Development and Comparison of Optimization and Forecasting Methods for Creating an Individual Diet

Personalization of the diet is becoming more and more relevant in the modern world, due to the growing interest in a healthy lifestyle and the need to take into account the individual characteristics of the body. Optimization and forecasting methods are used to develop effective personalized nutrition systems. This paper proposes an approach to the creation of an individual diet based on a comparison of four optimization algorithms, as well as three forecasting algorithms. The developed modeling system provides for the determination of the optimal distribution of food components, taking into account the individual daily calorie requirement. While the primary goal of any dietary recommendation system must be ensuring high nutritional quality and balance, the underlying algorithms were comparatively evaluated as a multi-criteria task based on their computational accuracy (compliance with the target calorie content), processing speed, and the variability of possible diet options generated. The results demonstrate the benefits of a combined approach that integrates optimization and machine learning methods to form a balanced and individually adapted diet.