Automatic Diet Generation by Artificial Bee Colony Algorithm

• Published in: Advances in Swarm Intelligence Vol. 11655; pp. 299 - 309
• Main Authors: López-López, Magda, Zamora, Axel, Vazquez, Roberto A.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial Bee Colony; Automatic diet generation; Basal Metabolic Rate
• ISBN: 9783030263683; 3030263681
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-26369-0_28

The overweight in the population has become a problem due to the deficiency on the nutritional contributions, increasing the number of people with diseases. The origin of this problem lies in the way people eat, with a poor nutritional quality and in excessive quantities. To solve this, it is necessary that people consider balance diets with the nutritional expectation and the necessary food to improve people’s health and reduce the rates of overweight and obesity. The diet design can be stated as an optimization problem and solved using different algorithms. In this paper, an Artificial Bee Colony (ABC) algorithm has been proposed to automatically design diets considering the physical characteristics of the subjects to find the best diet that satisfies their nutritional requirements using the USDA National Nutrient Database. Particularly, this research is focused on relatively healthy people between 18 and 55 years old to help them to avoid nutritional related diseases. The proposed methodology is compared against particle swarm optimization using the Harris-Benedict equation in order to verify if is capable to achieve the calorie goal.