A operação eficiente do sistema é uma ferramenta fundamental para que sua vida útil se prolongue o máximo possível, garantindo o perfeito atendimento aos consumidores, além de manter os custos com energia elétrica e manutenção dentro de padrões aceitáveis. Para uma eficiente operação, é fundamental o conhecimento do sistema, pois, através deste, com ferramentas como modelos de simulação hidráulica, otimização e definição de regras, é possível fornecer ao operador condições de operacionalidade das unidades do sistema de forma racional, não dependendo exclusivamente de sua experiência pessoal, mantendo a confiabilidade do mesmo. Neste trabalho é desenvolvido um modelo computacional direcionado ao controle operacional ótimo de sistemas de macro distribuição de água potável, utilizando um simulador hidráulico, um algoritmo de otimização, considerando dois objetivos (custos de energia elétrica e benefícios hidráulicos) e um algoritmo de aprendizado para extração de regras operacionais para o sistema. Os estudos foram aplicados no sistema de macro distribuição da cidade de Goiânia. Os resultados demonstraram que podem ser produzidas estratégias operacionais satisfatórias para o sistema em substituição ao julgamento pessoal do operador. / The efficient operation of a system is a fundamental tool to postpone the system’s service life as much as possible, thus ensuring a good service to the consumer while keeping electrical energy and maintenance costs at acceptable levels. Efficient operation requires knowledge of the system, for this knowledge, supported by tools such as models for hydraulic simulation, optimization, and definition of rules, provides the operator with proper conditions for the rational operating of the system’s units without depending exclusively on personal experience while maintaining the system’s reliability. In this work is developed a computational model for the optimal operation control of macro water distribution systems using a hydraulic simulator, an optimization algorithm, and a learn algorithm to extract operational rules (strategies) for the system. These studies are to be based on the macro system of the city of Goiânia, in Brazil. The results show that solutions for satisfactory operation can be quickly produced as a substitute to the personal judgment of the operator.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2014-09-19T11:19:07Z No. of bitstreams: 2 Dissertacao Daniel Vitor de Lucena.pdf: 708978 bytes, checksum: 466a21a76649073c30364b80f17037fc (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-09-19T11:25:02Z (GMT) No. of bitstreams: 2 Dissertacao Daniel Vitor de Lucena.pdf: 708978 bytes, checksum: 466a21a76649073c30364b80f17037fc (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-19T11:25:02Z (GMT). No. of bitstreams: 2 Dissertacao Daniel Vitor de Lucena.pdf: 708978 bytes, checksum: 466a21a76649073c30364b80f17037fc (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2013-05-03 / This work proposes the use of multi-objective genetics algorithms NSGA-II and SPEA-II on the variable selection in multivariate calibration problems. These algorithms are used for selecting variables for a Multiple Linear Regression (MLR) by two conflicting objectives: the prediction error and the used variables number in MLR. For the case study are used wheat data obtained by NIR spectrometry with the objective for determining a variable subgroup with information about protein concentration. The results of traditional techniques of multivariate calibration as the Partial Least Square (PLS) and Successive Projection Algorithm (SPA) for MLR are presents for comparisons. The obtained results showed that the proposed approach obtained better results when compared with a monoobjective evolutionary algorithm and with traditional techniques of multivariate calibration. / Este trabalho propõe a utilização dos algoritmos genéticos multiobjetivo NSGA-II e SPEA-II na seleção de variáveis em problemas de calibração multivariada. Esses algoritmos são utilizados para selecionar variáveis para Regressão Linear Múltipla (MLR) com dois objetivos conflitantes: o erro de predição e do número de variáveis utilizadas na MLR. Para o estudo de caso são usado dados de trigo obtidos por espectrometria NIR com o objetivo de determinar um subgrupo de variáveis com informações sobre a concentração de proteína. Os resultados das técnicas tradicionais de calibração multivariada como dos Mínimos Quadrados Parciais (PLS) e Algoritmo de Projeções Sucessivas (APS) para a MLR estão presentes para comparações. Os resultados obtidos mostraram que a abordagem proposta obteve melhores resultados quando comparado com um algoritmo evolutivo monoobjetivo e com as técnicas tradicionais de calibração multivariada.

The single biggest challenge facing municipalities today is a shortage of funds and labor for upgrading and expanding aging infrastructure. This continued lack of funding impairs the municipalities ability to maintain desired levels of service. Over the last decade, many Canadian municipalities have faced pressures of increasing complexity in infrastructure asset management decision-making which can be partly attributed to cost escalation, increasing service demand and interdependencies between networks. The goal of this research is to develop the framework for Asset Levels of Service (ALOS)-based decision support systems for municipal infrastructure network investment. The proposed framework is based on the fact that ALOS should be one of the main criteria for municipal infrastructure maintenance, repair and rehabilitation (MR&R). Since ALOS is based on qualitative and quantitative parameters, the use of ALOS in municipal infrastructure MR&R decisions will result in improved funding allocation. Secondary parameters used for municipal infrastructure investment decision making in the proposed framework are the physical deterioration of assets, future growth and the impact on the dependent infrastructure network. The proposed framework focuses on funding allocation for the MR&R of municipal networks. The framework is applicable to municipal infrastructure networks, excluding the other assets such as buildings, parks, etc. Application of the proposed framework is demonstrated by its implementation in the case of urban roads. Implementation is carried out in four phases. Phase I involves the quantification of ALOS for urban roads. Quantification of ALOS for urban roads has various challenges such as multiple users and interdependencies of levels of services between various users. An Analytical Hierarchy Process (AHP) has been used to quantify ALOS. Phase II involves the determination of a multiattribute utility function for investment decision. Calculated multiattribute utility of investment decision is used in the multiobjective optimization model in Phase III. In Phase IV, the proposed methodology is incorporated into a computer application called OPTIsys (OPTImum Infrastructure SYStems). OPTIsys will facilitate MR&R decision making based on fully integrated considerations of ALOS, future demand and network interdependencies. Stakeholders benefiting from OPTIsys include the general public, asset-managers, infrastructure departments and municipal councils. OPTIsys will enable infrastructure departments to maintain the operational capability of the network in compliance with the targeted levels of service. Overall, municipalities will be able to reduce the infrastructure deficit while maximizing economic returns. / Construction Engineering and Management

The purpose of this research project is the design and optimization of complex chemical engineering problems, by employing evolutionary algorithms (EAs). EAs are optimization techniques which mimic the principles of genetics and natural selection. Given their population-based approach, EAs are well suited for solving multiobjective optimization problems (MOOPs) to determine Pareto-optimal solutions. The Pareto front refers to the set of non-dominated solutions which highlight trade-offs among the different objectives. A broad range of applications have been studied, all of which are drawn from the chemical engineering field. The design of an industrial packed bed styrene reactor is initially studied with the goal of maximizing the productivity, yield and selectivity of styrene. The dual population evolutionary algorithm (DPEA) was used to circumscribe the Pareto domain of two and three objective optimization case studies for three different configurations of the reactor: adiabatic, steam-injected and isothermal. The Pareto domains were then ranked using the net flow method (NFM), a ranking algorithm that incorporates the knowledge and preferences of an expert into the optimization routine. Next, a multiobjective optimization of the heat transfer area and pumping power of a shell-and-tube heat exchanger is considered to provide the designer with multiple Pareto-optimal solutions which capture the trade-off between the two objectives. The optimization was performed using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) on two case studies from the open literature. The algorithm was also used to determine the impact of using discrete standard values of the tube length, diameter and thickness rather than using continuous values to obtain the optimal heat transfer area and pumping power. In addition, a new hybrid algorithm called the FP-NSGA-II, is developed in this thesis by combining a front prediction algorithm with the fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II). Due to the significant computational time of evaluating objective functions in real life engineering problems, the aim of this hybrid approach is to better approximate the Pareto front of difficult constrained and unconstrained problems while keeping the computational cost similar to NSGA-II. The new algorithm is tested on benchmark problems from the literature and on a heat exchanger network problem.

Recientemente, el uso de técnicas de manipulación genética ha abierto la puerta a la obtención de microorganismos con fenotipos mejorados, lo que a su vez ha llevado a unas mejoras significativas en la síntesis de algunos productos bioquímicos. Sin embargo, la mutación y selección de estos nuevos organismos se ha llevado a cabo, en la mayoría de casos, por ensayo y error. Es de esperar que estos procesos puedan ser mejorados si se usan principios de diseño cuantitativos para guiar la búsqueda hacia el perfil enzimático ideal. Esta tesis está dedicada al desarrollo de un conjunto de herramientas de optimización avanzadas para asesorar en problemas de ingeniería metabólica y otras cuestiones emergentes en biología de sistemas. Concretamente, nos centramos en problemas en qué se modelan las redes metabólicas usando expresiones cinéticas. La utilidad de los algoritmos desarrollados para resolver tales problemas es demostrada por medio de varios casos de estudio. / In recent years, the use of genetic manipulation techniques has opened the door for obtaining microorganisms with enhanced phenotypes, which has in turn led to significant improvements in the synthesis of certain biochemical products. However, mutation and selection of these new organisms has been performed, in most cases, in a trial-and-error basis. It is expected that these processes could be further improved if quantitative design principles were used to guide the search towards the ideal enzymatic profiles. This thesis is devoted to developing a set of advanced global optimization tools to assess metabolic engineering problems and other questions arising in systems biology. In particular, we focus on problems where metabolic networks are modeled making use of kinetic expressions. The usefulness of the algorithms developed to solve such problems is demonstrated by means of several case studies.

Exons are expression DNA sequences. A DNA sequence which includes gene has exons and introns. During transcription and translation, introns will be removed, and exons will remain to become protein. Many researchers need exon primers for PCR experiments. However, it is a difficult to find that many exon primers satisfy all primer design constraints at the same time. Here, we proposed an efficient exon primer design algorithm. The algorithm applies multiobjective genetic algorithm (MGA) instead of the single objective algorithm which can easily lend to unsuitable solutions. And a hash-index algorithm is applied to make specificity checking in a reasonable time. The algorithm has tested by a variety of mRNA sequences. These dry dock experiments show that our proposed algorithm can find primers which satisfy all exon primer design constraints.

The traveling salesman problem (TSP) is defined as: given a finite number of cities along with the cost of travel between each pair of them, find the cheapest way of visiting all the cities only once and returning to your starting city. Some variants of TSP are proposed to visit cities depending on the profit gained when the visit occurs. In literature, these kind of problems are named TSP with profit. In TSP with profit, there are two conflicting objectives, one to collect profit and the other to decrease traveling cost. In literature, TSP with profit are addressed as single objective, either two objectives are combined linearly or one objective is constrained with a specified bound. In this study, a multiobjective approach is developed by combining &amp / #949 / -constrained method and heuristics from the literature in order to find the efficient frontier for the TSP with profit. The performance of approach is tested on the problems studied in the literature. Also an interactive software is developed based on the multiobjective approach.

In Traveling Salesman Problem (TSP) with profits, a profit is associated with each city and the requirement to visit all cities is removed. The purpose is to simultaneously minimize cost (excluding as many cities as possible) and maximize profit (including as many cities as possible). Although the reduced single-objective case of the problem has been well-studied, the true biobjective problem has been studied only by a few researchers. In this paper we study the true biobjective problem using the Multiobjective Genetic Algorithm NSGA II and the Lin-Kernighan Heuristic. We propose several improvements for NSGA II in solving the problem. Based on these improvements, we provide computational results of the approximated Pareto-optimal front for a set of practically large size TSP instances. Finally, we provide a framework and its computational results for a post-optimality analysis to guide the decision maker, using the data mining software Clementine.

We propose an interactive preference-based evolutionary algorithm for the clustering problem. The problem is highly combinatorial and referred to as NP-Hard in the literature. The goal of the problem is putting similar items in the same cluster and dissimilar items into different clusters according to a certain similarity measure, while maintaining some internal objectives such as compactness, connectivity or spatial separation. However, using one of these objectives is often not sufficient to detect different underlying structures in different data sets with clusters having arbitrary shapes and density variations. Thus, the current trend in the clustering literature is growing into the use of multiple objectives as the inadequacy of using a single objective is understood better. The problem is also difficult because the optimal solution is not well defined. To the best of our knowledge, all the multiobjective evolutionary algorithms for the clustering problem try to generate the whole Pareto optimal set. This may not be very useful since majority of the solutions in this set may be uninteresting when presented to the decision maker. In this study, we incorporate the preferences of the decision maker into a well known multiobjective evolutionary algorithm, namely SPEA-2, in the optimization process using reference points and achievement scalarizing functions to find the target clusters.

Wind is one of the most promising resources in the renewable energy portfolio. Still, the cost of electrical power produced by small wind turbines impedes the use of this technology, which can otherwise provide power to millions of homes in rural regions worldwide. To encourage their use, small wind turbines must convert wind energy more effectively while avoiding increased equipment costs. A variable ratio gearbox (VRG) can provide this capability to the simple low-cost fixed-speed wind turbine through discrete operating speeds. The VRG concept is based upon mature technology taken from the automotive industry and is characterized by low cost and high reliability. A 100 kW model characterizes the benefits of integrating a VRG into a fixed-speed stall-regulated wind turbine system. Simulation results suggest it improves the efficiency of the fixed-speed turbine in the partial-load region and has the ability to limit power in the full-load region where pitch control is often used. To maximize electrical production, mechanical braking is applied during the normal operation of the wind turbine. A strategy is used to select gear ratios that produce torque slightly above the maximum amount the generator can accept while simultaneously applying the mechanical brake, so that full-load production may be realized over greater ranges of the wind speed. Dynamic programming is used to establish the VRG ratios and an optimal control design. This optimization strategy maximizes the energy production while insuring that the brake pads maintain a predetermined service life. In the final step of the research, a decision-making algorithm is developed to find the gears that emulate the ratios found in the optimal control design. The objective is to match the energy level as closely as possible, minimize the mass of the gears, and insure that tooth failure does not occur over the design life of the VRG. Recorded wind data of various wind classes is used to quantify the benefit of using the VRG. The results suggest that an optimized VRG design can increase wind energy production by roughly 10% at all of the sites in the study. / text