Due to the high cost of manual content creation within the game development industry, methods for procedural generation of content such as game maps and levels have emerged. However, methods for generating game maps have remained relatively unexplored in competitive multiplayer contexts. Presumably, this is due to the opposing goals of generating game maps that are both interesting and fair. This study aims to explore the possibility of satisfying both these goals simultaneously by separating the generative phase from the phase that enforces fairness. In this endeavor, simple game maps for a generic multiplayer grand strategy game are generated using noise-based methods. The task of partitioning the game map fairly between the players is then modeled as a constrained categorical multiobjective minimization problem that is subsequently solved by two genetic algorithms, the reference-point-based algorithm NSGA-III and the decomposition-based algorithm MOEA/D-IEpsilon. In a primary study, the proposed partitioning method is evaluated based on the quality of the solutions produced, its scalability, and its ability to find symmetrical partitions of symmetrical game maps. The results show that the proposed method makes significant improvement from the initial guess but fails to produce completely fair partitions in general. Explanations and possible solutions to this are presented. The timing results indicate that the proposed method is not applicable in real-time contexts. However, the proposed method might still be applicable in online contexts if smaller game maps are considered and in offline contexts if larger game maps are considered. Finally, the partitioning results show that the proposed method successfully finds fair partitions of symmetrical game maps but fails to find the obvious symmetrical partitions. In a secondary study, the two genetic algorithms are compared to determine which algorithm produces dominating solutions and which algorithm produces most diverse solution. The results indicate that, for the partitioning problems considered in this study, the reference-point-based algorithm is both dominant and produces the most diverse solutions.

Ce travail a eu pour objectif de déterminer les conditions optimales de production d’un caroténoïde original, la torularhodine, par Sporobolomyces ruberrimus, cultivée en réacteur discontinu. Cette souche est capable d’utiliser le glycérol technique comme source de carbone et d’énergie pour sa croissance et pour la production de caroténoïdes. D’abord, il s’est agi d’identifier les facteurs opératoires majeurs qui sont susceptibles d’avoir une influence sur la production de la torularhodine, au travers d’une étude préliminaire. L’identification expérimentale de ces facteurs d’action - la température, le taux d’oxygène dissous et la supplémentation en acide oléique - a été validée statistiquement, à des degrés divers, avant d’engager une étape d’optimisation par la construction d’un plan d’expériences multicritère. Celui-ci a conduit à l’établissement de modèles polynômiaux du second degré pour représenter l’effet conjugué des facteurs retenus et permettre la prédiction des valeurs de µmax et de concentration de torularhodine rapportée à la biomasse. Cette étude a alors été consacrée à un essai de fonctionnalisation de la torularhodine, à partir de sa fonction carboxylique, en vue de la stabilisation de la molécule dont l’activité antioxydante est élevée. L’acylation enzymatique de la lysine par la torularhodine a été envisagée. Les conditions d’acylation par la lipase B de C. antarctica ont été déterminées avec un caroténoïde modèle, la bixine. Le produit dérivé obtenu après transacylation a été purifié et a montré une activité antiradicalaire supérieure à celle de la bixine. Ces résultats permettent d’envisager la synthèse de peptides acylés avec ce type de caroténoïdes / The aim of this work was to determine the optimum of an original carotenoid, the torularhodin, produced by Sporobolomyces ruberrimus, in batch culture. A very interesting characteristic of this strain is its ability to consume raw glycerol as a carbon and energy source for microbial growth and carotenoid production. In the fist part of this study, the identification of operating parameters that have an influence on the optimum torularhodin production, was achieved. Experimental assays reinforced by a statistical study allowed to identify temperature, dissolved oxygen pressure and oleic acid supplementation, as the major parameters of influence, and then the integration of these data was performed for the construction of a multiobjective optimization based on a multicriteria experimental design. The establishment of a mathematical model of a second degree polynomial type was developed for the prediction of the values of µmax and of the torularhodin concentration reported to biomass. In the last part, considering that torularhodin has an important antioxidant property and it exhibits a free carboxyl acid function which can be used as acyl agent, a study of its structure modifying by an enzymatic way as a stabilization pattern was started. The experimental conditions of lysine acylation by the lipase B of Candida antarctica were determined using a model carotenoid, the bixin. The resulting product of the synthesis of bixin derivative was purified and showed an antiradical activity of 2.5 times higher than that of bixin. This result showed the ability of the acylation reaction of peptides with this kind of carotenoids

Cette thèse traite de l'optimisation sous incertitude de fonctions coûteuses dans le cadre de la conception de systèmes aéronautiques.Nous développons dans un premier temps une stratégie d'optimisation robuste multiobjectifs par modèles de substitution. Au-delà de fournir une représentation plus rapide des fonctions initiales, ces modèles facilitent le calcul de la robustesse des solutions par rapport aux incertitudes du problème. L'erreur de modélisation est maîtrisée grâce à une approche originale d'enrichissement de plan d'expériences qui permet d'améliorer conjointement plusieurs modèles au niveau des régions de l'espace possiblement optimales.Elle est appliquée à la minimisation des émissions polluantes d'une chambre de combustion de turbomachine dont les injecteurs peuvent s'obstruer de façon imprévisible.Nous présentons ensuite une méthode heuristique dédiée à l'optimisation robuste multidisciplinaire. Elle repose sur une gestion locale de la robustesse au sein des disciplines exposées à des paramètres incertains, afin d'éviter la mise en place d'une propagation d'incertitudes complète à travers le système. Un critère d'applicabilité est proposé pour vérifier a posteriori le bien-fondé de cette approche à partir de données récoltées lors de l'optimisation. La méthode est mise en œuvre sur un cas de conception avion où la surface de l'empennage vertical n'est pas connue avec précision. / This PhD thesis deals with the optimization under uncertainty of expensive functions in the context of aeronautical systems design.First, we develop a multiobjective robust optimization strategy based on surrogate models.Beyond providing a faster representation of the initial functions, these models facilitate the computation of the solutions' robustness with respect to the problem uncertainties. The modeling error is controlled through a new design of experiments enrichment approach that allows improving several models concurrently in the possibly optimal regions of the search space. This strategy is applied to the pollutant emission minimization of a turbomachine combustion chamber whose injectors can clog unpredictably. We subsequently present a heuristic method dedicated to multidisciplinary robust optimization. It relies on local robustness management within disciplines exposed to uncertain parameters, in order to avoid the implementation of a full uncertainty propagation through the system. An applicability criterion is proposed to check the validity of this approach a posteriori using data collected during the optimization. This methodology is applied to an aircraft design case where the surface of the vertical tail is not known accurately.

Les métaheuristiques sont une famille d'algorithmes stochastiques destinés à résoudre des problèmes d 'optimisation difficile . Utilisées dans de nombreux domaines, ces méthodes présentent l'avantage d'être généralement efficaces, sans pour autant que l'utilisateur ait à modifier la structure de base de l'algorithme qu'il utilise. Parmi celles-ci, l'Optimisation par Essaim Particulaire (OEP) est une nouvelle classe d'algorithmes proposée pour résoudre les problèmes à variables continues. Les algorithmes d'OEP s'inspirent du comportement social des animaux évoluant en essaim, tels que les oiseaux migrateurs ou les poissons. Les particules d'un même essaim communiquent de manière directe entre elles tout au long de la recherche pour construire une solution au problème posé, en s'appuyant sur leur expérience collective. Reconnues depuis de nombreuses années pour leur efficacité, les métaheuristiques présentent des défauts qui rebutent encore certains utilisateurs. Le réglage des paramètres des algorithmes est un de ceux-ci. Il est important, pour chaque probléme posé, de trouver le jeu de paramètres qui conduise à des performances optimales de l'algorithme. Cependant, cette tâche est fastidieuse et coûteuse en temps, surtout pour les utilisateurs novices. Pour s'affranchir de ce type de réglage, des recherches ont été menées pour proposer des algorithmes dits adaptatifs . Avec ces algorithmes, les valeurs des paramètres ne sont plus figées, mais sont modifiées, en fonction des résultats collectés durant le processus de recherche. Dans cette optique-là, Maurice Clerc a proposé TRIBES, qui est un algorithme d'OEP mono-objectif sans aucun paramètre de contrôle. Cet algorithme fonctionne comme une boite noire , pour laquelle l'utilisateur n'a qu'à définir le problème à traiter et le critàre d'arrêt de l'algorithme. Nous proposons dans cette thèse une étude comportementale de TRIBES, qui permet d'en dégager les principales qualités et les principaux défauts. Afin de corriger certains de ces défauts, deux modules ont été ajoutés à TRIBES. Une phase d'initialisation régulière est insérée, afin d'assurer, dès le départ de l'algorithme, une bonne couverture de l'espace de recherche par les particules. Une nouvelle stratégie de déplacement, basée sur une hybridation avec un algorithme à estimation de distribution, est aussi définie, afin de maintenir la diversité au sein de l'essaim, tout au long du traitement. Le besoin croissant de méthodes de résolution de problèmes multiobjectifs a conduit les concepteurs à adapter leurs méthodes pour résoudre ce type de problème. La complexité de cette opération provient du fait que les objectifs à optimiser sont souvent contradictoires. Nous avons élaboré une version multiobjectif de TRIBES, dénommée MO-TRIBES. Nos algorithmes ont été enfin appliqués à la résolution de problèmes de seuillage d'images médicales et au problème de dimensionnement de composants de circuits analogiques / Metaheuristics are a new family of stochastic algorithms which aim at solving difficult optimization problems. Used to solve various applicative problems, these methods have the advantage to be generally efficient on a large amount of problems. Among the metaheuristics, Particle Swarm Optimization (PSO) is a new class of algorithms proposed to solve continuous optimization problems. PSO algorithms are inspired from the social behavior of animals living in swarm, such as bird flocks or fish schools. The particles of the swarm use a direct way of communication in order to build a solution to the considered problem, based on their collective experience. Known for their e ciency, metaheuristics show the drawback of comprising too many parameters to be tuned. Such a drawback may rebu some users. Indeed, according to the values given to the parameters of the algorithm, its performance uctuates. So, it is important, for each problem, to nd the parameter set which gives the best performance of the algorithm. However, such a problem is complex and time consuming, especially for novice users. To avoid the user to tune the parameters, numerous researches have been done to propose adaptive algorithms. For such algorithms, the values of the parameters are changed according to the results previously found during the optimization process. TRIBES is an adaptive mono-objective parameter-free PSO algorithm, which was proposed by Maurice Clerc. TRIBES acts as a black box , for which the user has only the problem and the stopping criterion to de ne. The rst objective of this PhD is to make a global study of the behavior of TRIBES under several conditions, in order to determine the strengths and drawbacks of this adaptive algorithm. In order to improve TRIBES, two new strategies are added. First, a regular initialization process is defined in order to insure an exploration as wide as possible of the search space, since the beginning of the optimization process. A new strategy of displacement, based on an hybridation with an estimation of distribution algorithm, is also introduced to maintain the diversity in the swarm all along the process. The increasing need for multiobjective methods leads the researchers to adapt their methods to the multiobjective case. The di culty of such an operation is that, in most cases, the objectives are con icting. We designed MO-TRIBES, which is a multiobjective version of TRIBES. Finally, our algorithms are applied to thresholding segmentation of medical images and to the design of electronic components

Na área de inteligência artificial existem algoritmos de aprendizado, notavelmente aqueles pertencentes à área de aprendizado de máquina AM , capazes de automatizar a extração do conhecimento implícito de um conjunto de dados. Dentre estes, os algoritmos de AM simbólico são aqueles que extraem um modelo de conhecimento inteligível, isto é, que pode ser facilmente interpretado pelo usuário. A utilização de AM simbólico é comum no contexto de classificação, no qual o modelo de conhecimento extraído é tal que descreve uma correlação entre um conjunto de atributos denominados premissas e um atributo particular denominado classe. Uma característica dos algoritmos de classificação é que, em geral, estes são utilizados visando principalmente a maximização das medidas de cobertura e precisão, focando a construção de um classificador genérico e preciso. Embora essa seja uma boa abordagem para automatizar processos de tomada de decisão, pode deixar a desejar quando o usuário tem o desejo de extrair um modelo de conhecimento que possa ser estudado e que possa ser útil para uma melhor compreensão do domínio. Tendo-se em vista esse cenário, o principal objetivo deste trabalho é pesquisar métodos de computação evolutiva multiobjetivo para a construção de regras de conhecimento individuais com base em critérios definidos pelo usuário. Para isso utiliza-se a biblioteca de classes e ambiente de construção de regras de conhecimento ECLE, cujo desenvolvimento remete a projetos anteriores. Outro objetivo deste trabalho consiste comparar os métodos de computação evolutiva pesquisados com métodos baseado em composição de rankings previamente existentes na ECLE. É mostrado que os métodos de computação evolutiva multiobjetivo apresentam melhores resultados que os métodos baseados em composição de rankings, tanto em termos de dominância e proximidade das soluções construídas com aquelas da fronteira Pareto-ótima quanto em termos de diversidade na fronteira de Pareto. Em otimização multiobjetivo, ambos os critérios são importantes, uma vez que o propósito da otimização multiobjetivo é fornecer não apenas uma, mas uma gama de soluções eficientes para o problema, das quais o usuário pode escolher uma ou mais soluções que apresentem os melhores compromissos entre os objetivos / Machine Learning algorithms are notable examples of Artificial Intelligence algorithms capable of automating the extraction of implicit knowledge from datasets. In particular, Symbolic Learning algorithms are those which yield an intelligible knowledge model, i.e., one which a user may easily read. The usage of Symbolic Learning is particularly common within the context of classification, which involves the extraction of knowledge such that the associated model describes correelation among a set of attributes named the premises and one specific attribute named the class. Classification algorithms usually target into creating knowledge models which maximize the measures of coverage and precision, leading to classifiers that tend to be generic and precise. Althought this constitutes a good approach to creating models that automate the decision making process, it may not yield equally good results when the user wishes to extract a knowledge model which could assist them into getting a better understanding of the domain. Having that in mind, it has been established as the main goal of this Masters thesis the research of multi-objective evolutionary computing methods to create individual knowledge rules maximizing sets of arbitrary user-defined criteria. This is achieved by employing the class library and knowledge rule construction environment ECLE, which had been developed during previous research work. A second goal of this Masters thesis is the comparison of the researched evolutionary computing methods against previously existing ranking composition methods in ECLE. It is shown in this Masters thesis that the employment of multi-objective evolutionary computing methods produces better results than those produced by the employment of ranking composition-based methods. This improvement is verified both in terms of solution dominance and proximity of the solution set to the Pareto-optimal front and in terms of Pareto-front diversity. Both criteria are important for evaluating the efficiency of multi-objective optimization algorithms, for the goal of multi-objective optimization is to provide a broad range of efficient solutions, so the user may pick one or more solutions which present the best trade-off among all objectives

Im Mittelpunkt unserer Untersuchungen steht das mehrkriterielle Optimierungsproblem, in einer beliebigen nichtleeren Menge eines halbgeordneten endlich dimensionalen Raumes. Zu dessen Lösung betrachten wir ein Dialogverfahren, in dem der Entscheidungsträger in jedem Schritt seine Wünsche äußert. Bei der Bestimmung einer Lösung, die den Entscheidungsträger zufriedenstellt, müssen wir ein im Allgemeinen nichtkonvexes und nicht triviales skalares Optimierungsproblem lösen. Zur Lösung dieses Problems haben wir zwei Klassen einparametrischer Optimierungsprobleme (Einbettungen) konstruiert. Mit Hilfe der Projektion auf den konvexen Ordungskegel haben wir gezeigt, dass diese Einbettungen wohldefiniert sind. Im Gegensatz zu der in der Literatur untersuchten Standardeinbettung, sind die in dieser Arbeit betrachteten Einbettungen durch die Skalarisierungen der Vektoroptimierungsprobleme mittels streng monotoner skalarisierender Funktionen motiviert. Diese Untersuchung wird unter dem Gesichtspunkt der Theorie der einparametrischen Optimierungsprobleme für den Fall eines beliebigen spitzen polyedrischen Ordnungskegels durchgeführt. Sie umfasst z.B. Fragestellungen nach der Art der Singularitäten, die für die verschiedenen Einbettungen auftreten können, nach den Bedingungen, unter denen eine Zusammenhangskomponente in der Menge stationärer oder verallgemeinerter kritischer Punkte mit Hilfe von Kurvenverfolgungsmethoden numerisch beschrieben werden kann und nach den hinreichenden Bedingungen für die Existenz einer Lösungskurve. Anschließend haben wir das von Guddat und Jongen eingeführte Konzept der strukturellen Stabilität eines skalaren Optimierungsproblems in der Vektoroptimierung verallgemeinert und einen Zusammenhang zur strukturellen Stabilität eines Minimaxproblems erstellt. Dieses Minimaxproblem steht in starker Beziehung zur Skalarisierungsmethode der Vektoroptimierungsprobleme. / In this work we consider the multiobjective optimization in a subset of a partially orded finite dimensional space. In order to solve this problem we use a dialogue procedure in which the decision maker has to determine in each step the aspiration and reservation level expressing his wishes (goals). This leads to an optimization problem which is not easy to solve in the nonconvex case. We solve it proposing two classes of one-parametric optimization problems (embeddings). Using the projection in the ordering cone, we show that these embeddings are well defined, i.e. the corresponding constraint sets depending on real-valued parameters are not empty. Contrary to the very known standard embedding the proposed embeddings are motivated by the use of strongly monotonically increasing functions, which play an important role by the scalarization of multiobjective optimization problems. The two classes of embeddings are investigated from the point of view of parametric optimization considering a pointed polyhedral cone. This investigation includes the determination of the kind of singularities which can appear, the conditions under which a connected component in the set of stationary or generalized critical point can be numerically described using pathfollowing methods and a solution curve may exist. Finally, we extend the concept of structural stability by Guddat and Jongen to the multiobjective optimization problems and establish a connection to the problem of Minimax type, which is related to the scalarization of multiobjective optimization problems.

Submitted by Jadson Francisco de Jesus SILVA (jadson@uefs.br) on 2018-07-18T21:55:12Z No. of bitstreams: 1 Disserta??o.pdf: 2639155 bytes, checksum: af49bdcdf83d4a063546324a223124a4 (MD5) / Made available in DSpace on 2018-07-18T21:55:12Z (GMT). No. of bitstreams: 1 Disserta??o.pdf: 2639155 bytes, checksum: af49bdcdf83d4a063546324a223124a4 (MD5) Previous issue date: 2018-03-27 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / Wireless visual sensor networks can provide valuable information for a lot of moni- toring and control applications, which has driven much attention from the academic community in last years. For some applications, a set of targets have to be covered by visual sensors and sensing redundancy may be desired in many cases, especially when applications have availability requirements or demands for multiple coverage perspectives for viewed targets. For rotatable visual sensors, the sensing orientations can be adjusted for optimized coverage and redundancy, with different optimization approaches available to address this problem. Particularly, as different optimization parameters may be considered, the redundant coverage maximization issue may be treated as a multi-objective problem, with some potential solutions to be conside- red. In this context, two different evolutionary algorithms are proposed to compute redundant coverage maximization for target viewing, intending to be more efficient alternatives to greedy-based algorithms. Simulation results reinforce the benefits of employing evolutionary algorithms for adjustments of sensors? orientations, poten- tially benefiting deployment and management of wireless visual sensor networks for different applications. / As redes de sensores visuais sem fio podem obter, atrav?s de c?meras, informa??es importantes para aplica??es de controle e monitoramento, e tem ganhado aten??o da comunidade acad?mica nos ?ltimos anos. Para algumas aplica??es, um conjunto de alvos deve ser coberto por sensores visuais, e por vezes com demanda de redund?ncia de cobertura, especialmente quando h? requisitos de disponibilidade ou demandas de m?ltiplas perspectivas de cobertura para os alvos visados. Para sensores visuais rotacion?veis, as orienta??es de detec??o podem ser ajustadas para otimizar cobertura e redund?ncia, existindo diferentes abordagens de otimiza??o dispon?veis para solucionar esse problema. Particularmente, como diferentes par?metros de otimizac?o podem ser considerados, o problema de maximiza??o de cobertura redundante pode ser tratado como um problema multiobjetivo, com algumas solu??es potenciais a serem consideradas. Neste contexto, dois algoritmos evolutivos diferentes s?o propostos para calcular a maximiza??o de cobertura redundante para visualiza??o de alvos, pretendendo ser alternativas mais eficientes para algoritmos gulosos. Os resultados da simula??o refor?am os benef?cios de empregar algoritmos evolutivos para ajustes das orienta??es dos sensores, potencialmente beneficiando a implanta??o e o gerenciamento de redes de sensores visuais sem fio para diferentes aplica??es.

[pt] Estudos empíricos na área de séries temporais indicam que combinar modelos preditivos, originados a partir de diferentes técnicas de modelagem, levam a previsões consensuais superiores, em termos de acurácia, às previsões individuais dos modelos envolvidos na combinação. No presente trabalho é apresentada uma metodologia de combinação convexa de modelos estatísticos de previsão, cujo sucesso depende da forma como os pesos de combinação de cada modelo são estimados. Uma Rede Neural Artificial Perceptron Multi-camada (Multilayer Perceptron - MLP) é utilizada para gerar dinamicamente vetores de pesos ao longo do horizonte de previsão, sendo estes dependentes da contribuição individual de cada previsor observada nos dados históricos da série. O ajuste dos parâmetros da rede MLP é efetuado através de um algoritmo de treinamento híbrido, que integra técnicas de busca global, baseadas em computação evolucionária, junto com o algoritmo de busca local backpropagation, de modo a otimizar de forma simultânea tanto os pesos quanto a arquitetura da rede, visando, assim, a gerar de forma automática um modelo de ponderação dinâmica de previsores de alto desempenho. O modelo proposto, batizado de Neural Expert Weighting - Genetic Algorithm (NEW-GA), foi avaliado em diversos experimentos comparativos com outros modelos de ponderação de previsores, assim como também com os modelos individuais envolvidos na combinação, contemplando 15 séries temporais divididas em dois estudos de casos: séries de derivados de petróleo e séries da versão reduzida da competição NN3, uma competição entre metodologias de previsão, com maior ênfase nos modelos baseados em Redes Neurais. Os resultados demonstraram o potencial do NEWGA em fornecer modelos acurados de previsão de séries temporais. / [en] Empirical studies on time series indicate that the combination of forecasting models, generated from different modeling techniques, leads to higher consen+sus forecasts, in terms of accuracy, than the forecasts of individual models involved in the combination scheme. In this work, we present a methodology for convex combination of statistical forecasting models, whose success depends on how the combination weights of each model are estimated. An Artificial Neural Network Multilayer Perceptron (MLP) is used to generate dynamically weighting vectors over the forecast horizon, being dependent on the individual contribution of each forecaster observed over historical data series. The MLP network parameters are adjusted via a hybrid training algorithm that integrates global search techniques, based on evolutionary computation, along with the local search algorithm backpropagation, in order to optimize simultaneously both weights and network architecture. This approach aims to automatically generate a dynamic weighted forecast aggregation model with high performance. The proposed model, called Neural Expert Weighting - Genetic Algorithm (NEW-GA), was com- pared with other forecaster combination models, as well as with the individual models involved in the combination scheme, comprising 15 time series divided into two case studies: Petroleum Products and the reduced set of NN3 forecasting competition, a competition between forecasting methodologies, with greater emphasis on models based on neural networks. The results obtained demonstrated the potential of NEW-GA in providing accurate models for time series forecasting.

Problemas ocasionados por perturbações na qualidade da energia elétrica (QEE) podem provocar sérios prejuízos, tanto de cunho social, quanto financeiros, aos clientes conectados ao sistema elétrico de potência como um todo. Neste contexto, os clientes que mais sofrem são os clientes industriais, pois estes possuem cargas sensíveis a vários distúrbios associados à falta da QEE. Sendo assim, para adoções de medidas preventivas, ou corretivas, que melhorem os índices de QEE, faz-se necessário um monitoramento dos sistemas elétricos que permita um melhor acompanhamento da ocorrência dos distúrbios. Nesta pesquisa é proposta a modelagem do problema de alocação ótima de monitores de QEE em sistemas de distribuição com múltiplos objetivos, os quais são: minimização do custo do monitoramento, minimização da ambiguidade topológica, maximização do monitoramento das cargas, maximização da quantidade de ramais monitorados, minimização da quantidade de afundamentos não monitorados, e maximização da redundância do monitoramento dos afundamentos. Na resolução do problema foi utilizado o Algoritmo Evolutivo Multiobjetivo com Tabelas (AEMT), adotado por ter boa capacidade de resolução com muitos objetivos. Os resultados obtidos permitiram observar que o AEMT forneceu as fronteiras de Pareto com soluções diversificadas e bem distribuídas ao longo da mesma, mostrando-se de grande relevância para o planejamento de sistemas de monitoramento da QEE em sistemas de distribuição de energia. A principal contribuição desta tese é o fornecimento de um modelo que permite às empresas de energia avaliar os investimentos que farão nos seus sistemas de monitoramento considerando seis critérios distintos, permitindo uma maior flexibilidade no estabelecimento do plano de monitoramento e uma melhor análise do custo/benefício considerando os seis aspectos abordados. / Problems arising from disturbances in power quality (PQ) can cause serious damage, both social, and financial, to customers connected to the electrical power distribution systems as a whole. In this context, the customers who suer most are industrial customers, as they have loads sensitive to various disturbances associated with the lack of PQ. Thus, in order to adopt preventive or corrective measures to improve PQ rates, it is necessary to monitor electrical systems to allow better oversight of the occurrence of disturbances. In this research, the proposal is to model the problem of optimal allocation of power quality monitors in distribution systems with multiple objectives. The multiple objectives are: minimizing the monitoring cost, minimizing ambiguities in topology, maximizing the load monitoring, maximizing the area monitoring, minimizing the voltage sag unmonitored, and maximizing the redundancy in the sag monitoring. In solving the problem, a Multiobjective Evolutionary Algorithm with Tables (MEAT) was adopted due to ability to deal with many objectives. The results show that the AMET finds a set of ecient solutions that are diversified and well-distributed along the Pareto Front, and that they are highly relevant for planning of PQ monitoring systems in electrical power distribution systems. The main contribution of this thesis is to provide a model that allows utilities better evaluate investments that they will make in their monitoring systems comprising six dierent criteria, allowing greater flexibility in establishing the monitoring plan and a better analysis of cost/benefit considering the six aspects.

Problemas ocasionados por perturbações na qualidade da energia elétrica (QEE) podem provocar sérios prejuízos, tanto de cunho social, quanto financeiros, aos clientes conectados ao sistema elétrico de potência como um todo. Neste contexto, os clientes que mais sofrem são os clientes industriais, pois estes possuem cargas sensíveis a vários distúrbios associados à falta da QEE. Sendo assim, para adoções de medidas preventivas, ou corretivas, que melhorem os índices de QEE, faz-se necessário um monitoramento dos sistemas elétricos que permita um melhor acompanhamento da ocorrência dos distúrbios. Nesta pesquisa é proposta a modelagem do problema de alocação ótima de monitores de QEE em sistemas de distribuição com múltiplos objetivos, os quais são: minimização do custo do monitoramento, minimização da ambiguidade topológica, maximização do monitoramento das cargas, maximização da quantidade de ramais monitorados, minimização da quantidade de afundamentos não monitorados, e maximização da redundância do monitoramento dos afundamentos. Na resolução do problema foi utilizado o Algoritmo Evolutivo Multiobjetivo com Tabelas (AEMT), adotado por ter boa capacidade de resolução com muitos objetivos. Os resultados obtidos permitiram observar que o AEMT forneceu as fronteiras de Pareto com soluções diversificadas e bem distribuídas ao longo da mesma, mostrando-se de grande relevância para o planejamento de sistemas de monitoramento da QEE em sistemas de distribuição de energia. A principal contribuição desta tese é o fornecimento de um modelo que permite às empresas de energia avaliar os investimentos que farão nos seus sistemas de monitoramento considerando seis critérios distintos, permitindo uma maior flexibilidade no estabelecimento do plano de monitoramento e uma melhor análise do custo/benefício considerando os seis aspectos abordados. / Problems arising from disturbances in power quality (PQ) can cause serious damage, both social, and financial, to customers connected to the electrical power distribution systems as a whole. In this context, the customers who suer most are industrial customers, as they have loads sensitive to various disturbances associated with the lack of PQ. Thus, in order to adopt preventive or corrective measures to improve PQ rates, it is necessary to monitor electrical systems to allow better oversight of the occurrence of disturbances. In this research, the proposal is to model the problem of optimal allocation of power quality monitors in distribution systems with multiple objectives. The multiple objectives are: minimizing the monitoring cost, minimizing ambiguities in topology, maximizing the load monitoring, maximizing the area monitoring, minimizing the voltage sag unmonitored, and maximizing the redundancy in the sag monitoring. In solving the problem, a Multiobjective Evolutionary Algorithm with Tables (MEAT) was adopted due to ability to deal with many objectives. The results show that the AMET finds a set of ecient solutions that are diversified and well-distributed along the Pareto Front, and that they are highly relevant for planning of PQ monitoring systems in electrical power distribution systems. The main contribution of this thesis is to provide a model that allows utilities better evaluate investments that they will make in their monitoring systems comprising six dierent criteria, allowing greater flexibility in establishing the monitoring plan and a better analysis of cost/benefit considering the six aspects.