Search

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.

Population based incremental learning algorithms and selection hyper-heuristics are highly adaptive methods which can handle different types of dynamism that may occur while a given problem is being solved. In this study, we present an approach based on a multi-population framework hybridizing these methods to solve dynamic environment problems. A key feature of the hybrid approach is the utilization of offline and online learning methods at successive stages. The performance of our approach along with the influence of different heuristic selection methods used within the selection hyper-heuristic is investigated over a range of dynamic environments produced by a well known benchmark generator as well as a real world problem, referred to as the Unit Commitment Problem. The empirical results show that the proposed approach using a particular hyper-heuristic outperforms some of the best known approaches in literature on the dynamic environment problems dealt with. [ABSTRACT FROM AUTHOR]