Food Nutrition Optimization and Prediction System Based on Intelligent Algorithm

This paper therefore presents the use of an intelligent algorithm in developing a food nutrition optimization and prediction system where the system can collect nutrition data on food, constructs a nutrition prediction model, applies an optimization algorithm according to requirements in order to reach its nutritional needs. The use of a machine learning model of the system therefore makes it possible to accurately predict nutrients in the food formula. The experimental results show that the artificial neural network model performs well in capturing complex nutrient relationships and has a high prediction accuracy. Next, the genetic algorithm and particle swarm algorithm are used to optimize the food formula, successfully balancing the intake ratio of different nutrients and achieving specific optimization goals. The system has shown its effectiveness in multi-objective optimization tasks through experimental verification. It can adjust the food formula according to the input health needs and ensure that the nutrients meet the expected standards. This study shows that intelligent algorithms have significant application potential in the prediction and optimization of food nutrients, providing strong support for future personalized nutrition plans. Through the training and verification of the model and the performance evaluation of the optimization algorithm, the system has shown broad application prospects in the fields of nutrition and health management and the food industry.