Advertisement

Diet Generator for Elders using Cat Swarm Optimization and Wolf Search

  • D. Moldovan
  • P. Stefan
  • C. Vuscan
  • V. R. Chifu
  • I. Anghel
  • T. Cioara
  • I. Salomie
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 59)

Abstract

This paper addresses the problem of the generation of a recommendation of five healthy meals for the elders for an entire day by taking into consideration their nutritional constraints and their dietary restrictions. This problem is modeled as an optimization problem and is solved by using the Cat Swarm Optimization (CSO) and the Wolf Search (WS) algorithms. These two algorithms were integrated in an experimental prototype that allows the elders to order foods daily. Finally, a series of experiments were conducted in order to determine which algorithm leads to a combination of food packets that best matches the nutritional constraints imposed by the nutritionist and the older adult’s preferences for nutrition, price, time and aspect.

Keywords

Meals Diets Elders Cat Swarm Optimization Wolf Search 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hoddinot J, Rosegrant M, Torero M (2012) Hunger and malnutrition. Global Copenhagen Consensus 1-69Google Scholar
  2. 2.
    Hickson M (2006) Malnutrition and ageing. Postgrad Med J 82(693):2-8Google Scholar
  3. 3.
    Donini L, Scardella P, Piombo L, Neri B, Asprino R, Proietti A, Carcaterra S, Cava E, Cataldi S, Cucinotta D, Bella G D, Barbagallo M, Morrone A (2013) Malnutrition in elderly: Social and economic determinants. The Journal of Nutrition, Health & Ageing 17(1):9-15Google Scholar
  4. 4.
    Tsai P W, Istanda V (2013) Review on Cat Swarm Optimization Algorithms. 3rd International Conference on Consumer Electronics, Communications and Networks (CECNet) 564-567Google Scholar
  5. 5.
    Tang R, Fong S, Yang X S, Deb S (2012) Wolf Search Algorithm with Ephemeral Memory. 2012 Seventh International Conference on Digital Information Management (ICDIM) 165-172Google Scholar
  6. 6.
    Fister I, Yang X-S, Fister I, Brest J, Fister D (2013) A Brief Review of Nature-Inspired Algorithms for Optimization. Elektrotehniski vestnik 80(3):1-7Google Scholar
  7. 7.
    Catalan-Salgado E-A, Zagal-Flores R, Torres-Fernandez Y, Paz-Nieves A (2014) Diet Generator Using Genetic Algorithms. Research in Computer Science (75):71-77Google Scholar
  8. 8.
    Hartati S, ‘Uyun S (2011) Computation of Diet Composition for Patients Suffering from Kidney and Urinary Tract Diseases with the Fuzzy Genetic System. International Journal of Computer Applications (0975-8887) 36(6):38-45Google Scholar
  9. 9.
    Youbo L (2009) Multi-Objective Nutritional Diet Optimization Based on Quantum Genetic Algorithm. 2009 Fifth International Conference on Natural Computation 4:336-340Google Scholar
  10. 10.
    Gumustekin S, Senel T, Cengiz M (2014) A Comparative Study on Bayesian Optimization Algorithm for Nutrition Problem. Journal of Food and Nutrition Research 2(12):952-958Google Scholar
  11. 11.
    Pop C B, Chifu V R, Salomie I, Racz D S, Bonta R M (2016) Hybridization of the Flower Polination Algorithm – A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults. Nature Inspired Computation and Optimization (NICO), 2016, accepted for publicationGoogle Scholar
  12. 12.
  13. 13.
    “Macronutrients: the Importance of Carbohydrate, Protein, and Fat”, McKINLEY HEALTH CENTER University of Illinois at Urbana Champaign at http://mckinley.illinois.edu/handouts/macronutrients.htm
  14. 14.
    DIET4Elders AAL Project at www.diet4elders.eu

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • D. Moldovan
    • 1
  • P. Stefan
    • 1
  • C. Vuscan
    • 1
  • V. R. Chifu
    • 1
  • I. Anghel
    • 1
  • T. Cioara
    • 1
  • I. Salomie
    • 1
  1. 1.Department of Computer ScienceTechnical University of Cluj-NapocaCluj-NapocaRomania

Personalised recommendations