Roteamento por colônia de formigas é um método de roteamento em redes de comunicação, e diversos algoritmos foram propostos nos últimos anos baseado nessa estrutura. Todos esses algoritmos produzem excelentes resultados, provando a sua eficiência e eficácia. Este trabalho apresenta os resultados de desempenho dos principais algoritmos encontrados na literatura, e com base nesses resultados, propõe um novo algoritmo com desempenho equivalente e com uma complexidade computacional menor. O trabalho é focalizado em redes tipo datagrama com topologia irregular, descrevendo suas propriedades e características e realizando uma análise e comparação de seus desempenhos em um ambiente de simulação. / Ant Colony Routing is an adaptive method for routing in communication networks, and several algorithms have been proposed in the last years based on this framework. All these algorithms show excellent results, proving their efficiency and efficacy. This work presents the results of the performance of the main algorithms found in the literature, and based on these results, it proposes a novel algorithm that has a similar performance but with a lower computational complexity. The work is focused in datagram like networks with irregular topology, describing its characteristics and properties. The performances in an simulation environment are analysed and compared.

M. Ing. / Fault detection tools have gained popularity in recent years due to the increasing need for reliable and predictable equipments. Transformer bushings account for the majority of transformer faults. Hence, to uphold the integrity of the power transmission and dis- tribution system, a tool to detect and identify faults in their developing stage is necessary in transformer bushings. Among the numerous tools for bushings monitoring, dissolved gas analysis (DGA) is the most commonly used. The advances in DGA and data storage capabilities have resulted in large amount of data and ultimately, the data analysis crisis. Consequent to that, computational intelligence methods have advanced to deal with this data analysis problem and help in the decision-making process. Numerous computational intelligence approaches have been proposed for bushing fault detection. Most of these approaches focus on the accuracy of prediction and not much research has been allocated to investigate the interpretability of the decisions derived from these systems. This work proposes a rough set theory (RST) model for bushing fault detection based on DGA data analyzed using the IEEEc57.104 and the IEC 60599 standards. RST is a rule-based technique suitable for analyzing vague, uncertain and imprecise data. RST extracts rules from the data to model the system. These rules are used for prediction and interpreting the decision process. The lesser the number of rules, the easier it is to interpret the model. The performance of the RST is dependent on the discretization technique employed. An equal frequency bin (EFB), Boolean reasoning (BR) and entropy partition (EP) are used to develop an RST model. The model trained using EFB data performs better than the models trained using BR and EP. The accuracy achieved is 96.4%, 96.0% and 91.3% for EFB, BR and EP respectively. This work also pro poses an ant colony optimization (ACO) for discretization. A model created using ACO discretized achieved an accuracy of 96.1%, which is compatible with the three methods above. When considering the overall performance, the ACO is a better discretization tool since it produces an accurate model with the least number of rules. The rough set tool proposed in this work is benchmarked against a multi-layer perceptron (MLP) and radial basis function (RBF) neural networks. Results prove that RST modeling for bushing is equally as capable as the MLP and better than RBF. The RST, MLP and RBF are used in an ensemble of classifiers. The ensemble performs better than the standalone models.

Um desafio recorrente em processos produtivos é o desenvolvimento de sistemas de monitoração e diagnóstico. Esses sistemas ajudam na detecção de mudanças inesperadas e interrupções, prevenindo perdas e mitigando riscos. Redes Neurais Artificiais (RNA) têm sido largamente utilizadas na criação de sistemas de monitoração. Normalmente as RNA utilizadas para resolver este tipo de problema são criadas levando-se em conta apenas parâmetros como o número de entradas, saídas e quantidade de neurônios nas camadas escondidas. Assim, as redes resultantes geralmente possuem uma configuração onde há uma total conexão entre os neurônios de uma camada e os da camada seguinte, sem que haja melhorias em sua topologia. Este trabalho utiliza o algoritmo de Otimização por Colônia de Formigas (OCF) para criar redes neurais otimizadas. O algoritmo de busca OCF utiliza a técnica de retropropagação de erros para otimizar a topologia da rede neural sugerindo as melhores conexões entre os neurônios. A RNA resultante foi aplicada para monitorar variáveis do reator de pesquisas IEA-R1 do IPEN. Os resultados obtidos mostram que o algoritmo desenvolvido é capaz de melhorar o desempenho do modelo que estima o valor de variáveis do reator. Em testes com diferentes números de neurônios na camada escondida, utilizando como comparativos o erro quadrático médio, o erro absoluto médio e o coeficiente de correlação, o desempenho da RNA otimizada foi igual ou superior ao da tradicional. / A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANN) have been extensively used in creating monitoring systems. Usually the ANN used to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and number of neurons in the hidden layers. This way, the result networks are generally fully connected and have no improvements in its topology. This work uses an Ant Colony Optimization (ACO) algorithm to create a tuned neural networks. The ACO search algorithm uses Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The outcome ANN was applied to monitoring the IEA-R1 research reactor at IPEN. The results show that the algorithm is able to improve the performance of the model which estimates the values of the reactor variables. In tests with different numbers of neurons in the hidden layer, using as comparison the mean squared error, the mean absolute error, and the correlation coefficient, the performance of the optimized ANN proved equal or better than the equivalent traditional neural networks.

Ant algorithms have been used for a variety of combinatorial optimization problems. One of these problems, where ant algorithms haven't been used, is the design of transition rules for cellular automata (CA). Which is a problem that this master's thesis is focused on. This work begins with an introduction into ant algorithms and a overview of its applications, followed by an introduction into CA. In the next part the author proposes a way how to encode rules of CA into a graph which is used in ant algorithms. The last part of this thesis contains an application of encoded graph on elitist ant system and MAX-MIN ant system. This is followed by experimental results of creating transition rules for CA problems by these algorithms.

This diploma thesis deals with optimization of planar antennas on the Electromagnetic Bandgap (EBG) substrates by the help of Ant Colony Optimization (ACO). This method is based on the communications mechanisms of a real ant colony. Firstly, the working principle of the planar antennas and the theory of the Ant Colony Optimization are analyzed. Next, the description of the working principle of the Electromagnetic Bandgap and generally physical phenomena accompanying electromagnetic waves propagation in a periodic medium are given. In the second part of this thesis, the ACO was implemented into the VBA language, and was applied to two models of planar antennas. These models were created in the CST Microwave Studio. After an optimization of the antennas the results were evaluated, and the optimization of one of the antennas was compared to the optimization methods in CST Microwave Studio. Then, the standard substrate of the second model was replaced by the EBG substrate, and the results were confronted. Two types of EBG lattice were used. The design procedure of the square lattice was described, and the dispersion diagram was created in the CST Microwave Studio. In the final part of thesis, the verification of the results was carried out in Ansoft HFSS, and the results from both simulation programs were compared to each other.

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Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. However, the finding of optimal BN from data is NP-hard. This fact has driven the use of heuristic algorithms for solving this kind of problem. Amajor recent focus in BN structure learning is on search and score algorithms. In these algorithms, a scoring function is introduced and a heuristic search algorithm is used to evaluate each network with respect to the training data. The optimal network is produced according to the best score evaluated. This thesis investigates a range of search and score algorithms to understand the relationship between technique performance and structure features of the problems. The main contributions of this thesis include (a) Two novel Ant Colony Optimization based search and score algorithms for BN structure learning; (b) Node juxtaposition distribution for studying the relationship between the best node ordering and the optimal BN structure; (c) Fitness landscape analysis for investigating the di erent performances of both chain score function and the CH score function; (d) A classifier method is constructed by utilizing receiver operating characteristic curve with the results on fitness landscape analysis; and finally (e) a selective o -line hyperheuristic algorithm is built for unseen BN structure learning with search and score algorithms. In this thesis, we also construct a new algorithm for producing BN benchmark structures and apply our novel approaches to a range of benchmark problems and real world problem.

Dans cette thèse, nous nous intéressons à l'étude des capacités de la méta heuristique d'optimisation par colonie de fourmis (Ant Colony Optimization - ACO) pour résoudre des problèmes d’optimisation combinatoire multi-objectif. Dans ce cadre, nous avons proposé une taxonomie des algorithmes ACO proposés dans la littérature pour résoudre des problèmes de ce type. Nous avons mené, par la suite, une étude expérimentale de différentes stratégies phéromonales pour le cas du problème du sac à dos multidimensionnel mono-objectif. Enfin,nous avons proposé un algorithme ACO générique pour résoudre des problèmes d'optimisation multi-objectif. Cet algorithme est paramétré par le nombre de colonies de fourmis et le nombre de structures de phéromone considérées. Il permet de tester et de comparer, dans un même cadre,plusieurs approches. Nous avons proposé six variantes de cet algorithme dont trois présentent de nouvelles approches et trois autres reprennent des approches existantes. Nous avons appliqué et comparé ces variantes au problème du sac à dos multidimensionnel multi-objectif / In this thesis, we investigate the capabilities of Ant Colony Optimization (ACO) metaheuristic to solve combinatorial and multi-objective optimization problems. First, we propose a taxonomy of ACO algorithms proposed in the literature to solve multi-objective problems. Then, we studydifferent pheromonal strategies for the case of mono-objective multidimensional knapsackproblem. We propose, finally, a generic ACO algorithm to solve multi-objective problems. Thisalgorithm is parameterised by the number of ant colonies and the number of pheromonestructures. This algorithm allows us to evaluate and compare new and existing approaches in thesame framework. We compare six variants of this generic algorithm on the multi-objectivemultidimensional knapsack problem

O objetivo deste trabalho é apresentar uma nova abordagem para obtenção de configurações para sistemas de distribuição de energia elétrica com o intuito de minimizar o valor de perdas ativas sem violar as restrições operacionais. Para isso, considera-se que os sistemas de distribuição estão operando em regime permanente e que suas fases estão equilibradas e simétricas, podendo o sistema ser representado por um diagrama unifilar. A reconfiguração é feita de forma a redistribuir os fluxos de corrente nas linhas, transferindo cargas entre os alimentadores e melhorando o perfil de tensão ao longo do sistema. O problema de reconfiguração do sistema pode ser formulado como um problema de programação não-linear inteiro misto. Devido à explosão combinatorial inerente a este tipo de problema, a resolução do mesmo por técnicas de otimização clássicas torna-se pouco atraente, dando espaço para técnicas heurísticas e metaheurísticas. Essas outras, mesmo não garantindo o ótimo global, são capazes de encontrar boas soluções em um espaço de tempo relativamente curto. Para a resolução do problema de reconfiguração, utilizou-se uma nova metodologia baseada no comportamento de colônias de formigas em busca de alimento na natureza. Nesta, formigas artificiais (agentes) exploram o meio ambiente (sistema de distribuição) e trocam informações para tentar encontrar a topologia que apresente os menores valores de perdas ativas. Para o cálculo das perdas, este trabalho também apresenta uma nova abordagem para resolução do problema de fluxo de potência (FP) em sistemas de distribuição radial. O fluxo de potência é uma ferramenta básica utilizada pelos centros de controle para determinar os estados e condições operacionais desses sistemas de potência. Basicamente, as metodologias empregadas para o cálculo do fluxo de potência são baseadas nos métodos clássicos de Newton ou Gauss. Mas em sistemas de distribuição de energia, devido a particularidades inerentes a estes, como a alta relação entre resistência e reatância das linhas (r/x) e a operação radial, estes métodos apresentam problemas de convergência e se tornam ineficientes na maioria das vezes. A abordagem consiste na associação dos métodos da função penalidade e de Newton. O mal-condicionamento da matriz Jacobiana de Newton é resolvido pela associação com o método da função penalidade. São apresentados testes realizados em sistemas de 5 barras, 16 barras, 33 barras, 69 barras e 136 barras para avaliar a potencialidade das técnicas propostas. Os resultados são considerados bons ou muito bons quando comparado com as técnicas existentes atualmente. / The objective of this work is to present a novel methodology for obtaining new configurations of the distribution system in order to minimize the active power losses without violating operational constraints. For this, it is considered that any distribution system is operating in a steady state and that it is balanced, therefore it can be represented by a one-line diagram. The reconfiguration is done in order to redistribute de current flows on the distribution power lines, transferring loads among the feeders and improving the voltage profile along the system. Such problem can be formulated as a mixed integer nonlinear programming problem. Due to its inherent combinatorial characteristic and since its solution by classic optimization techniques is not appealing, heuristic and metaheuristic techniques are thus better suited for its solution. Although these latter do not guarantee a global optimum, they are able to find good solutions in a relatively short time. The solution of the reconfiguration problem in this approach makes use of a novel methodology based on ant colony behavior, when these search for victuals in nature. In this technique, the artificial ants (agents) explore the environment (distribution system) and exchange information among them in order to find the topology that provides the smallest active losses. For the active losses calculation, this work also presents a novel approach for the solution of the power flow problem for radial distribution systems. The solution of the power flow problem is used by system operators in order to determine the state and operational conditions of power systems. Basically, the most common techniques used in the power flow solution are based on either Newton\'s or Gauss\' approaches. However, due to particular characteristics of distribution systems such as the high ratio of r/x and the radial topology, these methods present convergence problems and are not efficient in most of the cases. Thus, this novel technique consists in associating Newton\'s and the penalty function approaches. The matter of the ill-conditioned Jacobian matrix in Newton\'s method is overcome with the penalty function method. Some tests performed in different systems are then presented in order to assess the effectiveness of both proposed techniques.

A tarefa de planejamento de trajetórias de robôs móveis autônomos consiste em determinar objetivos intermediários para que um robô seja capaz de partir de sua localização inicial e alcançar seu objetivo final. Além do planejamento, é importante definir um método de controle da navegação (seguimento da trajetória) do robô para que ele seja capaz de realizar seu trajeto de forma segura. Este projeto propõe uma abordagem híbrida para planejamento exploratório e execução de trajetórias de robôs móveis autônomos em ambientes indoor. Para o planejamento de trajetória, foram investigados algoritmos de busca em espaço de estados, dando ênfase ao uso de algoritmos evolutivos e algoritmos de otimização por colônia de formigas para a descoberta e otimização da trajetória. O controle da navegação é realizado por meio de comportamentos locais reativos, baseado na exploração e uso de mapas topológicos, os quais permitem uma maior flexibilidade em termos de definição da localização da posição do robô móvel e sobre os detalhes do mapa do ambiente (mapas com informações aproximadas e não métricos). Assim, foi proposto e desenvolvido um método robusto capaz de planejar, mapear e explorar um caminho ótimo ou quase ótimo para que o robô possa navegar e alcançar seu objetivo de forma segura, com pouca informação prévia do ambiente ou mesmo sobre sua localização. Além disso, o robô pode reagir a ambientes com alterações dinâmicas em sua estrutura, considerando por exemplo, elementos dinâmicos como portas que possam ser abertas ou fechadas e passagens que são obstruídas. Por fim, foram realizados diversos testes e simulações a fim de validar o método proposto, com a avaliação da qualidade das soluções encontradas e comparação com outras abordagens tradicionais de planejamento de trajetórias (algoritmos A* e D*). / The task of planning path for autonomous mobile robots consists in determine intermediary goals in order to allow a robot be able to leave its initial location and reach its final goal. Besides the planning, it is important to define a method of navigation control (the trajectory following) of the robot for it be able to do its path safely. This project proposes a hybrid approach to path planning and execution of an autonomous mobile robot in indoor environments. For the path planning, search algorithms in state space have been investigated, with emphasis in evolutionary algorithms and ant colony optimization algorithms for the trajectory search and optimization. The navigation control is done by local reactive behaviors, based on topological maps, which allow more flexibility concerning localization definition of position of the mobile robot and about the details of the environment map (maps with approximate information and not metric). Thus, a robust method able to plan an optimum or almost optimum path for the robot to reach its goal safely has been proposed, with little previous information of the environment. Furthermore, the robot can react to dynamic elements in the environment structure, concerning, for example, dynamic elements such as doors that can be opened or closed and ways that are blocked. Finally, several tests and simulations has been carried out to validate the proposed method, with evaluation of the solutions quality and comparison with others traditional approaches for the path planning task (A* and D* algorithms).