, and N. Darmon, “The cost of eating more sustainable diets: A nutritional and environmental diet optimisation study,” Global public health, vol. 17, no. 6, pp. 1073–1086, 2022.
E. Verly-Jr, A. da Silva Pereira, E. S. Marques, P. M. Horta, D. S. Canella, and D. B. Cunha, “Reducing ultra-processed foods and increasing diet quality in affordable and culturally acceptable diets: a study case from brazil using linear programming,” British Journal of Nutrition, vol. 126, no. 4, pp. 572–581, 2021.
P. Ducrot, C. Méjean, V. Aroumougame, G. Ibanez, B. Allès, E. Kesse-Guyot, S. Hercberg, and S. Péneau, “Meal planning is associated with food variety, diet quality and body weight status in a large sample of french adults,” International journal of behavioral nutrition and physical activity, vol. 14, pp. 1–12, 2017.
V. Espín, M. V. Hurtado, and M. Noguera, “Nutrition for elder care: a nutritional semantic recommender system for the elderly,” Expert Systems, vol. 33, no. 2, pp. 201–210, 2016.
T. Cioara, I. Anghel, I. Salomie, L. Barakat, S. Miles, D. Reidlinger, A. Taweel, C. Dobre, and F. Pop, “Expert system for nutrition care process of older adults,” Future Generation Computer Systems, vol. 80, pp. 368–383, 2018.
A. K. Sahoo, C. Pradhan, R. K. Barik, and H. Dubey, “Deepreco: deep learning based health recommender system using collaborative filtering,” Computation, vol. 7, no. 2, p. 25, 2019.
P. Forbes and M. Zhu, “Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation,” in Proceedings of the fifth ACM conference on Recommender systems, 2011, pp. 261–264.
O. Chávez-Bosquez, J. Marchi, and P. P. Parra, “Nutritional menu planning: A hybrid approach and preliminary tests.” Res. Comput. Sci., vol. 82, no. 1, pp. 93–104, 2014.
W. D. H. Wijekoon and S. Harshanath, “Meal preparation algorithm for diabetic patients using machine learning,” Sri Lankan Journal of Applied Sciences, vol. 1, no. 02, pp. 27–33, 2023.
K. R. Pawar, T. Ghorpade, and R. Shedge, “Constraint based recipe recommendation using forward checking algorithm,” in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016, pp. 1474–1478.
J.-M. Ramos-Pérez, G. Miranda, E. Segredo, C. León, and C. Rodríguez-León, “Application of multi-objective evolutionary algorithms for planning healthy and balanced school lunches,” Mathematics, vol. 9, no. 1, p. 80, 2020.
A. Marrero, E. Segredo, C. León, and C. Segura, “A memetic decomposition-based multi-objective evolutionary algorithm applied to a constrained menu planning problem,” Mathematics, vol. 8, no. 11, p. 1960, 2020.
B. L. R. Milagres, “Uma abordagem multi-objetivo do problema de planejamento de cardápios voltada para escolas de ensino básico de minas gerais.” Master’s thesis, Universidade Federal de Ouro Preto, 2023.
E. Kaldirim and Z. Kose, “Application of a multi-objective genetic algorithm to the modified diet problem,” in Genetic and Evolutionary Computation Conference (GECCO), vol. 6, 2006.
E. M. Porras, A. C. Fajardo, and R. P. Medina, “Solving dietary planning problem using particle swarm optimization with genetic operators,” in Proceedings of the 3rd international conference on machine learning and soft computing, 2019, pp. 55–59.
C. R. Xavier, J. G. R. Silva, G. R. Duarte, I. A. Carvalho, V. d. F. Vieira, and L. Goliatt, “An island-based hybrid evolutionary algorithm for caloric-restricted diets,” Evolutionary Intelligence, vol. 16, no. 2, pp. 553–564, 2023.
S. H. Amin, S. Mulligan-Gow, and G. Zhang, “Problem using a multiobjective approach under uncertainty,” Application of decision science in business and management, p. 181, 2020.
C. Türkmeno˘glu, A. S¸ . Uyar, and B. Kiraz, “Fuzzy inference based a posterior decision-making for multi-objective diet optimization problem,” Avrupa Bilim ve Teknoloji Dergisi, vol. 45, no. 2214-A, pp. 41–47, 2022.
H. Eghbali, M. A. Eghbali, and A. V. Kamyad, “Optimizing human diet problem based on price and taste using multi-objective fuzzy linear programming approach,” An International Journal of Optimization and Control: Theories & Applications (IJOCTA), vol. 2, no. 2, pp. 139–151, 2012.
Y. Shen, J. Wu, M. Ma, X. Du, H. Wu, X. Fei, and D. Niu, “Improved differential evolution algorithm based on cooperative multi-population,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108149, 2024.
B.-W. Xiang, Y.-X. Xiang, and T.-Y. Zhang, “Rabbit algorithm for global optimization,” Applied Mathematical Modelling, vol. 140, p. 115860, 2025.
A. Alizadeh, F. S. Gharehchopogh, M. Masdari, and A. Jafarian, “A hybrid multi-population optimization algorithm for global optimization and its application on stock market prediction,” Computational Economics, pp. 1–46, 2024.
J. Who and F. E. Consultation, “Diet, nutrition and the prevention of chronic diseases,” World Health Organ Tech Rep Ser, vol. 916, no. i-viii, pp. 1–149, 2003.
S. C. on the Scientific Evaluation of Dietary Reference Intakes, S. on Interpretation, U. of Dietary Reference Intakes, S. on Upper Reference Levels of Nutrients, P. on the Definition of Dietary Fiber, and P. on Macronutrients, Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. National Academies Press, 2005.
E. Cuevas, J. Gálvez, K. Avila, M. Toski, and V. Rafe, “A new metaheuristic approach based on agent systems principles,” Journal of Computational Science, vol. 47, p. 101244, 2020.
B. B. Gatto, M. A. Mollinetti, E. M. dos Santos, A. L. Koerich, and W. S. da Silva Junior, “A novel genetic algorithm approach for discriminative subspace optimization,” in Brazilian Conference on Intelligent Systems. Springer, 2024, pp. 64–79.
E. I. A. Alonso, K. V. Delgado, and F. C. B. dos Santos, “Combining clustering and genetic algorithms for portfolio optimization: A case study with b3 companies,” in Brazilian Conference on Intelligent Systems. Springer, 2024, pp. 141–156.
E. A. Melo, P. C. Jaime, and C. A. Monteiro, Guia alimentar para a população brasileira. Brasília: Ministério da Saúde, 2014.
Publicado
22/10/2025
Como Citar
SILVA, Thalyson G. N. da; CAMPOS, Gustavo A. L. de; JÚNIOR, Bonfim Amaro; BARROS, Ana Luiza B. de Paula.
Intelligent Systems for Public Health: A Multi-Agent System for Culturally Tailored Dietary Policy. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 22. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 33-41.
DOI: https://doi.org/10.5753/latinoware.2025.14575.
