Skip to main content
SpringerLink
Log in

Recommending Healthy Personalized Daily Menus—A Cuckoo Search-Based Hyper-Heuristic Approach

  • Chapter
  • First Online:
Applied Nature-Inspired Computing: Algorithms and Case Studies

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

This paper presents a food marketplace-based system, which enables food providers to publish their food menus, and clients to order daily food menus personalized according to their profile. The proposed system integrates a Cuckoo Search based hyper-heuristic, which is a high-level method that selects and combines low-level heuristics in order to identify a sequence of low-level heuristics which lead to a menu for 1 day which best satisfies the profile of a client. In our approach, a daily food menu is composed of three main meals and two snacks and is generated by combining food menus for breakfast, lunch, dinner and snacks that are provided by various catering companies. As low-level heuristics, we considered random mutation (i.e. single-point/multiple-point mutation), random crossover (i.e. single-point/ multiple-point crossover) and memory-based mutation and crossover heuristics. We have evaluated the Cuckoo Search based hyper-heuristic on different client profiles and on 2600 menus.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Burke, E. K., et al. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64, 1695–1724.

    Article  Google Scholar 

  2. Koulinas, G., Kotsikas, L., & Anagnostopoulos, K. (2014). A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Information Sciences, 277, 680–693.

    Article  Google Scholar 

  3. Hernández-Ocaña, B., et al. (2018). Bacterial foraging optimization algorithm for menu planning. IEEE Access Journal, 6, 8619–8629.

    Article  Google Scholar 

  4. Syahputra, M. F., et al. (2017). Scheduling diet for diabetes mellitus patients using genetic algorithm. Journal of Physics: Conference Series, 801(1),

    Google Scholar 

  5. Catalan-Salgado, E. A., Zagal-Flores, R., Torres-Fernandez, Y., & Paz-Nieves, A. (2014). Diet generator using genetic algorithms. Research in Computing Science, 75, 71–77.

    Google Scholar 

  6. Ribeiro, D., et al. (2017). SousChef: Mobile meal recommender system for older adults. In Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2017) (pp. 36–45).

    Google Scholar 

  7. Lim-Cheng, N. R., Fabia, G. I. G., Quebral, M. E. G., & Yu, M. T. (2014). Shed: An online diet counselling system. DLSU Research Congress, 1–7.

    Google Scholar 

  8. Espin, V., Hurtado, M. V., & Noguera, M. (2015). Nutrition for client care: A nutritional semantic recommender system for the cliently. Expert Systems Journal, 33(2), 201–210.

    Article  Google Scholar 

  9. Bianchini, D., De Antonellis, V., De Franceschi, N., & Melchiori, M. (2016). PREFer: A prescription-based food recommender system. Computer Standards Interfaces Journal.

    Google Scholar 

  10. Sivilai, S., Snae, C., & Brueckner, M. (2012). Ontology-driven personalized food and nutrition planning system for the cliently. In Proceedings of the 2nd International Conference in Business Management and Information Sciences.

    Google Scholar 

  11. Pop, C. B., et al. (2016). Hybridization of the flower pollination algorithm—A case study in the problem of generating healthy nutritional meals for older adults. Nature-Inspired Computing and Optimization, 151–183.

    Google Scholar 

  12. Cioara, T., Anghel, I., Salomie, I., et al. (2018). Expert system for nutrition care process of older adults. Future Generation Computer Systems, 80, 368–383.

    Article  Google Scholar 

  13. Body Mass Index. http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi.

  14. Harris, J. A., & Benedict, F. G. (1918). A biometric study of human basal metabolism. Proceedings of the National Academy of Sciences of the United States of America, 4(12), 370–373.

    Google Scholar 

  15. The Harris-Benedict Equation. http://www.globalrph.com/harris-benedict-equation.htm.

  16. Eash, H. (2010). Weight loss success. ISBN: 978-0-557-57239-7.

    Google Scholar 

  17. Obesity. http://www.drsharma.ca/obesity-myth-1-the-3500-calorie-rule.

  18. Dietary Reference Intakes (DRIs): Recommended Intakes for Individuals, Food and Nutrition Board. Institute of Medicine, National Academies (2004).

    Google Scholar 

  19. Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In Proceedings of the World Congress on Nature and Biologically Inspired Computing.

    Google Scholar 

  20. Shehab, M., Khader, A. T., & Al-Betar, M. A. (2017). A survey on applications and variants of the cuckoo search algorithm. Applied Soft Computing, 61, 1041–1059.

    Article  Google Scholar 

  21. Marichelvam, M. K., & Geetha, M. (2018). Cuckoo search algorithm for solving real industrial multi-objective scheduling problems (4th ed.). Encyclopedia of Information Science and Technology.

    Google Scholar 

  22. Wang, H., Wang, W., Sun, H., Cui, Z., Rahnamayan, S., & Zeng, S. (2017). A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Computing, 21(15), 4297–4307.

    Article  Google Scholar 

  23. Xiao, L., Hajjam-El-Hassani, A., & Dridi, M. (2017). An application of extended cuckoo search to vehicle routing problem. In Proceedings of the 2017 International Colloquium on Logistics and Supply Chain Management.

    Google Scholar 

  24. Teymourian, E., Kayvanfar, V., Komaki, G. H. M., & Zandieh, M. (2016). Enhanced intelligent water drops and cuckoo search algorithms for solving the capacitated vehicle routing problem. Information Sciences, 334–335, 354–378.

    Article  Google Scholar 

  25. Sait, S. M., Bala, A., & El-Maleh, A. H. Cuckoo search based resource optimization of datacenters. Applied Intelligence, 44(3), 489–506.

    Google Scholar 

  26. Abd Elazim, S. M., & Ali, E. S. (2016). Optimal power system stabilizers design via cuckoo search algorithm. International Journal of Electrical Power and Energy Systems, 75, 99–107.

    Article  Google Scholar 

  27. Nguyen, T. T., Truong, A. V., & Phung, T. A. A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network. International Journal of Electrical Power and Energy Systems, 78, 801– 815.

    Google Scholar 

  28. Li, Z., Dey, N., Ashour, A. S., & Tang, Q. (2018). Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem. Neural Computing and Applications, 30(9), 2685–2696.

    Article  Google Scholar 

  29. Chakraborty, S., Chatterjee, S., Dey, N., Ashour, A.S., Shi, F., & Mali, K. Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microscopy Research and Technique, 80(10), 1051–1072.

    Google Scholar 

  30. Binh, H. T. T., Hanh, N. T., & Dey, N. (2018). Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Computing and Applications, 30(7), 2305–2317.

    Article  Google Scholar 

  31. Chatterjee, S., Sarkar, S., Dey, N., Ashour, A. S., Sen, S., & Hassanien, A. E. (2017). Application of cuckoo search in water quality prediction using artificial neural network. International Journal of Computational Intelligence Studies, 6(2–3), 229–244.

    Article  Google Scholar 

  32. Chatterjee, S., Dzitac, S., Sen, S., Rohatinovici, N. C., Dey, N., Ashour, A. S., et al. (2017). Hybrid modified cuckoo search-neural network in chronic kidney disease classification. In 2017 14th International Conference on Engineering of Modern Electric Systems (EMES) (pp. 164–167).

    Google Scholar 

  33. Chifu, V. R., Pop, C. B., Birladeanu, A., Dragoi, N., & Salomie, I. (2018). Choice function-based constructive hyper-heuristic for generating personalized healthy menu recommendations. In 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (pp. 111–118).

    Google Scholar 

  34. Squirrel’s RecipeML Archive. http://dsquirrel.tripod.com/recipeml/indexrecipes2.html.

  35. Chifu, V., Bonta, R., Chifu, E. St., Salomie, I., & Moldovan, D. (2016). Particle swarm optimization based method for personalized menu recommendations, In Proceedings of the International Conference on Advancements of Medicine and Health Care through Technology (pp. 232–237).

    Google Scholar 

Download references

Acknowledgements

The results presented in this paper were obtained with the support of the Technical University of Cluj-Napoca through the research Contract no. 1997/12.07.2017, Internal Competition CICDI-2017.

Author information

Authors and Affiliations

  1. Department of Computer Science, Technical University of Cluj-Napoca, Baritiu Street, no. 28, Cluj-Napoca, Romania

    Cristina Bianca Pop, Viorica Rozina Chifu, Nicolae Dragoi, Ioan Salomie & Emil Stefan Chifu

Authors
  1. Cristina Bianca Pop
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Viorica Rozina Chifu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicolae Dragoi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ioan Salomie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emil Stefan Chifu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristina Bianca Pop .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  3. Research and Development and Academic Affairs, RCC Institute of Information Technology, Kolkata, India

    Siddhartha Bhattacharyya

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Pop, C.B., Chifu, V.R., Dragoi, N., Salomie, I., Chifu, E.S. (2020). Recommending Healthy Personalized Daily Menus—A Cuckoo Search-Based Hyper-Heuristic Approach. In: Dey, N., Ashour, A., Bhattacharyya, S. (eds) Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-9263-4_3

Download citation

Publish with us

Policies and ethics

Search

Navigation