Research on the optimization of dietary recipes based on neural network optimization algorithms

Abstract

The paper developed an optimization model that not only focuses on protein quality but also ensures that daily food calories for both genders fall within recommended ranges to meet nutritional needs. With affordability in mind, the present paper identified the lowest-priced breakfast options. Furthermore, wethe paper constructed a daily diet optimization model that balances protein quality and economy, achieving multiple goals of nutrition, taste, and cost-effectiveness through refined algorithms. Scaling up to a one-week meal plan, the present paper designed three distinct optimization models for men and women. These models prioritize maximizing protein amino acid scores, minimizing meal costs, and a combination of both. The resulting meal plans meet nutritional needs while remaining economical, providing a comprehensive weekly meal guide. Based on the paper research, The present paper compiled dietary recommendations that emphasize the importance of amino acid intake in proteins, offer effective cost control methods, and guide individuals on finding the optimal balance between nutrition and economy. The paper aim is to promote healthier, science-based eating habits that support overall development. These recommendations not only ensure nutritional adequacy but also align with economic considerations, making them practical and sustainable for daily use.