A qualidade da água de abastecimento é de vital importância à saúde da população dos núcleos urbanos do mundo todo. Por essa razão, muitas pesquisas enfocam esse tema. Além disso, os ataques terroristas recentes ocorridos nos Estados Unidos e Europa, vêm fomentando a antiga preocupação relativa a possíveis injeções de contaminantes em redes de distribuição de água para abastecimento, evidenciando a importância da efetiva vigilância de tais sistemas. O presente trabalho investiga a rede de monitoramento ótima para detecção de injeções intencionais de poluentes e/ou contaminantes em concentrações e/ou quantidades suficientes para que se propaguem nas direções do fluxo da água no interior das redes, do ponto de vista de objetivos múltiplos. A metodologia aqui apresentada representa uma extensão de propostas anteriores e é demonstrada ilustrativamente, através de redes já utilizadas na literatura. Com base no conceito da rede auxiliar proposto por Kessler et al. (1998), propõe-se o emprego de algoritmos genéticos multiobjetivo para considerar os níveis de serviço em termos do volume consumido, do tempo e da extensão da rede atingida antes da detecção. São criadas matrizes de poluição para os níveis de serviço considerados e, então, o algoritmo genético multiobjetivo SPEA é aplicado para identificar as soluções não-dominadas, em conformidade com o conceito de otimalidade de Pareto. Os resultados demonstram o potencial do método proposto em identificar tais soluções / The water supply quality is very important to the healthy of urban nucleus populations around the world. This is the reason why many researches focus on such theme. Besides this, recent terrorist attacks occurred in USA and Europe, have incited the old apprehension related to possible deliberate intrusions of contaminants into the water supply networks, making evident the importance of the effective vigilance of such systems. This work investigates the optimal monitoring network for detection of deliberate intrusions of pollutes and/or contaminants at concentrations and/or quantities enough for propagation inside the networks, on the point of view of multiple objectives. The method here proposed represents an extension to earlier proposals and is demonstrated with the support of networks from literature. Based on the concept of auxiliary network proposed by Kessler et al. (1998), a multiobjective genetic algorithm is used in order to consider the levels of service in terms of the consumed volume, time period and length of the network reached before detection. Pollution matrixes are built for the levels of service considered and the multiobjective genetic algorithm SPEA applied in the identification of the non-dominated solutions, according to the Paretto optimality concept. The results demonstrate the potential of the method in the identification of such solutions

Submitted by isabela.moljf@hotmail.com (isabela.moljf@hotmail.com) on 2017-07-04T16:01:12Z No. of bitstreams: 1 rafaelvalenteveiga.pdf: 4234191 bytes, checksum: 215e887a2d4ac1ec9993886981d707eb (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-08-08T15:16:30Z (GMT) No. of bitstreams: 1 rafaelvalenteveiga.pdf: 4234191 bytes, checksum: 215e887a2d4ac1ec9993886981d707eb (MD5) / Made available in DSpace on 2017-08-08T15:16:30Z (GMT). No. of bitstreams: 1 rafaelvalenteveiga.pdf: 4234191 bytes, checksum: 215e887a2d4ac1ec9993886981d707eb (MD5) Previous issue date: 2017-03-08 / Nas últimas décadas os casos de asma e demais doenças alérgicas tiveram grande aumento em todo o mundo, sendo atualmente um grande problema de saúde pública. As causas do aumento da prevalência destas patologias são desconhecidas, porém a hipótese mais aceita é que seja oriunda da redução de infecções na infância como consequência da recente melhora nas condições de higiene. Esta redução das infecções pode levar ao desenvolvimento anômalo do sistema imune, aumentando assim a chance de desenvolver alergias e a asma. Tanto a asma como as demais alergias são patologias complexas, causadas por fatores genéticos e ambientais, de modo que o uso de ferramentas computacionais, tais como a programação genética podem contribuir para a compreensão destas doenças. Aplicou-se a técnica de Programação Genética Gramatical Multiobjetivo (MGGP) em dados obtidos de um coorte de 1445 crianças entre 4 e 11 anos para gerar modelos os quais possam representar como as relações entre infecções e ambiente podem explicar o desenvolvimento de atopia e asma. Para avaliar a presença de asma foi usado um questionário do ISAAC fase II e para avaliar atopia foram realizadas medições de anticorpos IgE contra alérgenos comuns e teste de reatividade cutânea. Os resultados obtidos mostram que os modelos gerados pela MGGP apresentam desempenho em acurácia competitivos aos obtidos pelo algoritmo C4.5 e regressão logística múltipla para os diferentes desfechos. Os resultados obtidos pela MGGP são de fácil interpretação, e capazes de encontrar relações complexas relevantes para o entendimento destas patologias, sendo assim, a MGGP é uma poderosa ferramenta para ajudar a compreender essas condições. / In the last decades cases of asthma and other allergic diseases have increased greatly throughout the world, being nowadays a major public health problem. The causes of this increased prevalence are unknown, however the most accepted hypothesis is that it comes from the reduction of childhood infections as a consequence of the recent improvement in hygiene conditions. This reduction of infections can lead to anomalous development of the immune system, thus increasing the chance of developing allergies, including asthma. Asthma and other allergies are complex pathologies caused by genetical and environmental factors, so the use of computational tools such as genetic programming can contribute to the understanding of these diseases. We applied the Multiobjective Grammatical Genetic Programming (MGGP) technique to data obtained from a cohort of 1445 children to generate models which may represent how the relationships between infections and environment may explain the development of allergies and asthma. To assess the presence of asthma, a questionnaire was used and allergy measurements were performed on IgE antibodies against common allergens and skin reactivity test. The results obtained show that the models generated by MGGP show a performance in accuracy that is competitive with those obtained by the algorithm C4.5 and multiple logistic regression for the different outcomes. The results obtained by MGGP are easy to interpret, and capable of finding complex relationship relevant to the understanding of these complex pathologies, therefore MGGP is a powerful tool to help understand these conditions.

Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-07-19T15:32:30Z No. of bitstreams: 1 felipegomesduque.pdf: 3507645 bytes, checksum: d7e6e84ea872f86ac2bd64caa361fce0 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-07-23T15:26:16Z (GMT) No. of bitstreams: 1 felipegomesduque.pdf: 3507645 bytes, checksum: d7e6e84ea872f86ac2bd64caa361fce0 (MD5) / Made available in DSpace on 2018-07-23T15:26:16Z (GMT). No. of bitstreams: 1 felipegomesduque.pdf: 3507645 bytes, checksum: d7e6e84ea872f86ac2bd64caa361fce0 (MD5) Previous issue date: 2018-04-12 / O presente trabalho propõe uma metodologia de planejamento de medição em Sistemas de Distribuição de Energia Elétrica (SDE) e um novo método para estimação de estados destes sistemas. Para tanto, a técnica metaheurística de otimização bio-inspirada denominada Modified Monkey Search (MMS) é proposta para alocação ótima de medidores inteligentes e unidades de medição fasorial. O modelo de otimização é multiobjetivo e visa a maximização da eficácia do processo de estimação de estados com o custo mínimo de investimento em sistemas de medição. O método de Pareto é associado ao algoritmo MMS para o tratamento adequado destes objetivos conflitantes considerando-se custos reais associados aos equipamentos de medição. Adicionalmente, um novo método de estimação de estados baseado na modelagem de um Fluxo de Potência Ótimo (FPO) modificado é proposto, cuja resolução é dada pelo Método de Pontos Interiores (MPI). O algoritmo MMS determina as variáveis discretas associadas aos tipos de equipamentos de medição, bem como aos locais de instalação dos mesmos no SDE. Estudos são realizados para comparar a nova metodologia de estimação de estados proposta com uma metodologia tradicional, bem como para comparar os resultados da metaheurística de otimização aplicada ao problema com outras técnicas desenvolvidas para esta finalidade. Os estudos são conduzidos com sistemas da literatura, além de um sistema real de médio porte de uma concessionária brasileira. / The present work proposes an approach for planning the measurement locations in Electric Distribution Systems (EDS) and a new method for static state estimation. The bio-inspired meta-heuristic optimization technique called Modified Monkey Search (MMS) is proposed for optimal allocation of smart meters and phasor measurement units. The optimization model is multiobjective and aims at maximizing the efficiency of the state estimation process with minimum measurement investment costs. The Pareto’s method is associated with the MMS algorithm for handling the conflicting objectives in a suitable manner by considering real costs related to measurement equipments. In addition, a new method for static state estimation based on the modeling of a modified Optimal Power Flow (OPF) is proposed, whose solution is given by the Interior Point Method (IPM). The MMS algorithm determines the discrete variables related to types and location of measurement equipments in the system. Studies are made to compare the new approach for static state estimation with a traditional method, as well as to compare the results from the meta-heuristic optimization applied to the problem with existing techniques. The studies are performed using systems from the literature, as will as a practical medium size distribution network from a Brazilian utility.

Les pays industrialisés cherchent aujourd’hui à réduire leur consommation d'énergie et à utiliser des matières premières de substitution, notamment renouvelables dont le bois fait partie. Pour promouvoir son usage, le développement de méthodes favorisant son recours dans les systèmes constructifs pour la construction multiétage est nécessaire.La conception d’un bâtiment est multicritère. Des objectifs contradictoires sont à optimiser simultanément. Des solutions de compromis Pareto-optimaux sont par exemple recherchées entre l’atteinte des meilleures performances d’usage et la limitation de l’impact environnemental du bâtiment. Ces travaux portent ainsi sur le développement d’une méthode d’optimisation multiobjectif de systèmes constructifs bois adaptés au multiétage.Des objectifs de maximisation du confort vibratoire des planchers et de minimisation des besoins de chauffage, d’inconfort thermique, de potentiel de réchauffement climatique et d’énergie grise sont pris en compte. La méthode repose sur un algorithme d’optimisation multiobjectif par essaim particulaire capable de proposer un ensemble de solutions non-dominées constituant le front de Pareto. L’espace des solutions est contraint par des exigences réglementaires nécessaires à la sécurité de l’ouvrage. L’ensemble des fonctions-objectif est modélisé sous forme de fonctions analytiques. Les sorties d’intérêt du modèle de simulation thermique dynamique sont substituées par des métamodèles.La méthode développée est mise en oeuvre sur un cas d’étude. Les résultats obtenus offrent une grande diversité dans un panel de 20 000 solutions optimales. Ces résultats constituent un support de discussion entre les différents acteurs d’un projet de construction. / Industrialised countries are seeking to reduce their energy consumption and to use alternative raw materials, including renewables such as wood. To promote its use, multi-storey timber constructive systems need the development of new design methods.Building required a multicriteria design where conflicting objectives must be optimised simultaneously. Research solutions have to achieve the best Pareto-compromise between use performance and environmental impact of the building. This work aims to develop a multiobjective optimisation method of timber multi-storey building.The objectives of maximising floor vibration comfort and minimising heating needs, thermal discomfort, global warming potential and embodied energy are taken into account. A multi-objective particle swarm optimization algorithm is used to obtain a set of non-dominated solutions which is the Pareto front. The solution space is constrained by regulatory requirements necessary for the safety of the structure. All objective-functions are modelled as analytic functions. Dynamic thermal simulation model outputs are replaced by metamodels.The developed method is implemented on a case study. The results offer a great diversity in a panel of 20 000 optimal solutions. These results provide a basis for discussion between the different actors of a construction project.

Flooding is an important concern for the UK, as evidenced by the many extreme flooding events in the last decade. Improved flood risk intervention strategies are therefore highly desirable. The application of hydroinformatics tools, and optimisation algorithms in particular, which could provide guidance towards improved intervention strategies, is hindered by the necessity of performing flood modelling in the process of evaluating solutions. Flood modelling is a computationally demanding task; reducing its impact upon the optimisation process would therefore be a significant achievement and of considerable benefit to this research area. In this thesis sophisticated multi-objective optimisation algorithms have been utilised in combination with cutting-edge flood-risk assessment models to identify least-cost and most-benefit flood risk interventions that can be made on a drainage network. Software analysis and optimisation has improved the flood risk model performance. Additionally, artificial neural networks used as feature detectors have been employed as part of a novel development of an optimisation algorithm. This has alleviated the computational time-demands caused by using extremely complex models. The results from testing indicate that the developed algorithm with feature detectors outperforms (given limited computational resources available) a base multi-objective genetic algorithm. It does so in terms of both dominated hypervolume and a modified convergence metric, at each iteration. This indicates both that a shorter run of the algorithm produces a more optimal result than a similar length run of a chosen base algorithm, and also that a full run to complete convergence takes fewer iterations (and therefore less time) with the new algorithm.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-15T14:16:25Z No. of bitstreams: 1 denisemanueldacostavargas.pdf: 16589539 bytes, checksum: 44a0869db27ffd5f8254f85fb69ab78c (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T17:40:31Z (GMT) No. of bitstreams: 1 denisemanueldacostavargas.pdf: 16589539 bytes, checksum: 44a0869db27ffd5f8254f85fb69ab78c (MD5) / Made available in DSpace on 2016-01-25T17:40:31Z (GMT). No. of bitstreams: 1 denisemanueldacostavargas.pdf: 16589539 bytes, checksum: 44a0869db27ffd5f8254f85fb69ab78c (MD5) Previous issue date: 2015-11-05 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Problemas de Otimização Multiobjetivo (POMs) com restrições são frequentes em diversas áreas das ciências e engenharia, entre elas a Otimização Estrutural (OE). Apesar da Evolução Diferencial (ED) ser uma metaheurística muito atraente na resolução de problemas do mundo real, há uma carência na literatura de discussões sobre o desempenho em POMs de OE. Na sua grande maioria os problemas de OE apresentam restrições. Nesta tese utiliza-se uma técnica para o tratamento de restrições chamada de APM (Adaptive Penalty Method) que tem histórico de bons resultados quando aplicada em problemas monobjetivo de OE. Pelo potencial da ED na resolução de problemas do mundo real e da técnica APM em OE, juntamente com a escassez de trabalhos envolvendo esses elementos em POMs de OE, essa tese apresenta um estudo de um algoritmo bem conhecido de ED acoplado à técnica APM nesses problemas. Experimentos computacionais considerando cenários sem e com inserção de informações de preferência do usuário foram realizados em problemas com variáveis continuas e discretas. Os resultados foram comparados aos encontrados na literatura, além dos obtidos pelo algoritmo que representa o estado da arte. Comparou-se também os resultados obtidos pelo mesmo algoritmo de ED adotado, porém sem ser acoplado à técnica APM, objetivando investigar sua influência no desempenho da combinação proposta. As vantagens e desvantagens do algoritmo proposto em cada cenário são apresentadas nessa tese, além de sugestões para trabalhos futuros. / Multiobjective Optimization Problems (MOPs) with constraints are common in many areas of science and engineering, such as Structural Optimization (SO). In spite of Differential Evolution (DE) being a very attractive metaheuristic in real-world problems, no work was found assessing its performance in SO MOPs. Most OE problems have constraints. This thesis uses the constraint handling technique called Adaptive Penalty Method (APM) that has a history of good results when applied in monobjective problems of SO. Due to the potential of DE in solving real world problems and APM in SO problems, and also with the lack of studies involving these elements in SO MOPs, this work presents a study of a well-known DE algorithm coupled to the APM technique in these problems. Computational experiments considering scenarios with and without inclusion of user preference information were performed in problems with continuous and discrete variables. The results were compared with those in the literature, in addition to those obtained by the algorithm that represents the state of the art. They were also compared to the results obtained by the same DE algorithm adopted, but without the APM technique, aiming at investigating the influence of the APM technique in their performance. The advantages and disadvantages of the proposed algorithm in each scenario are presented in this research, as well as suggestions for future works.

Constructing a road is a complex process that may be represented as a series of correlated steps, from the planning to the construction and usage of the new road. At the heart of this process, the preliminary and detailed design stages are key elements that will ensure the quality and the adequacy of the final solution regarding the constraints and objectives of the project. In particular, infrastructure layout and design will have a strong impact on the global performances of the road in operational conditions. Among them, road safety, mobility, environment preservation, noise pollution limitation, economic feasibility and viability of the project, or even its socio-economic impact at the local level. Consequently, it is crucial to offer engineers and road planners some tools and methods that may assist them in designing and selecting the most efficient solutions considering the distinctive features of each design problem. In this work, a multicriteria analysis methodology is developed to carry out an integrated and preventive assessment of road projects at the design stage by considering both their safety performances and some economic and environmental aspects. Its purpose is to support design engineers in the analysis of their projects and the identification of innovative, consistent and effective solutions. The proposed methodology is composed of two main research frameworks. On the one hand, the road design problem is addressed by focusing successively on the structuring of the multicriteria problem, the identification of the approximate set of non-dominated solutions using a genetic algorithm (based on NSGA-II), and the application of the methodology to a real road design project. On the other hand, the methodological development of a multicriteria interval clustering model was performed (based on PROMETHEE). Due to the applicability of this model to the studied problem, the interactions between the two frameworks are also analysed. / Doctorat en Sciences de l'ingénieur et technologie / The present PhD thesis is an aggregation of published contributions related to the application of multicriteria analysis to the evaluation of road projects at the design stage. The aim of the two introductory chapters is to offer a synthesised and critical presentation of the scientific contributions that constitute the PhD thesis. The complete version of the journal articles and preprints are found in Chapters 3 to 6. In the appendices, we also provide reprints of conference papers that are usually related to one of the main contributions of the thesis. / info:eu-repo/semantics/nonPublished

Les ressources humaines jouent un rôle crucial dans les systèmes de production. D’une part, elles doivent être polyvalentes et flexibles pour pouvoir effectuer des tâches variées. D’autre part, elles doivent être affectées aux différentes machines du système de façon qu’elles puissent satisfaire  les performances industrielles. Toutefois, il est souvent difficile de prendre des décisions concernant le nombre, les compétences et les stratégies d’affectation des opérateurs, en particulier dans les systèmes où la demande est aléatoire, changeante et fluctuante et lorsqu’il y a plusieurs objectifs à optimiser. Dans ce contexte, la présente thèse vise en premier lieu à déterminer comment affecter les opérateurs aux machines. En second lieu, elle vise à définir le nombre d’opérateurs et les compétences requises afin de réduire les durées de fabrication et le coût de la main d’œuvre. Deux heuristiques sont proposées pour affecter les opérateurs de façon dynamique, et en ligne, à chaque fois ils deviennent disponibles. Le choix de la machine se fait en se basant sur une analyse multicritère de l’état actuel du système avec la méthode TOPSIS pour la première heuristique et avec une méthode de restriction basée sur des seuils numériques pour la deuxième. Les critères utilisés ont été choisis à l'aide d’un jeu sérieux (simulation interactive visuelle dédiée) en utilisant le logiciel ARENA. Pour optimiser les poids de ces critères et les seuils numériques et afin d’adapter les heuristiques proposées, nous avons utilisé l’optimisation basée sur la simulation, qui intègre la dimension à la fois aléatoire et fluctuante de la demande. Les résultats des comparaisons avec les stratégies d’affectation les plus utilisées dans la littérature ont montré l’intérêt et la pertinence de nos heuristiques. Pour optimiser le nombre d’opérateurs et leurs compétences, nous avons proposé une approche basée sur la simulation et sur un algorithme évolutionnaire multi objectif (NSGAII). Nous suggérons d’accepter une certaine redondance aux niveaux des compétences afin de réduire l’impact de l’absentéisme des opérateurs sur la performance du système. Pour prendre en compte le comportement changeant et fluctuant des systèmes de production, nous avons intégré dans l’approche d’optimisation l’heuristique d’affectation la plus performante sur les heuristiques proposées. Un modèle de simulation d’un système de type Job-shop a été utilisé pour illustrer notre approche. Les résultats obtenus sur l’ensemble des solutions non dominées ont montré la pertinence de l’approche proposée. / Human resources play a crucial role in production systems. On one hand, they have to be multi-skilled and flexible to carry out various tasks. On the other hand, they must be assigned to the different machines in the system so as to satisfy industrial performances. However, it is often difficult to make decisions about the number, skills and workers assignment strategies, particularly when systems are characterized by random, changing and fluctuating demand and when there are multiple objectives to be optimized. In this context, the present thesis aims first to determine how to assign workers to machines. Second, it aims to define the number of workers and the required skills in order to reduce production times and manpower costs. Two heuristics are then proposed to assign workers dynamically, on line, each time they become idle. The choice of the machine is based on a multi criteria analysis of the current state of the system using TOPSIS method for the first heuristic and a restriction method based on numerical thresholds for the second one. The criteria used are chosen through a serious game (a dedicated Visual Interactive Simulation) implemented using ARENA software. In order to optimize the weights of these criteria and the numerical thresholds and to adapt the proposed heuristics, we used a simulation optimization, which integrates both of the random and fluctuating dimensions of the demand. The results of comparisons with the most used assignment strategies in the literature have shown the interest and relevance of our heuristics. In order to optimize the number of workers and their skills, we proposed an approach based on simulation and an evolutionary multi-objective algorithm (NSGAII). We suggest accepting some redundancy on skills so as to reduce the impact of worker absenteeism on system performance. In order to take into account the changing and fluctuating behavior of production systems, we have integrated in our optimization approach the most efficient assignment heuristic among the proposed ones. A simulation model of a job shop system has been used to illustrate our approach. The results obtained on all non-dominated solutions showed the relevance of the proposed approach.

This master thesis focuses on approximate computing applied to image processing. Specifically, the approximation is applied to adaptive thresholding. Two approaches were used, the design of a new system using approximated components and the approximation of an existing algorithm. The resulting effect on thresholding quality was investigated. Experimental evaluation of the first approach shows quality improvements of thresholding with usage of aproximated components. Also, area of found aproximated solutions is smaller. Evaluation of the second approach shows worse quality of thresholding with usage of aproximated components. The second approach is then declared inappropriate.

This thesis deals with radio network design for a chosen part of a map. Here map is represented by digital map file, which was created within the project DEM. First step is to calculate distances between points in chosen map. With help of optimization algorithms, appropriate position of transceiver in the map and parameters of radio systems are determined, also final coverage by radio signal, represented by intensity of electric field or received power in whole map. The optimization algorithm is used to find the best solution in terms of input parameters (e.g. power of transmitter, height of mast) and resulting coverage of land by radio signal.