Iago Augusto Carvalho

Developing Tasty Calorie Restricted Diets Using a Differential Evolution Algorithm

The classical diet problem seeks a diet that respects the indicated nutritional restrictions at a person with the minimal cost. This work presents a variation of this problem, that aims to minimize the number of ingested calories, instead of the financial cost. It aims to generate tasty and hypocaloric diets that also respect the indicated nutritional restrictions. In order to obtain a good diet, this work proposes a Mixed Integer Linear Programming formulation and a Differential Evolution algorithm that solves the proposed formulation. Computational experiments show that it is possible to obtain tasty diets constrained in the number of calories that respect the nutritional restrictions of a person.

A MILP-based VND for the min-max regret Shortest Path Tree Problem with interval costs

Abstract The min-max regret Shortest Path Tree problem (RSPT) is a NP-Hard Robust Optimization counterpart of the Shortest Path Tree problem, where arcs costs are modeled as intervals of possible values. This problem arises from the uncertainty in link quality the routing protocols for IPv6 Low Wireless Personal Area Networks have to handle. In this paper, we propose a Variable Neighborhood Descent (VND) heuristic based on a Mixed Integer Linear Programming formulation. An exact algorithm based on the same formulation is used to assess the quality of this heuristic. Computational experiments show that VND has an average optimality gap of 0.91%, being smaller that with the best heuristic in literature for RSPT.

Study of Parameter Sensitivity on Bat Algorithm

Heuristics and metaheuristics are known to be sensitive to input parameters. Bat algorithm (BA), a recent optimization metaheuristic, has a great number of input parameters that need to be adjusted in order to increase the quality of the results. Despites the crescent number of works with BA in literature, to the best of our knowledge, there is no work that aims the fine tuning of the parameters. In this work we use benchmark functions and more than 9 millions tests with BA in order to find the best set of parameters. Our experiments shown that we can have almost 14000% of difference in objective function value between the best and the worst set of parameters. Finally, this work shows how to choose input parameters in order to make Bat Algorithm to achieve better results.

A Differential Evolution Algorithm for Computing Caloric-Restricted Diets - Island-Based Model

A rich and balanced diet, combined with physical exercises, is the most common and efficient manner to achieve a healthy body. Since the classic Diet Problem proposed by Stigler, several works in the literature proposed to compute a diet that respects the nutritional needs of an individual. This work deals with a variation of the Diet Problem, called Caloric-Restricted Diet Problem (CRDP). The CRDP objective is to find a reduced caloric diet that also respects the nutritional needs of an individual, thus enabling weight loss in a healthy way. In this paper we propose an Island-based Differential Evolution algorithm, a distributed metaheuristic that evolves a set of populations semi-isolated from each other. Computational experiments showed that this island-based structure outperforms its non-distributed implementation, generating a greater variety of diets with small calorie count.

Solving a Multiobjective Caloric-Restricted Diet Problem using Differential Evolution

The Caloric-Restricted Diet Problem (CRDP) aims at finding diets with a reduced caloric count that also respects the nutritional needs of an individual. Thus, it is possible to achieve weight loss without compromising the individuals health. However, due to the small amount of energy contained in such diets, one may not be fully satisfied after a meal. It is possible to overcome this drawback by inserting a larger amount of protein in the diet, as it was shown to be the most effective macronutrient that provides satiety. Thus, this work presents a multi-objective mathematical formulation for the CRDP that minimizes the calorie count of the diet and maximizes the number of proteins ingested. Besides that, a Generalized Differential Evolution algorithm (GDE3) is proposed to solve the resulting problem. Computational experiments are performed with both mono-objective and multi-objective CRDP and two example diets are presented. It shows that it is possible to achieve a diet with a large amount of proteins, while restricting the caloric number.

Estudo comparativo de métricas de ranqueamento em redes complexas utilizando coeficientes de correlação

Resumo—A classificação e o ranqueamento de vértices é um tema muito estudado em redes complexas. Existem na literatura diversas métricas utilizadas na classificação de vértices em uma rede. Este trabalho visa comparar as diferentes métricas calculando o coeficiente de correlação entre elas. Resultados demonstram que as métricas Grau e Hub apresentam a maior correlação, ranqueando os vértices de maneira mais similar, enquanto as métricas Hub e PageRank obtiveram o menor coeficiente de correlação.