Elizabeth F. Wanner

Centro Federal de Educação Tecnológica de Minas Gerais | CEFETMG · Departamento de Computação (DECOM)

A Multiobjective Optimization Problem (MOP) requires the optimization of several objective functions simultaneously, usually in conflict with each other. One of the most efficient algorithms for solving MOPs is MOEA/D (Multiobjective Evolutionary Algorithm Based on Decomposition), which decomposes a MOP into single-objective optimization subproblem...

Agent-based and individual-based modeling have been widely used to simulate ecological systems. The historical architectures designed to artificial life simulation, namely LIDA and MicroPsy, rely into classical concurrence mechanisms based on threads, shared memory and locks. Although these mechanisms seem to work fine for many multi-agent systems...

In this paper, we propose a multi-objective formulation for solving the menu planning problem, in a Brazilian school context. Considering the school category, the student age group, and the school duration time, we propose a formulation of the problem in which the total cost and the nutritional error according to the Brazilian reference are simulta...

This paper presents two approaches to deal with the shortest path problem (SPP) solution for routing network packets in an optimized way. The first one uses Simulated Annealing (SA), and the second one is a novel hybridization of the Genetic Algorithm with Dijkstra mutation accelerated by the SA (SGA). Also, two different case scenario configuratio...

This paper presents a novel methodology aiming to define and refine a LSTM architecture applied to predict stock market prices. The methodology, dubbed STOCK-PRED: THE LSTM PROPHET OF THE STOCK MARKET, uses iRace and NSGA-II algorithms. The LSTM is built in two steps: (i) initially, iRace determines a robust set of hyperparameters using a compound...

In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a contr...

Background In this paper, we conduct a mobility reduction rate comparison between the first and second COVID-19 waves in several localities from America and Europe using Google community mobility reports (CMR) data. Through multi-dimensional visualization, we are able to compare the reduction in mobility from the different lockdown periods for each...

The unit dispatch problem is defined as the attribution of operational values to each generation unit inside a hydropower plant (HPP), given some criteria such as the total power to be generated, or the operational bounds of each unit. An optimal dispatch programming for hydroelectric units in HPP provides a larger production of electricity, with m...

In this work we present a pattern classification approach coupling the Neighbourhood Component Analysis (NCA) classifier with the Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO). The standard NCA uses the conjugate gradient method to minimize the classification error. Here we propose an approach using the C-DEEPSO instead...

Ant colony optimization (ACO) algorithms have originally been designed for static optimization problems, where the input data is known in advance and is not subject to changes over time. Later, the long term memory of ACO proved effective for reoptimization over environment changes when extended to deal with dynamic combinatorial optimization probl...

With the logistic challenges faced by most countries for the production, distribution, and application of vaccines for the novel coronavirus disease~(COVID-19), social distancing~(SD) remains the most tangible approach to mitigate the spread of the virus. To assist SD monitoring, several tech companies have made publicly available anonymized mobili...

This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical pow...

In this paper, we address an online dimensionality reduction approach to deal with a many-objective formulation of a Vehicle Routing Problem with a Demand Responsive Transport (VRPDRT). The problem relates to a mode of transport similar to available carpooling services in which passengers are transported from their origin to their destination shari...

The confidence of medical equipment is intimately related to false alarms. The higher the number of false events occurs, the less truthful is the equipment. In this sense, reducing (or suppressing) false positive alarms is hugely desirable. In this work, we propose a feasible and real-time approach that works as a validation method for a heartbeat...

Social distancing (SD) has been critical in the fight against the novel coronavirus disease (COVID-19). To aid SD monitoring, many technology companies have made available mobility data, the most prominent example being the community mobility reports (CMR) provided by Google. Given the wide range of research fields that have been drawing insights f...

Backed by the virtually unbounded resources of the cloud, battery-powered mobile robotics can also benefit from cloud computing, meeting the demands of even the most computationally and resource-intensive tasks. However, many existing mobile-cloud hybrid (MCH) robotic tasks are inefficient in terms of optimising trade-offs between simultaneously co...

Recent advances in the availability of computational resources allow for more sophisticated approaches to speech recognition than ever before. This study considers Artificial Neural Network and Hidden Markov Model methods of classification for Human Speech Recognition through Diphthong Vowel sounds in the English Phonetic Alphabet rather than the c...

Recent advances in the availability of computational resources allow for more sophisticated approaches to speech recognition than ever before. This study considers Artificial Neural Network and Hidden Markov Model methods of classification for Human Speech Recognition through Diphthong Vowel sounds in the English Phonetic Alphabet rather than the c...

Dominance move (DoM) is a binary quality indicator to compare solution sets in multiobjective optimization. The indicator allows a more natural and intuitive relation when comparing solution sets. It is Pareto compliant and does not demand any parameters or reference sets. In spite of its advantages, the combinatorial calculation nature is a limita...

This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical pow...

Phoneme awareness provides the path to high resolution speech recognition to overcome the difficulties of classical word recognition. Here we present the results of a preliminary study on Artificial Neural Network (ANN) and Hidden Markov Model (HMM) methods of classification for Human Speech Recognition through Diphthong Vowel sounds in the English...

This paper addresses the problem of optimizing a Demand Responsive Transport (DRT) service. A DRT is a flexible transportation service that provides on-demand transport for users who formulate requests specifying desired locations and times of pick-up and delivery. The vehicle routing and scheduling procedures are performed based on a set of reques...

This work discusses the solution of a Large-scale global optimization problem named Security Constrained Optimal Power Flow (SCOPF) using a method based on Cross Entropy (CE) and Evolutionary Particle Swarm Optimization (EPSO). The obtained solution is compared to the Entropy Enhanced Covariance Matrix Adaptation Evolution Strategy (EE-CMAES) and S...

The benefits of optimising fleets of vehicles regards scheduling tasks are threefold; reduced costs, reduced road use, and most importantly, reduced emissions. However, optimisation methods, both exact and meta-heuristic, scale poorly. This issue is addressed with Partial-ACO, a novel variant of ACO that scales by ants only partially modifying good...

Phoneme awareness provides the path to high resolution speech recognition to overcome the difficulties of classical word recognition. Here we present the results of a preliminary study on Artificial Neural Network (ANN) and Hidden Markov Model (HMM) methods of classification for Human Speech Recognition through Diphthong Vowel sounds in the English...

Accent classification provides a biometric path to high resolution speech recognition. This preliminary study explores various methods of human accent recognition through classification of locale. Classical, ensemble, timeseries and deep learning techniques are all explored and compared. A set of diphthong vowel sounds are recorded from participant...

Taking inspiration from nature for meta-heuristics has proven popular and relatively successful. Many are inspired by the collective intelligence exhibited by insects, fish and birds. However, there is a question over their scalability to the types of complex problems experienced in the modern world. Natural systems evolved to solve simpler problem...

Hybrid micro-grid systems (HMGS) are gaining increasing attention worldwide. The balance between electricity load and generation based on fluctuating renewable energy sources is a main challenge in the operation and design of HMGS. Battery energy storage systems are considered essential components for integrating high shares of renewable energy int...

Optimisation of fleets of commercial vehicles with regards scheduling tasks from various locations to vehicles can result in considerably lower fleet traversal times. This has significant benefits including reduced expenses for the company and more importantly, a reduction in the degree of road use and hence vehicular emissions. Exact optimisation...

The QRS complex is a very important part of a heartbeat in the electrocardiogram signal, and it provides useful information for physicians to diagnose heart diseases. Accurately detecting the fiducial points that compose the QRS complex is a challenging task. Another issue concerning the QRS detection is its computational costs since the algorithm...

Renewable energy technologies use natural sources, such as wind and solar, to produce electricity. Nowadays, there is a global sustainable electric power generation pressure to alleviate environmental impacts caused by the usage of fossil fuels. Energy market is focused on improving those technologies by meeting customer needs, but it proves to be...

The QRS complex is a very important part of a heartbeat in the electrocardiogram signal, and it provides useful information for physicians to diagnose heart diseases. Accurately detecting the fiducial points that compose the QRS complex is a challenging task. Another issue concerning the QRS detection is its computational costs since the algorithm...

A hybrid population-based metaheuristic, Hybrid Canonical Differential Evolutionary Particle Swarm Optimization (hC-DEEPSO), is applied to solve Security Constrained Optimal Power Flow (SCOPF) problems. Despite the inherent difficulties of tackling these real-world problems, they must be solved several times a day taking into account operation and...

This paper presents a hybrid PSO algorithm (Particle Swarm Optimization) with an ILS (Iterated Local Search) operator for handling equality constraints problems in mono-objective optimization problems. The ILS can be used to locally search around the best solutions in some generations, exploring the attraction basins in small portions of the feasib...

In this paper, a hybrid single-objective metaheuris-tic, named as C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed to solve large-scale optimization problems. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO or an swarm optimization method with selection and self-a...

This paper presents a hybrid PSO algorithm with a VND-based operator for handling equality constraint problems in continuous optimization. The VND operator can be defined both as a local search and a kind of elitism operator for equality constraint problems playing the role of “fixing” the best estimates of the feasible set. Experiments performed o...

Electrical power is an important factor to any community, commercial center or industry. Currently, new forms of electricity generation are being proposed and implemented. Among them, hybrid microgrid systems (HGMS) have been playing a significant role. Such systems are gaining increasing attention in the energy transition moment that the entire Wo...

This paper aims to present and evaluate a software that uses an evolutionary strategy to design weekly nutritional menus for School Feeding. The software ensures the nutritional needs of students and also minimizes the total cost of the menu. We based our nutritional needs on the Brazilian National School Feeding Programme (PNAE). This program take...

O objetivo deste artigo é apresentar e avaliar um software que utiliza técnicas de Inteligência Artificial para elaborar, automaticamente e de forma rápida, cardápios nutricionais semanais para a Alimentação Escolar, atendendo às necessidades nutricionais diárias dos alunos e, simultaneamente, minimizando o custo total do cardápio. Esses cardápios...

Battery storage is considered as crucial for the safe operation and design of hybrid micro-grid systems (HMGS) by balancing load and generation from renewable energy sources. However, several battery technologies are available for this purpose, with different greenhouse gas emissions associated with their production. This paper applies a canon...

This paper presents a vision-based system to support tactical and physical analyses of futsal teams. Most part of the current analyses in this sport are manually performed, while the existing solutions based on automatic approaches are frequently composed of costly and complex tools, developed for other kind of team sports, making it difficult thei...

In this work, we propose a genetic algorithm for solving the allocation of Roadside Units (RSUs) in a Hybrid Vehicular Network with Synchronous Communication. We run our algorithm for several V2V communication ranges and compare the influence of these ranges in the number of chosen RSUs.

In this work, we developed a genetic algorithm for solving the automatic menu planning for the Brazilian school context. Our objectives are to create menus that: (i) minimize the total cost and, simultaneously, (ii) minimize the nutritional error according to the Brazilian reference. Those menus also satisfy requirements of the Brazilian government...

Metaheuristics for optimization based on the immune network theory are often highlighted by being able to maintain the diversity of candidate solutions present in the population, allowing a greater coverage of the search space. This work, however, shows that algorithms derived from the aiNET family for the solution of combinatorial problems may not...

This article presents a non-deterministic approach to the Three-Dimensional Bin Packing Problem, using a genetic algorithm. To perform the packing, an algorithm was developed considering rotations, size constraints of objects and better utilization of previous free spaces (flexible width). Genetic operators have been implemented based on existing o...

In this work we propose a GRASP+VNS algorithm for solving the allocation of Roadside Units (RSUs) in a Vehicular Network. Our main objective is to find the minimum set of RSUs to meet a Deployment Delta(r1,r2). The Deployment Delta(r1,r2) is a metric for specifying minimal communication guarantees from the infrastructure supporting Vehicular Networ...

In this work we propose a GRASP-based algorithm, Delta-r-GRASP, for solving the allocation of Roadside Units (RSUs) in a Vehicular Network. Our goal is to find the minimum set of RSUs in order to meet a Deployment Delta(?1, ?2). The Deployment Delta(?1, ?2) is a metric for specifying minimal communication guarantees from the infrastructure supporti...

Demand Responsive Transport (DRT) systems emanate as a substitute to face the problem of volatile, or even inconstant, demand, occurring in popular urban transport systems. This paper is focused in the Vehicle Routing Problem with Demand Responsive Transport (VRPDRT), a type of transport which enables passengers to be taken to their destination, as...

In this work, we propose the Delta-MGA, a specific multiobjective algorithm for solving the allocation of Roadside Units (RSUs) in a Vehicular Network (VANETs). We propose two multiobjective models to solve two different problems. The first one, our objectives are to find the minimum set of RSUs and to maximize the number of covered vehicles. The s...

Demand Responsive Transport (DRT) systems emerge as an alternative to deal with the problem of variable demand, or even unpredictable, occurring in conventional urban transport systems. It can be seen in some practical situations such as public transport in rural areas, wherein in some situations, there is no way to predict demand. This paper addre...

A Deposição Gamma é uma métrica de avaliação da qualidade da infraestrutura de comunicação apoiando a operação de redes veiculares, que leva em consideração dois parâmetros: a)~o tempo entre contatos de veículos com a infraestrutura; b)~a porcentagem de veículos que devem respeitar essas garantias de tempo. A Deposição Gamma pode ser usada pelo pro...

A simple success-based step-size adaptation rule for single-parent Evolution Strategies is formulated, and the setting of the corresponding parameters is considered. Theoretical convergence on the class of strictly unimodal functions of one variable that are symmetric around the optimum is investigated using a stochastic Lyapunov function method de...

The problem of choosing an open-pit mining investment portfolio can be stated as, given a budget, picking among the possible projects the combination that will incur in the best increase for the mine's productivity when applied. Due to interaction between projects even a seemingly cheap and effective project may not be the most appropriate choice a...

The Vehicle Routing Problem (VRP) has been largely studied over the last years, since problems involving the transport of persons and/or goods have great practical application. This paper addresses the Vehicles Routing Problem with Demand Responsive Transport (VRPDRT), a type of transport which enables customers to be taken to your destination like...

In this work we propose Delta-r, a new greedy heuristic for solving the allocation of roadside units in order to meet a $\Delta^{\rho_1}_{\rho_2}$-Deployment. The $\Delta^{\rho_1}_{\rho_2}$-Deployment is a metric for specifying minimal levels of performance from the infrastructure supporting vehicular networks. As far as we are concerned, this is t...

The tensile test is one of the main techniques of mechanical characterization of materials. It is a destructive test and, considering the preparation of the proof corps, it has high cost and is time consuming. This work intends to use Artificial Neural Networks to simulate the behavior of the tensile testing machine at different annealing temperatu...

Dengue epidemics, one of the most important viral disease worldwide, can be prevented by combating the transmission vector Aedes aegypti. In support of this aim, this article proposes to analyze the Dengue vector control problem in a multiobjective optimization approach, in which the intention is to minimize both social and economic costs, using a...

The optimal solution provided by metaheuristics can be viewed as a random variable, whose behavior depends on the value of the algorithm's strategic parameters and on the type of penalty function used to enforce the problem's soft constraints. This paper reports the use of parametric and non-parametric statistics to compare three different penalty...

The Brazilian population increase and the purchase power growth have resulted in a widespread use of electric home appliances. Consequently, the demand for electricity has been growing steadily in an average of 5 % a year. In this country, electric demand is supplied predominantly by hydro power. Many of the power plants installed do not operate ef...

The consumption of electric energy for general supply of a country is increasing over the years. In Brazil, energy demand grows, on average, 5% per year and the power source is predominantly hydroelectric. Many of the power plants installed in Brazil do not operate efficiently, from the water consumption point of view. The normal mode of operation...

This article presents the modeling, development and theoretical grounding for the development of an application based on the clustering algorithm DBSCAN, aiming to reduce the daily waste of time on the locomotion of a huge number of people to a common place. The clusters are created based on attributes, like the departure time of each person from i...