Generating eating plans for athletes using the particle swarm optimization

This paper presents the automatic generation of optimal eating plans for athletes. The automatic generation of the eating plans is introduced as an optimization problem, where particle swarm optimization is taken as the problem solver. Inputs for the proposed particle swarm optimization algorithm are generated training plan and list of the potential meals, while the output of the algorithm represents a list of meals that should be consumed by the athletes. The first practical experiments showed that this solution is very promising.

[1]  S. A. Khonsary Guyton and Hall: Textbook of Medical Physiology , 2017, Surgical Neurology International.

[2]  J. Kljusurić,et al.  Computer-generated vegan menus: The importance of food composition database choice , 2015 .

[3]  Ioan Salomie,et al.  Particle Swarm Optimization-based method for generating healthy lifestyle recommendations , 2013, 2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing (ICCP).

[4]  C. González-Haro,et al.  Indirect Assessment of Glycogen Status in Competitive Athletes: 3258 , 2011 .

[5]  Asker E Jeukendrup,et al.  Nutrition for endurance sports: Marathon, triathlon, and road cycling , 2011, Journal of sports sciences.

[6]  B.K. Seljak,et al.  Dietary Menu Planning Using an Evolutionary Method , 2006, 2006 International Conference on Intelligent Engineering Systems.

[7]  Barbara Koroušić Seljak,et al.  Computer-based dietary menu planning , 2006 .

[8]  T. Noakes,et al.  Prediction of energy expenditure from heart rate monitoring during submaximal exercise , 2005, Journal of sports sciences.

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  Matjaz Perc,et al.  Computational intelligence in sports: Challenges and opportunities within a new research domain , 2015, Appl. Math. Comput..

[11]  Z. Kurtanjek,et al.  Application of Fuzzy Logic in Diet Therapy - Advantages of Application , 2012 .