Neste trabalho são apresentadas contribuições para a estimação do desempenho elétrico na distribuição de energia elétrica, com implicações nos mais diversos problemas da operação e do planejamento da distribuição. Entende-se por desempenho elétrico, a avaliação dos parâmetros de congestionamento de redes, as perdas e o nível de tensão. A motivação deste trabalho está na agregação dos esforços advindos da campanha de medição compulsória das concessionárias de distribuição e da necessidade do órgão regulador de estabelecer parâmetros de avaliação do desempenho operacional das empresas, como previsto no documento intitulado “Procedimentos da Distribuição” da Aneel. A estimação do desempenho elétrico é formulada segundo um problema de otimização multiobjetivo onde as funções objetivo compõem uma avaliação de probabilidade de ocorrência e uma avaliação de proximidade dos parâmetros elétricos calculados com os valores obtidos por medição. Os valores das cargas são discretizados segundo probabilidades de ocorrência em cada intervalo, de modo que a formulação resulte em um problema de otimização combinatória multiobjetivo de dimensão exponencial. Propõe-se um procedimento de redução de rede, que diminua consideravelmente o espaço de decisões, e um procedimento de expansão de redes para recompô-la. Também são propostas heurísticas específicas para a obtenção de soluções com cargas diversificadas e desequilibradas. Para uma aplicação adequada destas heurísticas, propôs-se e aplicou-se um método evolucionário metaheurístico para composição das soluções factíveis, ordenadas de acordo com o conceito de dominância de Pareto. Para cada fronteira de dominância, ou conjunto de fronteiras, o aplicativo constrói a distribuição probabilística da corrente e fluxo de potência de cada trecho, o nível de tensão em todas as barras e as perdas técnicas totais do circuito. A formulação matemática de otimização é flexível o bastante para a aplicação prática, considerando os diversos estágios de implementação dos atuais sistemas supervisórios. O modelo evolucionário metaheurístico proposto foi aplicado para um caso ilustrativo evidenciando as suas potencialidades e os pontos a serem aprimorados. / This thesis aims at contributing for the estimation of electrical performance in the distribution of electrical energy. Electrical performance is assumed to be the evaluation of network congestion parameters, losses and voltage level. The development of this work was impelled due to distribution utilities compulsory measurement permanent campaigns, and due to the need of the regulatory agency in establishing operational performance standards, as stated in the Distribution Code of Aneel, the Brazilian Energy Regulatory Agency. The electrical performance estimation is formulated according to an optimization problem where the objective functions correspond to an evaluation of occurrence probability, and correspond to a proximity evaluation of calculated parameters with values obtained by measurement as well. Load values are discretized according to ocurrence probabilities within each interval, so that formulation results in a multiobjective combinatorial optimization of exponential dimension. Network reduction procedures to substantially reduce Decision Domain and network expansion procedures to recompose it are proposed. Specific heuristics are also proposed to get solutions with load diversity and unbalanced loads. In order to adequately apply these heuristics, a metaheuristic evolutionary method to build feasible solutions is proposed and applied, and ranked according to Pareto´s concept. For each dominance frontier or group of frontiers, the application builds the probabilistic: current and load flow distribution of for each branch, voltage level for each bar and circuit technical losses. The mathematical formulation of optimization is flexible enough to be effectively applied taking into account different levels of supervisory systems developed in the utilities. The metaheuristic evolutionary model proposed was applied to a representative case with main potentialities and weak points to be improved.

Neste trabalho são apresentadas contribuições para a estimação do desempenho elétrico na distribuição de energia elétrica, com implicações nos mais diversos problemas da operação e do planejamento da distribuição. Entende-se por desempenho elétrico, a avaliação dos parâmetros de congestionamento de redes, as perdas e o nível de tensão. A motivação deste trabalho está na agregação dos esforços advindos da campanha de medição compulsória das concessionárias de distribuição e da necessidade do órgão regulador de estabelecer parâmetros de avaliação do desempenho operacional das empresas, como previsto no documento intitulado “Procedimentos da Distribuição" da Aneel. A estimação do desempenho elétrico é formulada segundo um problema de otimização multiobjetivo onde as funções objetivo compõem uma avaliação de probabilidade de ocorrência e uma avaliação de proximidade dos parâmetros elétricos calculados com os valores obtidos por medição. Os valores das cargas são discretizados segundo probabilidades de ocorrência em cada intervalo, de modo que a formulação resulte em um problema de otimização combinatória multiobjetivo de dimensão exponencial. Propõe-se um procedimento de redução de rede, que diminua consideravelmente o espaço de decisões, e um procedimento de expansão de redes para recompô-la. Também são propostas heurísticas específicas para a obtenção de soluções com cargas diversificadas e desequilibradas. Para uma aplicação adequada destas heurísticas, propôs-se e aplicou-se um método evolucionário metaheurístico para composição das soluções factíveis, ordenadas de acordo com o conceito de dominância de Pareto. Para cada fronteira de dominância, ou conjunto de fronteiras, o aplicativo constrói a distribuição probabilística da corrente e fluxo de potência de cada trecho, o nível de tensão em todas as barras e as perdas técnicas totais do circuito. A formulação matemática de otimização é flexível o bastante para a aplicação prática, considerando os diversos estágios de implementação dos atuais sistemas supervisórios. O modelo evolucionário metaheurístico proposto foi aplicado para um caso ilustrativo evidenciando as suas potencialidades e os pontos a serem aprimorados. / This thesis aims at contributing for the estimation of electrical performance in the distribution of electrical energy. Electrical performance is assumed to be the evaluation of network congestion parameters, losses and voltage level. The development of this work was impelled due to distribution utilities compulsory measurement permanent campaigns, and due to the need of the regulatory agency in establishing operational performance standards, as stated in the Distribution Code of Aneel, the Brazilian Energy Regulatory Agency. The electrical performance estimation is formulated according to an optimization problem where the objective functions correspond to an evaluation of occurrence probability, and correspond to a proximity evaluation of calculated parameters with values obtained by measurement as well. Load values are discretized according to ocurrence probabilities within each interval, so that formulation results in a multiobjective combinatorial optimization of exponential dimension. Network reduction procedures to substantially reduce Decision Domain and network expansion procedures to recompose it are proposed. Specific heuristics are also proposed to get solutions with load diversity and unbalanced loads. In order to adequately apply these heuristics, a metaheuristic evolutionary method to build feasible solutions is proposed and applied, and ranked according to Pareto´s concept. For each dominance frontier or group of frontiers, the application builds the probabilistic: current and load flow distribution of for each branch, voltage level for each bar and circuit technical losses. The mathematical formulation of optimization is flexible enough to be effectively applied taking into account different levels of supervisory systems developed in the utilities. The metaheuristic evolutionary model proposed was applied to a representative case with main potentialities and weak points to be improved.

A capacidade das células para responder corretamente a sinais externos e perceber mudanças no seu microambiente é a base do desenvolvimento, reparação de tecidos e de imunidade, bem como a homeostase do tecido normal. Transdução de sinal é o principal meio pelo qual as células respondem a sinais externos de seu ambiente e coordenam alterações celulares complexas. O estudo das vias de sinalização molecular permite-nos tentar compreender o funcionamento dessas transduções de sinais e, consequentemente, as respostas celulares a estímulos externos. Uma abordagem adequada para tais estudos é o uso de modelos matemáticos para simular a cinética das reações químicas que descrevem uma dada via de sinalização, o que nos permite gerar predições testáveis de processos celulares. Construir modelos cinéticos preditivos de vias de sinalização molecular através de dados de alto rendimento produzidos utilizando técnicas ômicas (i.e., genômica, transcriptômica, (fosfo-)proteômica) constitui um dos atuais desafios enfrentados pelos pesquisadores na área de Biologia Molecular. Recentemente, para lidar com este desafio, o arcabouço de e-Science SigNetSim foi introduzido pelo Grupo de Biologia Computacional e de Bioinformática do Instituto Butantan. Esse arcabouço permite fazer a descrição de vias de sinalização molecular através da descrição da estrutura de um modelo através de um conjunto de reações químicas, que por sua vez é mapeado para um sistema de Equações Diferencias Ordinárias (EDOs), numericamente simuladas e avaliadas. Todavia, modificações na estrutura das vias precisam ser feitas manualmente, o qual restringe severamente o número de estruturas da via que precisam ser testadas, especialmente no caso de modelos grandes. Portanto, diante desse panorama, este trabalho propõe o desenvolvimento de um método para modificar vias de sinalização molecular. Esse método se baseia no uso de bancos de dados de interatomas para fornecer um conjunto de espécies químicas candidatas para serem incluídas na via de sinalização. Um componente integrado ao arcabouço SigNetSim capaz de testar diferentes hipóteses de modificação de vias foi desenvolvido neste projeto utilizando a metodologia de heurística incremental. Para avaliar a eficiência do componente implementado, utilizamos como estudo de caso um modelo de vias sinalização de MAPKs e PI3K/Akt para realizar testes experimentais e analisar os resultados obtidos. / The ability of cells to respond correctly external signals and to perceive changes in their microenvironment is the basis for development, tissue repair and immunity as well as normal tissue homeostasis. Signal transduction is the primary means by which cells respond to external signals from their environment and coordinate complex cellular changes. The study of molecular signaling pathways allows us to understand the operation of each process of cellular signal transduction. The use of mathematical models to simulate the kinetics of chemical reactions that describe a given signaling pathway, allow us to generate testable predictions of the cell processos. To Build Kinetic predictive models to molecular signaling pathways through massive data omics produced using modern techniques, Genomics, transcriptomics, (Phospho) proteomics, is one of the current challenges faced by researchers in the field of molecular biology. Recently, the \\textit SigNetSim e-Science was introduced by the Biological Computacional and Bioinformatical Group from the Butantan Institute to face this challenge. This \\textit makes the description of molecular signaling pathways through a set of chemical reactions, which are mapped into a system of ordinary differential equations, this system will be numerically simulated and evaluated . However, changes in the structure of the pathways need to be updated manually presented in this work, which severely restricts the number of track structures that need to be tested, especially for the large models. Therefore, given this background, we present the method to modify the molecular signaling pathways. This method relies on the use of interactome database to provide a set of chemical species candidates to be included in the signaling pathway. An component integrated to SigNetSim framework able to test different hypotheses of pathways modification was developed in this project using the incremental heuristic methodology. To evaluate the implemented component, we used the MAPKs and PI3K/Akt pathways model as case study, in order to perform experimental tests and to analyze the obtained results.

La résolution efficace de problèmes d'optimisation a permutation de grande taille nécessite le développement de méthodes hybrides complexes combinant différentes classes d'algorithmes d'optimisation. L'hybridation des metaheuristiques avec les méthodes exactes arborescentes, tel que l'algorithme du branch-and-bound (B&B), engendre une nouvelle classe d'algorithmes plus efficace que ces deux classes de méthodes utilisées séparément. Le défi principal dans le développement de telles méthodes consiste a trouver des liens ou connections entre les stratégies de recherches divergentes utilisées dans les deux classes de méthodes. Les Algorithmes Genetiques (AGs) sont des metaheuristiques, a base de population, tr'es populaires bas'es sur des op'erateurs stochastiques inspirés de la théorie de l'évolution. Contrairement aux AGs et aux m'etaheuristiques généralement, les algorithmes de B&B sont basées sur l'énumération implicite de l'espace de recherche représente par le moyen d'un arbre, dit arbre de recherche. Notre approche d'hybridation consiste a définir un codage commun des solutions et de l'espace de recherche ainsi que des opérateurs de recherche ad'equats afin de permettre un couplage efficace de bas niveau entre les deux classes de méthodes AGs et B&B. La représentation de l'espace de recherche par le moyen d'arbres est traditionnellement utilis'ee dans les algorithmes de B&B. Dans cette thèse, cette représentation a été adaptée aux metaheuristiques. L'encodage des permutations au moyen de nombres naturels faisant référence a l'ordre d'énumération lexicographique des permutations dans l'arbre du B&B, est proposé comme une nouvelle manière de représenter l'espace de recherche des problèmes 'a permutations dans les metaheuristiques. Cette méthode de codage est basée sur les propriétés mathématiques des permutations, 'a savoir les codes de Lehmer et les tables d'inversions ainsi que les système d'énumération factoriels. Des fonctions de transformation permettant le passage entre les deux représentations (permutations et nombres) ainsi que des opérateurs de recherche adaptes au codage, sont définis pour les problèmes 'a permutations généralisés. Cette représentation, désormais commune aux metaheuristiques et aux algorithmes de B&B, nous a permis de concevoir des stratégies d'hybridation et de collaboration efficaces entre les AGs et le B&B. En effet, deux approches d'hybridation entre les AGs et les algorithmes de B&B (HGABB et COBBIGA) bas'es sur cette représentation commune ont été proposées dans cette thèse. Pour validation, une implémentation a été réalisée pour le problème d'affectation quadratique 'a trois dimension (Q3AP). Afin de résoudre de larges instances de ce problème, nous avons aussi propose une parallélisation pour les deux algorithme hybrides, basée sur des techniques de décomposition d'espace (décomposition par intervalle) utilisées auparavant pour la parallélisation des algorithmes de B&B. Du point de vue implémentation, afin de faciliter de futurs conceptions et implémentations de méthodes hybrides combinant metaheuristiques et méthodes exacte arborescentes, nous avons développe une plateforme d'hybridation intégrée au logiciel pour metaheuristiques, ParadisEO. La nouvelle plateforme a été utilisée pour réaliser des expérimentations intensives sur la grille de calcul Grid'5000.

The minimum set cover problem is, without question, among the most ubiquitous and well-studied problems in computer science. Its theoretical hardness has been fully characterized--logarithmic approximability has been established, and no sublogarithmic approximation exists unless P=NP. However, the gap between real-world instances and the theoretical worst case is often immense--many covering problems of practical relevance admit much better approximations, or even solvability in polynomial time. Simple combinatorial or geometric structure can often be exploited to obtain improved algorithms on a problem-by-problem basis, but there is no general method of determining the extent to which this is possible. In this thesis, we aim to shed light on the relationship between the structure and the hardness of covering problems. We discuss several measures of structural complexity of set cover instances and prove new algorithmic and hardness results linking the approximability of a set cover problem to its underlying structure. In particular, we provide: - An APX-hardness proof for a wide family of problems that encode a simple covering problem known as Special-3SC. - A class of polynomial dynamic programming algorithms for a group of weighted geometric set cover problems having simple structure. - A simplified quasi-uniform sampling algorithm that yields improved approximations for weighted covering problems having low cell complexity or geometric union complexity. - Applications of the above to various capacitated covering problems via linear programming strengthening and rounding. In total, we obtain new results for dozens of covering problems exhibiting geometric or combinatorial structure. We tabulate these problems and classify them according to their approximability.

Harmony search method based optimum design algorithm is presented for the grillage systems. This numerical optimization technique imitates the musical performance process that takes place when a musician searches for a better state of harmony. For instance, jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The design algorithm considers the displacement and strength constraints which are implemented from LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Construction). It selects the appropriate W (Wide Flange)-sections for the transverse and longitudinal beams of the grillage system among 272 discrete W-section designations given in LRFD-AISC so that the design limitations described in LRFD are satisfied and the weight of the system is confined to be minimal. Number of design examples is considered to demonstrate the efficiency of the algorithm presented.

Due to the complexity of multi-objective combinatorial optimization problems (MOCO), metaheuristics like multi-objective evolutionary algorithms (MOEA) are gaining importance to obtain a well-converged and well-dispersed Pareto-optimal frontier approximation. In this study, of the well-known MOCO problems, single-dimensional multi-objective knapsack problem and multi-objective assignment problem are taken into consideration. We develop a steady-state and elitist MOEA in order to approximate the Pareto-optimal frontiers. We utilize a territory concept in order to provide diversity over the Pareto-optimal frontiers of various problem instances. The motivation behind the territory definition is to attach the algorithm the advantage of fast execution by eliminating the need for an explicit diversity preserving operator. We also develop an interactive preference incorporation mechanism to converge to the regions that are of special interest for the decision maker by interacting with him/her during the optimization process.

The expected growth of civil aviation over the next twenty years places significant emphasis on revolutionary technology development aimed at mitigating the environmental impact of commercial aircraft. As the number of technology alternatives grows along with model complexity, current methods for Pareto finding and multiobjective optimization quickly become computationally infeasible. Coupled with the large uncertainty in the early stages of design, optimal designs are sought while avoiding the computational burden of excessive function calls when a single design change or technology assumption could alter the results. This motivates the need for a robust and efficient evaluation methodology for quantitative assessment of competing concepts. This research presents a novel approach that combines Bayesian adaptive sampling with surrogate-based optimization to efficiently place designs near Pareto frontier intersections of competing concepts. Efficiency is increased over sequential multiobjective optimization by focusing computational resources specifically on the location in the design space where optimality shifts between concepts. At the intersection of Pareto frontiers, the selection decisions are most sensitive to preferences place on the objectives, and small perturbations can lead to vastly different final designs. These concepts are incorporated into an evaluation methodology that ultimately reduces the number of failed cases, infeasible designs, and Pareto dominated solutions across all concepts. A set of algebraic samples along with a truss design problem are presented as canonical examples for the proposed approach. The methodology is applied to the design of ultra-high bypass ratio turbofans to guide NASA's technology development efforts for future aircraft. Geared-drive and variable geometry bypass nozzle concepts are explored as enablers for increased bypass ratio and potential alternatives over traditional configurations. The method is shown to improve sampling efficiency and provide clusters of feasible designs that motivate a shift towards revolutionary technologies that reduce fuel burn, emissions, and noise on future aircraft.

A Bayesian adaptive sampling method is developed for highly coupled multidisciplinary design problems. The method addresses a major challenge in aerospace design: exploration of a design space with computationally expensive analysis tools such as computational fluid dynamics (CFD) or finite element analysis. With a limited analysis budget, it is often impossible to optimize directly or to explore a design space with off-line design of experiments (DoE) and surrogate models. This difficulty is magnified in multidisciplinary problems with feedbacks between disciplines because each design point may require iterative analyses to converge on a compatible solution between different disciplines. Bayesian Collaborative Sampling (BCS) is a bi-level architecture for adaptive sampling that simulataneously - concentrates disciplinary analyses in regions of a design space that are favorable to a system-level objective - guides analyses to regions where interdisciplinary coupling variables are probably compatible BCS uses Bayesian models and sequential sampling techniques along with elements of the collaborative optimization (CO) architecture for multidisciplinary optimization. The method is tested with the aero-structural design of a glider wing and the aero-propulsion design of a turbojet engine nacelle.

Thesis (Ph.D.)--Mississippi State University. Department of Industrial and Systems Engineering. / Title from title screen. Includes bibliographical references.