Recommending healthy meal plans by optimising nature-inspired many-objective diet problem

Healthy eating is an important issue affecting a large part of the world population, so human diets are becoming increasingly popular, especially with the devastating consequences of Coronavirus Disease (Covid-19). A realistic and sustainable diet plan can help us to have a healthy eating habit sinc...

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Bibliographic Details
Published in:Health informatics journal 2021-01, Vol.27 (1), p.1460458220976719-1460458220976719
Main Authors: Türkmenoğlu, Cumali, Etaner Uyar, A Şima, Kiraz, Berna
Format: Article
Language:English
Subjects:
Adult
Age Factors
Aged
Algorithms
Behavioral Sciences
Body Mass Index
Cooking - economics
Cooking - methods
Coronaviruses
COVID-19 - epidemiology
Diet
Diet, Healthy - methods
Diet, Healthy - psychology
Energy Intake
Feeding Behavior - psychology
Female
Food Preferences
Genetic algorithms
Humans
Male
SARS-CoV-2
Sex Factors
Time Factors
Vegetarians
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Summary:Healthy eating is an important issue affecting a large part of the world population, so human diets are becoming increasingly popular, especially with the devastating consequences of Coronavirus Disease (Covid-19). A realistic and sustainable diet plan can help us to have a healthy eating habit since it considers most of the expectations from a diet without any restriction. In this study, the classical diet problem has been extended in terms of modelling, data sets and solution approach. Inspired by animals’ hunting strategies, it was re-modelled as a many-objective optimisation problem. In order to have realistic and applicable diet plans, cooked dishes are used. A well-known many-objective evolutionary algorithm is used to solve the diet problem. Results show that our approach can optimise specialised daily menus for different user types, depending on their preferences, age, gender and body index. Our approach can be easily adapted for users with health issues by adding new constraints and objectives. Our approach can be used individually or by dietitians as a decision support mechanism.
ISSN:1460-4582
1741-2811
DOI:10.1177/1460458220976719