O problema de Planejamento da Expansão de Sistemas de Distribuição (PESD) visa determinar diretrizes para a expansão da rede considerando a crescente demanda dos consumidores. Nesse contexto, as empresas distribuidoras de energia elétrica têm o papel de propor ações no sistema de distribuição com o intuito de adequar o fornecimento da energia aos padrões exigidos pelos órgãos reguladores. Tradicionalmente considera-se apenas a minimização do custo global de investimento de planos de expansão, negligenciando-se questões de confiabilidade e robustez do sistema. Como consequência, os planos de expansão obtidos levam o sistema de distribuição a configurações que são vulneráveis a elevados cortes de carga na ocorrência de contingências na rede. Este trabalho busca a elaboração de uma metodologia para inserir questões de confiabilidade e risco ao problema PESD tradicional, com o intuito de escolher planos de expansão que maximizem a robustez da rede e, consequentemente, atenuar os danos causados pelas contingências no sistema. Formulou-se um modelo multiobjetivo do problema PESD em que se minimizam dois objetivos: o custo global (que incorpora custo de investimento, custo de manutenção, custo de operação e custo de produção de energia) e o risco de implantação de planos de expansão. Para ambos os objetivos, são formulados modelos lineares inteiros mistos que são resolvidos utilizando o solver CPLEX através do software GAMS. Para administrar a busca por soluções ótimas, optou-se por programar em linguagem C++ dois Algoritmos Evolutivos: Non-dominated Sorting Genetic Algorithm-2 (NSGA2) e Strength Pareto Evolutionary Algorithm-2 (SPEA2). Esses algoritmos mostraram-se eficazes nessa busca, o que foi constatado através de simulações do planejamento da expansão de dois sistemas testes adaptados da literatura. O conjunto de soluções encontradas nas simulações contém planos de expansão com diferentes níveis de custo global e de risco de implantação, destacando a diversidade das soluções propostas. Algumas dessas topologias são ilustradas para se evidenciar suas diferenças. / The Distribution System Expansion Planning (DSEP) problem aims to determine guidelines to expand the network considering the growing demand of customers. In this context, the distribution companies have to propose actions for improvements in the distribution system in order to adjust the supply of energy to the standards required by regulators. Traditionally minimizing the global cost of expansion plans is the only goal that is considered, thus reliability and robustness issues are neglected. As a result, the optimal expansion plans lead the distribution system to configurations that are vulnerable to high load shedding under the occurrence of contingencies in the network. This work aims to develop a methodology to insert reliability and risk issues to the traditional DSEP problem in order to maximize the robustness of the network and hence mitigate the system damages caused by contingencies. We formulated a multi-objective model of the problem that compromises two objectives: minimization of the global cost (that comprises investment cost, maintenance cost, operational cost, and production cost) and minimization of the deployment risk of expansion plans. For both objectives, we formulated mixed integer linear models which are solved using CPLEX accessed through GAMS. To manage the search for optimal solutions, we chose to implement in C++ language two Evolutionary Algorithms (EAs): Non-dominated Sorting Genetic Algorithm-2 (NSGA2) and Strength Pareto Evolutionary Algorithm-2 (SPEA2). The effectiveness of both algorithms was verified through simulations of the expansion planning of two test systems, adapted from the literature. The set of solutions that has been found contains expansion plans with different levels of global cost and deployment risk. Some of these topologies are depicted to show this diversity of the proposed solutions.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / A teoria dos jogos à um ramo da matemÃtica dedicado à anÃlise das interaÃÃes entre elementos concorrentes, que se encontram em situaÃÃo de conflito, e à formulaÃÃo de estratÃgias de decisÃo. O potencial de aplicaÃÃo desta teoria em sistema de comunicaÃÃo mÃvel à considerado, jà que em alguns dos problemas podem ser identificados elementos em situaÃÃo de conflito. Dois problemas sÃo aqui abordados, a saber, o controle de potencia de transmissÃo e a equalizaÃÃo adaptativa de canal. Ambos estÃo relacionados à interferÃncia, que à um dos mais importantes fatores limitantes do desempenho de sistemas de telefonia celular. O controle de potÃncia de transmissÃo consiste em um procedimento de gerenciamento da interferÃncia de mÃltiplo acesso. Uma nova abordagem deste problema, via teoria dos jogos à considerada nesta dissertaÃÃo, resultando em uma nova deduÃÃo do algoritmo clÃssico de controle de potÃncia DPC (do inglÃs Distributed Power Control). Um novo algoritmo denominado GT-DPC ( do inglÃs Game â Theoretic Distributed Power Control), à desenvolvido e se revela uma forma geral do algoritmo DPC. O algoritmo GT-DPC se mostra mais eficiente em termos de energia do que os algritmos convencionais para serviÃos de qualidade flexÃvel( melhor esforÃo), isto Ã, para um mesmo nÃvel de potÃncia de transmissÃo mais altas do que o DPC. AlÃm disso, este algoritmo permite o gerenciamento dos recursos de potÃncia em cenÃrios de coexistÃncia de serviÃos com diferentes caracterÃsticas. Neste caso, o Lgoritmo à denominado GT-MSDPC ( do inglÃs Game-Theoretic Multi- Service Distributed Power Control).O desempenho dos algoritmos propostos para sistemas de serviÃos à avaliado atravÃs de simulaÃÃes computacionais que emulam os sistemas celulares TDMA( do inglÃs Time Division Multiple Acess) e CDMA(do inglÃs Code Division Multiple Acess). A aplicaÃÃo da teoria dos jogos à equalizaÃÃo adaptativa de canal, que à o procedimento de combate à interferÃncia entre sÃmbolos, està relacionada a situaÃÃes de pior caso. O filtro H( filtro robusto) à derivado atravÃs da aplicaÃÃo de conceitos da teoria dos jogos. AlÃm disso, suas interrelaÃÃes com o filtro de Kalman(RLS) sÃo apresentadas. Por meio de simulaÃÃes computacionais que emulam o sitema de telefonia celular GSM(do inglÃs Global System for Mobile Communications), ambos os fltros tÃm se desempenho como equalizador adaptativo de canal avaliado em dois diferentes cenÃrios. No primeiro deles, diferentes velocidades sÃo atribuÃdas ao usuÃrio, e os resultados mostram que o Rls e o equalizador H apresentam desempenhos comparÃveis. No segundo, considera-se a presenÃa de ruÃdo impulsivo, que pode ser uma consequÃncia do assincronismo de interferÃncia d mÃltiplo acesso, ou que pode ter fontes externas ao sistema de comunicaÃÃo, como a igniÃÃo de motores, llinhas de transmissÃo de energia, fornos de microondas, entre outros. Neste segundo cenÃrio, a robustez do equalizador H fica demonstrda, assim como a degradaÃÃo do desempenho do RLS. Um equalizador hÃbrido RLS-H à proposto, com a obtenÃÃo de ganhos expressivos com respeito ao equalizador RLS convencional. / Game theory is a branch of the Mathematics concerned with the analysis of interactions between competing elements, which are found in conflicting situations, and concerned with the formulation of decision strtegies. This theory is potentially applicable to communications systems problems, since elements in conflicting situations can be identified in some of such problems.Two problems are here considered: the transmit power control and the adaptive channel equalization. Both problems are related to interference, which is one of the most important limiting factors for the cellular system perfomance. Transmit power control consist of a procedure for multi-acess interference management. A new game theoretical approach to power control problem is considered, resulting in a new way to deduce the classical power control algorithm DPC(Distributed Power Con trol). A new algorithm, denoted GT-DPC(Game-theoretic Distributed Power Control), is developed and can be seen as a general form of DPC algorithms for best effort services, since for a unique transmit power level it provides data rates higher than DPC. Furthermore, it allows the power resource management in the presence of services, since for a unique transmit power level it provides data rates higher than DPC. Furthermore, it allows the power resource management in the presence of services with different characteristics. In this case, the algorithm is denoted GT-MSDPC(Game-Theoretic Multi-Service Distributed Power Control). The perfomace of the proposed algorithms for single-service and multi-service systems is demostrated through computational experiments whch simulate TDMA(Time Division Multiple Acess) and CDMA(Code Division Multiple Acess) cellular Systems. The game theory application to adaptive equalization, which is the procedure to combat the intersymbol interference, is related to worst case situations. The H filter(robust filter) is deduced by applying game-theoretic concepts. Furthermore, their relations with the Kalman filter are presented. Through computational experiments wihch simulate GSM(Global System for Mobile Communications) cellular system, both filters have their perfomance as adaptive channel equalizers valued in two different scenarios. In the first one,different speeds are attributed to the user, and results show that both RLS and H equalizer present similar perfomances. In the secon scenario, impulsive noise is considered. Impulsive noise may have external sources, as motors ignition, energy transmission lines or microwaves ovens. In this scenario, the H equalizer robustness is demontrated, so as the RLS perfomance degradation. A hybrid RLS-H equalizer is proposed, obtaining expressive gains with respect to conventional RLS equalizer.

A predição de estruturas de proteínas (Protein Structure Prediction PSP) é um problema computacionalmente complexo. Para tratar esse problema, modelos simplificados de proteínas, como o Modelo HP, têm sido empregados para representar as conformações e Algoritmos Evolutivos (AEs) são utilizados na busca por soluções adequadas para PSP. Entretanto, abordagens utilizando AEs muitas vezes não tratam adequadamente as soluções geradas, prejudicando o desempenho da busca. Neste trabalho, é apresentada uma formulação multiobjetivo para PSP em Modelo HP, de modo a avaliar de forma mais robusta as conformações produzidas combinando uma avaliação baseada no número de contatos hidrofóbicos com a distância entre os monômeros. Foi adotado o Algoritmo Evolutivo Multiobjetivo em Tabelas (AEMT) a fim de otimizar essas métricas. O algoritmo pode adequadamente explorar o espaço de busca com pequeno número de indivíduos. Como consequência, o total de avaliações da função objetivo é significativamente reduzido, gerando um método para PSP utilizando Modelo HP mais rápido e robusto / Protein Structure Prediction (PSP) is a computationally complex problem. To overcome this drawback, simplified models of protein structures, such as the HP Model, together with Evolutionary Algorithms (EAs) have been investigated in order to find appropriate solutions for PSP. EAs with the HP Model have shown interesting results, however, they do not adequately evaluate potential solutions by using only the usual metric of hydrophobic contacts, hamming the performance of the algorithm. In this work, we present a multi-objective approach for PSP using HP Model that performs a better evaluation of the solutions by combining the evaluation based on the number of hydrophobic contacts with the distance among the hydrophobic amino acids. We employ a Multi-objective Evolutionary Algorithm based on Sub-population Tables (MEAT) to deal with these two metrics. MEAT can adequately explore the search space with relatively low number of individuals. As a consequence, the total assessments of the objective function is significantly reduced generating a method for PSP using HP Model that is faster and more robust

Em um ambiente de computação em nuvem, as empresas têm a capacidade de alocar recursos de acordo com a demanda. No entanto, há um atraso que pode levar alguns minutos entre o pedido de um novo recurso e o mesmo estar pronto para uso. Por esse motivo, as técnicas reativas, que solicitam um novo recurso apenas quando o sistema atinge um determinado limiar de carga, não são adequadas para o processo de alocação de recursos. Para resolver esse problema, é necessário prever as requisições que chegam ao sistema, no próximo período de tempo, para alocar os recursos necessários antes que o sistema fique sobrecarregado. Existem vários modelos de previsão de séries temporais para calcular as previsões de carga de trabalho com base no histórico de dados de monitoramento. No entanto, é difícil saber qual é o melhor modelo de previsão a ser utilizado em cada caso. A tarefa se torna ainda mais complicada quando o usuário não tem muitos dados históricos a serem analisados. A maioria dos trabalhos relacionados, considera apenas modelos de previsão isolados para avaliar os resultados. Outros trabalhos propõem uma abordagem que seleciona modelos de previsão adequados para um determinado contexto. Mas, neste caso, é necessário ter uma quantidade significativa de dados para treinar o classificador. Além disso, a melhor solução pode não ser um modelo específico, mas sim uma combinação de modelos. Neste trabalho propomos um método de previsão adaptativo, usando técnicas de otimização multiobjetivo, para combinar modelos de previsão de séries temporais. O nosso método não requer uma fase prévia de treinamento, uma vez que se adapta constantemente a medida em que os dados chegam ao sistema. Para avaliar a nossa proposta usamos quatro logs extraídos de servidores reais. Os resultados mostram que a nossa proposta frequentemente converge para o melhor resultado, e é suficientemente genérica para se adaptar a diferentes tipos de séries temporais. / In a cloud computing environment, companies have the ability to allocate resources according to demand. However, there is a delay that may take minutes between the request for a new resource and it is ready for using. The reactive techniques, which request a new resource only when the system reaches a certain load threshold, are not suitable for the resource allocation process. To address this problem, it is necessary to predict requests that arrive at the system in the next period of time to allocate the necessary resources, before the system becomes overloaded. There are several time-series forecasting models to calculate the workload predictions based on history of monitoring data. However, it is difficult to know which is the best time series forecasting model to be used in each case. The work becomes even more complicated when the user does not have much historical data to be analyzed. Most related work considers only single methods to evaluate the results of the forecast. Other work propose an approach that selects suitable forecasting methods for a given context. But in this case, it is necessary to have a significant amount of data to train the classifier. Moreover, the best solution may not be a specific model, but rather a combination of models. In this work we propose an adaptive prediction method using multi-objective optimization techniques to combine time-series forecasting models. Our method does not require a previous phase of training, because it constantly adapts the extent to which the data is coming. To evaluate our proposal we use four logs extracted from real servers. The results show that our proposal often brings the best result, and is generic enough to adapt to various types of time series.

Matching people to their preferences is an algorithmic topic with real world applications. One such application is the kidney exchange. The best "cure" for patients whose kidneys are failing is to replace it with a healthy one. Unfortunately, biological factors (e.g., blood type) constrain the number of possible replacements. Kidney exchanges seek to alleviate some of this pressure by allowing donors to give their kidney to a patient besides the one they most care about and in turn the donor for that patient gives her kidney to the patient that this first donor most cares about. Roth et al.~first discussed the classic kidney exchange problem. Freedman et al.~expanded upon this work by optimizing an additional objective in addition to maximal matching. In this work, I implement the traditional kidney exchange algorithm as well as expand upon more recent work by considering multi-objective optimization of the exchange. In addition I compare the use of 2-cycles to 3-cycles. I offer two hypotheses regarding the results of my implementation. I end with a summary and a discussion about potential future work.

Traffic signal systems serve as one of the most powerful control tools in improving the efficiency of surface transportation travel. Traffic operations on arterial roads are particularly complex because of traffic interruptions caused by signalized intersections along the corridor. This dissertation research presents a systematic framework of integrated traffic control in an attempt to break down the complexities into several simpler sub-problems such as pattern recognition, environment-mobility relationships and multi-objective optimization for multi-criterial signal timing design. The overall goal of this dissertation is to develop signal timing plans, including a day plan schedule, cycle length parameters, splits and offsets, which are suitable for real traffic conditions with consideration of multi-criterial performance of the surface transportation system. To this end, the specific objectives are to: (1) identify appropriate time-of-day breakpoints and intervals to accommodate traffic pattern variations for day plan schedule of signal timing; (2) explore the relationship between environmental outcomes (e.g., emissions) from emission estimators and mobility measures (e.g., delay and stops) for different types of intersections; (3) optimize signal timing parameters for multi-criteria objectives (e.g., minimizing vehicular delay, number of stops, marginal costs of emissions and total costs), with the comparison of performance metrics for different objectives, at the intersection level; (4) optimize arterial offsets for different objectives at the arterial level and compare the performance metrics of different objectives to recommend suitable objectives for integrated multi-criteria signal timing design in arterial traffic operations. An extensive review of the literature, which covers existing tools, traffic patterns, traffic control with environmental concerns, and related optimization methods, shows that both opportunities and challenges have emerged for multi-criteria traffic signal timing design. These opportunities include large quantities of traffic condition data collected by system detectors or non-intrusive data collection platforms as well as powerful tools for microscopic traffic modeling and instantaneous emission estimation. The challenge is how to effectively deal with these big data, either from field collection or detailed simulation, and provide useful information for decision makers in practice. Methodologically, there's a tradeoff between the accuracy of objective function values and the computational efficiency of simulation and optimization. To address this need, in this dissertation, traffic signal timing design that systematically enables the use of integrated data and models are investigated and analyzed in the four steps/studies. The technology of identifying time-of-day breakpoints in the first study shows a mathematical way to classify dynamic traffic patterns by understanding dynamic traffic features and instabilities at a macroscopic level on arterials. Given the limitations of using built-in emissions modules within current traffic simulation and signal optimization tools, the metamodeling-based approach presented in the second study makes a methodological contribution. The findings of the second study on environment-mobility relationships set up the base for extensive application of two-stage optimization in the third and fourth studies for sustainable traffic operations and management. The comparison of outputs from an advanced estimator with those from the current tool also addresses improving the emissions module for more accurate analysis (e.g., benefit-cost analysis) in practical signal retiming projects. The third study shows that there are tradeoffs between minimizing delay and minimizing marginal costs of emissions. When total cost (including cost of delay, fuel consumption and emissions) is set as a single objective function, that objective clears the way for relatively reliable results for all the aspects. In the fourth study, the improvements in marginal cost of emissions and total cost by dynamic programming procedure are obvious, which indicates the effectiveness of using total link cost as an objective at the corridor level. In summary, this dissertation advocates a sustainable traffic control system by simultaneously considering travel time, fuel consumption and emissions. The outcomes of this integrated multi-criteria signal timing design can be easily implemented by traffic operators in their daily life of retiming signal timing.

This work deals with problems where multiple actors simultaneously take control decisions and implement the corresponding actions in large multi-area power systems. The fact that those actions take place in the same transmission grid introduces a coupling between the various decision-making problems. First, transmission constraints involving all actors' controls must be satisfied, while, second, the satisfaction of an actor's operational objective depends, in general, not only on its own actions but on the others' too. Algorithms and/or operational procedures are, thus, developed seeking to reconcile the multiple actors' simultaneous decisions. The confidentiality and operational autonomy of the actors' decision-making procedures are preserved. In particular, two specific problems leading to such a multi-actor situation have been treated. The first is drawn from a recently emerging situation, at least in Europe, where several Transmission System Operators (TSOs) have installed and/or are planning to install Phase Shifting Transformers (PSTs) in such locations in their areas that, by properly adjusting the PST phase angle settings, they can significantly control the power flows entering and exiting their systems. A general framework is proposed for the control of PSTs owned by several TSOs, taking into account their interactions. The proposed solution is the Nash equilibrium of a sequence of optimizations performed by the various TSOs, each of them taking into account the other TSOs' control settings as well as operating constraints relative to the whole system. The method is applied to a linearized network model and illustrated on the IEEE 118-bus system. The second multi-actor situation dealt with in this work stems from the recently increasing amount of discussions and efforts made towards creating the right market structures and operational practices that would facilitate a seamless inter-area trade of electricity throughout large interconnections. In this respect, in accordance with European Union's goal of a fully functional Internal Electricity Market where ideally every consumer will be able to buy electric energy from every producer all across the interconnection, the possibility of every market participant to place its bid in whatever electricity market of an interconnection has been considered. This results in overlapping markets, each with its own schedule of power injections and withdraws, comprising buses all around the interconnection, that are cleared simultaneously by Transaction Schedulers (TSs). An iterative procedure is proposed to reconcile the various TS schedules such that congestion is managed in a fair and efficient way. The procedure converges to such schedules that the various TS market clearings are in a Nash equilibrium. The method is then extended towards several directions: enabling market participants to place their bids simultaneously in more than one TS's market, incorporating $N-1$ security constraints, allowing for joint energy-reserve dispatch, and, accounting for transmission losses. The corresponding iterative algorithms are thoroughly illustrated in detail on a 15-bus as well as the IEEE RTS-96 system.

Motivated by an electric power portfolio selection problem, a sampling method is developed for simulation-based robust design that builds on existing multi-objective statistical improvement methods. It uses a Bayesian surrogate model regressed on both design and noise variables, and makes use of methods for estimating epistemic model uncertainty in environmental uncertainty metrics. Regions of the design space are sequentially sampled in a manner that balances exploration of unknown designs and exploitation of designs thought to be Pareto optimal, while regions of the noise space are sampled to improve knowledge of the environmental uncertainty. A scalable test problem is used to compare the method with design of experiments (DoE) and crossed array methods, and the method is found to be more efficient for restrictive sample budgets. Experiments with the same test problem are used to study the sensitivity of the methods to numbers of design and noise variables. Lastly, the method is demonstrated on an electric power portfolio simulation code.

In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (GA) are reviewed. Two algorithms, one for single objective and the other for multi-objective problems, which are believed to be more efficient are described in details. The algorithms are coded with MATLAB and applied on several test functions. The results are compared with the existing solutions in literatures and shows promising results. Obtained pareto-fronts are exactly similar to the true pareto-fronts with a good spread of solution throughout the optimal region. Constraint handling techniques are studied and applied in the two algorithms. Constrained benchmarks are optimized and the outcomes show the ability of algorithm in maintaining solutions in the entire pareto-optimal region. In the end, a hybrid method based on the combination of the two algorithms is introduced and the performance is discussed. It is concluded that no significant strength is observed within the approach and more research is required on this topic. For further investigation on the performance of the proposed techniques, implementation on real-world engineering applications are recommended.

The purpose of this dissertation is to address risk and consequences of and effective mitigation strategies for urban fire events involving two critical infrastructures- water distribution and emergency services. Water systems have been identified as one of the United States' critical infrastructures and are vulnerable to various threats caused by natural disasters or malevolent actions. The primary goals of urban water distribution systems are reliable delivery of water during normal and emergency conditions (such as fires), ensuring this water is of acceptable quality, and accomplishing these tasks in a cost-effective manner. Due to interdependency of water systems with other critical infrastructures-e.g., energy, public health, and emergency services (including fire response)- water systems planning and management offers numerous challenges to water utilities and affiliated decision makers. The dissertation is divided into three major sections, each of which presents and demonstrates a methodological innovation applied to the above problem. First, a risk based dynamic programming modeling approach is developed to identify the critical components of a water distribution system during fire events under three failure scenarios: (1) accidental failure due to soil-pipe interaction, (2) accidental failure due to a seismic activity, and (3) intentional failure or malevolent attack. Second, a novel evolutionary computation based multi-objective optimization technique, Non-dominated Sorting Evolution Strategy (NSES), is developed for systematic generation of optimal mitigation strategies for urban fire events for water distribution systems with three competing objectives: (1) minimizing fire damages, (2) minimizing water quality deficiencies, and (3) minimizing the cost of mitigation. Third, a stochastic modeling approach is developed to assess urban fire risk for the coupled water distribution and fire response systems that includes probabilistic expressions for building ignition, WDS failure, and wind direction. Urban fire consequences are evaluated in terms of number of people displaced and cost of property damage. To reduce the assessed urban fire risk, the NSES multi-objective approach is utilized to generate Pareto-optimal solutions that express the tradeoff relationship between risk reduction, mitigation cost, and water quality objectives. The new methodologies are demonstrated through successful application to a realistic case study in water systems planning and management.