Automatic Diet Generation by Particle Swarm Optimization Algorithm

Abstract

Deficient nutrition has caused high rates of overweight and obesity in the Mexican population, increasing the cases of people with diabetes and hypertension. In order to solve this, it is necessary to promote a change in the alimentation to reduce the rates of overweight and obesity. To achieve this, we propose a friendly solution to generate a change in the eating habits of the Mexicans by the generation of balance diets. Diet automation has been already created with different algorithms and applications in the past, but with a different purpose and objectives. Particularly, this work is focused on the design of balanced diets applying a Particle Swarm Optimization algorithm. The proposed methodology considers the physical characteristics of the user. To validate the accuracy of the proposed methodology several experiments were performed to asses if the proposal is capable of achieving the calorie goal in terms of the Harris-Benedict equation. The experimental results suggest that it is possible to generate diets using Particle Swarm Optimization algorithms with an error less than 10%.