Solving Dietary Planning Problem using Particle Swarm Optimization with Genetic Operators

Porras, Edmarlyn M.; Fajardo, Arnel C.; Medina, Ruji P.

doi:10.1145/3310986.3311005

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…Gumustekin et al 20 tried to solve the single-objective diet problem by Bayesian Optimisation Algorithm (BOA), which is an Estimation of Distribution Algorithm (EDA). Porras et al 21 tried to solve the single objective version of the diet problem by Particle Swarm Optimisation. A bi-objective version of the diet problem is studied in two works 22,23 where an additional objective used to maximise the food preference besides minimising the costs.…”

Section: Introductionmentioning

confidence: 99%

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

Türkmenoğlu

Uyar²,

Kiraz

2021

Health Informatics J

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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.

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