Navegando por Autor "Barbosa, Carlos Henrique Nogueira de Resende"

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Analysis of selection and crossover methods used by genetic algorithm-based heuristic to solve the LSP allocation problem in MPLS Networks under capacity constraints
(2008) Andrade, Alessandro Vivas; Errico, Luciano de; Aquino, André Luiz Lins de; Assis, Luciana Pereira de; Barbosa, Carlos Henrique Nogueira de Resende
The Multiprotocol Label Switching (MPLS) is a popular routing technique for IP networks, where the core problem is to find a route (called LSP) that satisfy all the capacity constraints imposed by a specific traffic. Genetic algorithms come as a simple, appealing solution approach, but one that requires careful choices concerning initial population generation, crossover, mutation and selection. The present paper discusses the influence of different crossover and selection methods in achieving a fast and accurate convergence of the genetic algorithm, when solving the MPLS allocation problem. The experimental results, using different network topologies such as Carrier, Dora, and Mesh, have shown that uniform crossover and Stochastic Remainder Sampling selection are the most suitable combination to solve the problem.
Distribution network reconfiguration using iterative branch exchange and clustering technique.
(2023) Pereira, Ezequiel C.; Barbosa, Carlos Henrique Nogueira de Resende; Vasconcelos, João Antônio de
The distribution network reconfiguration problem (DNRP) refers to the challenge of searching for a given power distribution network configuration with better operating conditions, such as minimized energy losses and improved voltage levels. To accomplish that, this paper revisits the branch exchange heuristics and presents a method in which it is coupled with other techniques such as evolutionary metaheuristics and cluster analysis. The methodology is applied to four benchmark networks, the 33-, 70-, 84-, and 136-bus networks, and the results are compared with those available in the literature, using the criteria of the number of power flow executions. The methodology minimized the four systems starting from the initial configuration of the network. The main contributions of this work are the use of clustering techniques to reduce the search space of the DNRP; the consideration of voltage regulation banks and voltage-dependent loads in the feeder, requiring the addition of a constraint to the mono-objective model to guarantee the transferred load will be supplied at the best voltage magnitude level, and the application of the methodology in real distribution networks to solve a set of 81 real DNRPs from CEMIG-D (the distribution branch of the Energy Company of Minas Gerais). Four out of those are presented as case studies to demonstrate the applicability of the approach, which efficiently found configurations with lower power and energy losses with few PF runs.
Uma ferramenta didática para aprendizagem de redes sem fio.
(2005) Silva, Leandro Fernandes da; Barbosa, Carlos Henrique Nogueira de Resende; Andrade, Alessandro Vivas; Castro, Cristiano Leite de
Redes sem fio são formadas por nós dotados de algum poder de processamento computacional, com liberdade para se movimentarem em direções arbitrárias e com a capacidade de estabelecerem conexões temporárias entre si para a transmissão de dados. Este trabalho tem por objetivo apresentar uma ferramenta de código aberto, desenvolvida para propósitos de aprendizagem dos conceitos básicos relacionados às redes sem fio.
LONSA : a labeling-oriented non-dominated sorting algorithm for evolutionary many-objective optimization.
(2018) Alexandre, Rafael Frederico; Barbosa, Carlos Henrique Nogueira de Resende; Vasconcelos, João Antônio de
Multiobjective algorithms are powerful in tackling complex optmization problems mathematically represented by two or more conflicting objective functions and their constraints. Sorting a set of current solutions across non-dominated fronts is the key step for the searching process to finally identify which ones are the best solutions. To perform that step, a high computational effort is demanded, especially if the size of the solution set is huge or the mathematical model corresponds to a many-objective problem. In order to overcome this, a new labeling-oriented algorithm is proposed in this paper to speed up the solution-to-front assignment by avoiding usual dominance tests. Along with this algorithm, called Labeling-Oriented Non-dominated Sorting Algorithm (LONSA), the associated methodology is carefully detailed to clearly explain how the classification of the solution set is successfully achieved. This work presents a comparison between LONSA and other well-known algorithms usually found in the literature. The simulation results have shown a better performance of the proposed algorithm against nine chosen strategies in terms of computational time as well as number of comparisons.
A practical codification and its analysis for the generalized reconfiguration problem.
(2013) Barbosa, Carlos Henrique Nogueira de Resende; Alexandre, Rafael Frederico; Vasconcelos, João Antônio de
Distribution network reconfiguration problem is simply aimed at finding the best set of radial configurations among a huge number of possibilities. Each solution of this set ensures optimal operation of the system without violating any prescribed constraint. To solve such problem, it is important to count on efficient procedure to enforce radiality. In this paper, we propose a reliable approach to deal properly with topology constraint enabling algorithm convergence toward optimal or quasi-optimal solutions. A simple and practical codification of individuals used in evolutionary algorithms to solve the generalized reconfiguration problem is detailed in this paper. Mathematical formulation, algorithm, and simulation results are presented for the distribution reconfiguration problem, incorporating a new representation scheme which is immune to topologically unfeasible possibilities. The individual interpretation procedure is straight and it demands no additional data structure or graph preprocessing. Comparisons are made for five well-known distribution systems to demonstrate the efficacy of the proposed methodology. It is also demonstrated that optimal configurations are properly surveyed when single or multiple sources are dealt.
Robust feeder reconfiguration in radial distribution networks.
(2014) Barbosa, Carlos Henrique Nogueira de Resende; Mendes, Marcus Henrique Soares; Vasconcelos, João Antônio de
Distribution feeder reconfiguration has been an active field of research for many years. Some recent theoretical studies have highlighted the importance of smart reconfiguration for the operating conditions of such radial networks. In general, this problem has been tackled using a multi-objective formulation with simplified assumptions, in which the uncertainties related to network components have been neglected by both mathematical models and solution techniques. These simplifications guide searches to apparent optima that may not perform optimally under realistic conditions. To circumvent this problem, we propose a method capable of performing interval computations and consider seasonal variability in load demands to identify robust configurations, which are those that have the best performance in the worst case scenario. Our proposal, named the Interval Multi-objective Evolutionary Algorithm for Distribution Feeder Reconfiguration (IMOEA-DFR), uses interval analysis to perform configuration assessment by considering the uncertainties in the power demanded by customers. Simulations performed in three cases on a 70-busbar system demonstrated the effectiveness of the IMOEA-DFR, which obtained robust configurations that are capable to keep such system working under significant load variations. Moreover, our approach achieved stable configurations that remained feasible over long periods of time not requiring additional reconfigurations. Our results reinforce the need to include load uncertainties when analyzing DFR under realistic conditions.
Utilização de sinais acústicos para detecção de trincas em dormentes de aço.
(2010) Yared, Glauco Ferreira Gazel; Barbosa, Carlos Henrique Nogueira de Resende; Ribeiro, Rodrigo Geraldo; Ribeiro, Marcos Henrique; Thebaldi, Rafael Santos; Nascimento, Leandro Prates do; Oliveira, Paganini Barcellos; Vincic, Jasmina; Silveira, Ingrid Machado; Ferreira, Tiago; Vasconcelos, Renato Lataliza
Os sistemas ferroviários têm sido amplamente utilizados para o transporte de passageiros, matérias primas e produtos industrializados. Diversas vantagens econômicas e sociais podem ser obtidas por meio da utilizacão do transporte ferroviário. Especificamente, as ferrovias brasileiras têm sido submetidas a um aumento crescente de peso da carga transportada, principalmente devido à demanda das indústrias primárias e secundárias, o que também pode ser notado como uma tendência global. A infra-estrutura ferroviária é afetada diretamente pelo aumento de carga, com destaque para as ferrovias de transporte de cargas pesadas (heavy haul), tal como a EFVM (Estrada de Ferro Vitória-Minas) que é utilizada essencialmente no transporte de minério de ferro. No intuito de se manter a confiabilidade e a segurança de tal sistema de transporte, evitando a ocorrência de acidentes, é necessária a realização de um procedimento preciso de manutenção. Este trabalho propõe uma nova abordagem baseada em sinais acústicos medido no ar para o diagnóstico da presença de fissuras microscópicas em dormentes de aço, utilizando-se Redes Neurais Artificiais (RNAs) como ferramentas para classificação. Os resultados obtidos em laboratório forneceram uma taxa de erro de aproximadamente 6%, após a realização de validação cruzada.