Intelligent Systems for Public Health: A Multi-Agent System for Culturally Tailored Dietary Policy - Silva, Thalyson G. N. da; Campos, Gustavo A. L. de; Júnior, Bonfim Amaro; Barros, Ana Luiza B. de Paula (Anais do Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware); 2025: Anais do XXII Congresso Latino-Americano de Software Livre e Tecnologias AbertasDO - 10.5753/latinoware.2025.14575, 2025)
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.