Orientador: Fernando Augusto da Silva Marins / Resumo: Em diversas áreas de trabalho, da Engenharia à Economia, os problemas se apresentam como sendo multiobjetivos, característica que torna complexa a tomada de decisão. Geralmente, estes objetivos são conflitantes e faz-se necessário o uso de técnicas de otimização para a obtenção de melhores resultados. Na presente dissertação serão estudados alguns métodos para a resolução destes problemas, com o objetivo de aplicar métodos de aglutinação em problemas de projetos de experimentos com múltiplas respostas. Deste modo, inicialmente foi realizada uma análise bibliométrica sobre os diferentes métodos utilizados para a resolução destes problemas. A partir disto, foi desenvolvida uma nova abordagem, utilizando a Programação por Compromisso (Compromise Programming – CP) e a Programação por Metas (Goal Programming – GP), bem como diferentes algoritmos (Gradiente Reduzido Generalizado – GRG e a metaheurística do software Optquest) que são usualmente adotados, com comparação de resultados e análise. De modo geral, esta nova proposta apresentou resultados melhores em relação à abordagem tradicional (desirability), qualificando este procedimento como uma alternativa na otimização de múltiplas respostas. / Mestre

A produção de alimentos, de grande importância para a humanidade, é realizada por meio de processos produtivos muitas vezes responsáveis pelo consumo desordenado de recursos naturais. Tais atividades também são consideradas de alto risco econômico, pois utilizam, em sua maioria, técnicas de baixo rendimento, culturas inapropriadas e até mesmo a utilização de técnicas avançadas, sem o devido controle, como a irrigação, acarretam desperdício de recursos. O uso de técnicas multiobjetivo permite um ordenamento de sistemas produtivos sendo capaz de levar em conta critérios muitas vezes conflitantes e essenciais para a produção agrícola, como: retorno econômico da atividade ao produtor, impacto ambiental, geração de emprego e aproveitamento da mão de obra. O objetivo do presente trabalho é utilizar a técnica multiobjetivo SPEA - Strenght Pareto Evolutionary Algorithm - no auxílio ao produtor rural quanto à tomada de decisão, oferecendo uma alternativa para a gestão de processos produtivos em propriedades rurais do Estado de São Paulo. As técnicas multiobjetivo foram empregadas para obter a ordenação em relação ao tipo de sistema de irrigação a ser adotado, cultura de frutíferas a ser empregada e seus possíveis desempenhos. Investiga-se também o impacto do valor unitário a ser aplicado pelo uso da água de irrigação. Os resultados obtidos permitiram confirmar que a técnica multiobjetivo utilizada se mostrou adequada para as condições adotadas neste trabalho, sendo a cultura da atemóia e o sistema de irrigação por gotejamento as que mais se destacaram em termos de viabilidade econômica de acordo com os parâmetros utilizados. / Food production, of great importance to humanity, is realized through agricultural activities, responsible for disorderly consumption of natural resources. Those activities are considered of high economic risk, for using low yield techniques, inappropriate crops, and even using advanced techniques, such as irrigation, without proper control, leading to natural resources waste. The use of multiobjective techniques allows the ordering of productive systems, being able to consider conflicting criteria such as: economic income, environmental impact, employment generation and workforce utilization. The objective of this study is to use the multiobjective optimization technique SPEA - Strenght Pareto Evolutionary Algorithm - to auxiliate rural producers concerning to decision making, offering an alternative for productive process management for small agricultural properties from São Paulo state, Brazil. The multiobjective techniques were used to obtain the ranking of irrigation system to be adopted, fruit crop and related performance. This work also studies the impact of unit cost to be applied for irrigation water. The results confirmed that the multiobjective technique used was appropriate to the actual conditions of this work, and atemoya crop in a drip irrigation the one that stood out as the best economic performance according to adopted parameters.

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.

Orientadores: Christiano Lyra Filho, Celso Cavellucci / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-25T04:13:51Z (GMT). No. of bitstreams: 1 Assis_LauraSilvade_D.pdf: 3122445 bytes, checksum: 01644f90a086983b8729f81804874faa (MD5) Previous issue date: 2014 / Resumo: Grande parte das falhas em sistemas elétricos de potência ocorrem por consequência de falhas permanentes nas redes de distribuição. Agências reguladoras definem índices de confiabilidade para quantificar e avaliar a qualidade da distribuição de energia. A violação dos limites estabelecidos podem resultar em multas significativas para a distribuidora de energia. Um dos objetivos ao se realizar a instalação de chaves em redes de distribuição é criar a possibilidade de re-energizar a maior quantidade de clientes no menor tempo possível através da transferência de carga para sistemas que não tiveram seu fornecimento de energia interrompido. Esta tese estuda o problema de alocação de chaves (PAC) em sistemas radiais de distribuição de energia elétrica e propõe a instalação otimizada desses dispositivos em locais apropriados das redes, a fim de melhorar a confiabilidade do sistema pela redução do período que os consumidores ficam sem energia. Uma metodologia baseada nos conceitos de algoritmo memético juntamente com uma população estruturada é proposta neste trabalho para alocar chaves seccionadoras e de manobra, manuais e automáticas, com diferentes capacidades. A função objetivo utilizada busca minimizar o custo de alocação das chaves e o custo da energia não distribuída sob restrições de confiabilidade e fluxo de carga em todos os componentes da rede. É apresentado também um estudo multiobjetivo para o PAC, que procura alocar chaves minimizando simultaneamente os custos de instalação das chaves e da energia não distribuída e maximizando a confiabilidade da rede, sob restrições de fluxos. A abordagem proposta para resolver o PAC mono-objetivo também foi utilizada no PAC multiobjetivo, juntamente com o método do ?-restrito. A metodologia proposta tem o seu bom desempenho confirmado por diferentes estudos de casos com redes reais de grande porte localizadas no estado de São Paulo / Abstract: Most failures in electric power systems occur as a result of permanent faults in distribution networks. Regulatory agencies establish reliability standards indices for quantify and evaluate the quality of power distribution. The infringe of established limits can result in costly fines for the utility suppliers. One of the aim when perform the switches allocation in distribution networks is the possibility of re-energize the largest amount of customers in the shortest possible time by transferring load to other power systems which don¿t had their energy supply interrupted. This thesis studies the switch allocation problem (SAP) in radial systems of electrical power distribution and proposes an optimized installation of these devices in appropriate locations of network, in order to improve the reliability system by the reducing of the period that consumers remains without power. A methodology based on the concepts of memetic algorithm with a structured population is proposed in this thesis to allocate sectionalizing and tie switches of different capacities, with manual or automatic operation schemes. The objective function used seeks to minimize the switches allocation and the energy not supplied costs under constraints of reliability and load flow. A Multi-objective study for SAP is presented, to perform the switches allocation seeks minimize simultaneously the switches installation and energy not supplied costs and maximize the network reliability, under constraints of load flow. The proposed approach to solve the SAP monocriteria was also used in SAP multi-criteria along with the ?-constraint method. The proposed methodology has its good performance confirmed by several case studies with real large networks located in the state of São Paulo / Doutorado / Automação / Doutora em Engenharia Elétrica

Depuis le début des années 2000, le secteur social et médico-social connait des évolutions et des mutations importantes. D’un côté, le nombre de personnes prises en charge est en perpétuelle augmentation. D’un autre côté, les finances et les budgets mis à disposition des établissements ne cessent de se réduire, ce qui oblige les décideurs à s’adapter et à trouver de nouvelles solutions pour faire plus avec moins de moyens. Dans cette thèse, nous étudions un problème pratique auquel sont souvent confrontés les directeurs des établissements qui est l’élaboration de plans d’actions optimaux. Un plan d’actions est un ensemble d’actions qui sont mises en place afin d’améliorer à la fois les performances de l’établissement et la qualité de prise en charge de ses résidents.Élaborer un plan d’actions optimal consiste à identifier et choisir les meilleures actions qui améliorent tous les objectifs du plan tout en respectant quelques contraintes. Après la présentation du contexte pratique et théorique, nous fournissons une modélisation formelle du problème sous forme d’un problème de sac-à-dos multi-objectif.Puis nous présentons quelques méthodes de résolution à base d’indicateurs de qualité et de la dominance de Lorenz. Nous montrons que la méthode IBMOLS combinée avec l’indicateur de qualité R2 permet d’obtenir des solutions efficaces et d’intégrer facilement les préférences du décideur. Nous montrons également que dans un contexte où les préférences du décideur sont inconnues ou les objectifs ont tous la même importance, la dominance de Lorenz est un outil très efficace qui permet, d’un côté, d’intégrer l’équité dans le processus de recherche et, d’un autre côté, de réduire le nombre de solutions non dominées ainsi que le temps d’exécution. / Since the early 2000s, the social and medico-social sector is experiencing significant evolutions and mutations. On the one hand, the number of persons taken over is constantly increasing. On the other hand, the finances and budgets available to the structures are constantly decreasing. This forces decision-makers to adapt and find new solutions to do more with fewer resources. In this thesis, we study a practical problem that is often faced by the decision-makers, which is the elaboration of optimal action plans. An action plan is a set of actions that are realized to improve both the performance of the structure and the quality of service offred to its residents. Elaborating an optimal action plan consists of identifying and selecting the best actions that improve all the objectives of the plan while respecting some constraints. After presenting the practical and theoretical context, we provide a formal modeling of the problem as a multi-objective knapsack problem. Then, we present a number of solution methods based on quality indicators and Lorenz dominance. We show that combining IBMOLS method with R2 indicator allows obtaining efficient solutions and easily integrating the decision-maker preferences. We also show that in a context where decision-maker preferences are not known or all the objectives are considered equals, Lorenz dominance is a very efficient tool to incorporate equity into the search process and reduce the number of non-dominated solutions as well as the algorithm runtime.

Multi-objective optimization is a growing field of interest for both theoretical and applied research, mostly due to the higher accuracy with which multi-objective problems (MOPs) model real- world scenarios. While single-objective models simplify real-world problems, MOPs can contain several (and often conflicting) objective functions to be optimized at once. This increased accuracy, however, comes at the expense of a higher difficulty that MOPs pose for optimization algorithms in general, and so a significant research effort has been dedicated to the development of approximate and heuristic algorithms. In particular, a number of proposals concerning the adaptation of evolutionary algorithms (EAs) for multi-objective problems can be seen in the literature, evidencing the interest they have received from the research community.This large number of proposals, however, does not mean that the full search power offered by multi- objective EAs (MOEAs) has been properly exploited. For instance, in an attempt to propose significantly novel algorithms, many authors propose a number of algorithmic components at once, but evaluate their proposed algorithms as monolithic blocks. As a result, each time a novel algorithm is proposed, several questions that should be addressed are left unanswered, such as (i) the effectiveness of individual components, (ii) the benefits and drawbacks of their interactions, and (iii) whether a better algorithm could be devised if some of the selected/proposed components were replaced by alternative options available in the literature. This component-wise view of MOEAs becomes even more important when tackling a new application, since one cannot antecipate how they will perform on the target scenario, neither predict how their components may interact. In order to avoid the expensive experimental campaigns that this analysis would require, many practitioners choose algorithms that in the end present suboptimal performance on the application they intend to solve, wasting much of the potential MOEAs have to offer.In this thesis, we take several significant steps towards redefining the existng algorithmic engineering approach to MOEAs. The first step is the proposal of a flexible and representative algorithmic framework that assembles components originally used by many different MOEAs from the literature, providing a way of seeing algorithms as instantiations of a unified template. In addition, the components of this framework can be freely combined to devise novel algorithms, offering the possibility of tailoring MOEAs according to the given application. We empirically demonstrate the efficacy of this component-wise approach by designing effective MOEAs for different target applications, ranging from continuous to combinatorial optimization. In particular, we show that the MOEAs one can tailor from a collection of algorithmic components is able to outperform the algorithms from which those components were originally gathered. More importantly, the improved MOEAs we present have been designed without manual assistance by means of automatic algorithm design. This algorithm engineering approach considers algorithmic components of flexible frameworks as parameters of a tuning problem, and automatically selects the component combinations that lead to better performance on a given application. In fact, this thesis also represents significant advances in this research direction. Primarily, this is the first work in the literature to investigate this approach for problems with any number of objectives, as well as the first to apply it to MOEAs. Secondarily, our efforts have led to a significant number of improvements in the automatic design methodology applied to multi-objective scenarios, as we have refined several aspects of this methodology to be able to produce better quality algorithms.A second significant contribution of this thesis concerns understanding the effectiveness of MOEAs (and in particular of their components) on the application domains we consider. Concerning combina- torial optimization, we have conducted several investigations on the multi-objective permutation flowshop problem (MO-PFSP) with four variants differing as to the number and nature of their objectives. Through thorough experimental campaigns, we have shown that some components are only effective when jointly used. In addition, we have demonstrated that well-known algorithms could easily be improved by replacing some of their components by other existing proposals from the literature. Regarding continuous optimization, we have conducted a thorough and comprehensive performance assessment of MOEAs and their components, a concrete first step towards clearly defining the state-of-the-art for this field. In particular, this assessment also encompasses many-objective optimization problems (MaOPs), a sub-field within multi-objective optimization that has recently stirred the MOEA community given its theoretical and practical demands. In fact, our analysis is instrumental to better understand the application of MOEAs to MaOPs, as we have discussed a number of important insights for this field. Among the most relevant, we highlight the empirical verification of performance metric correlations, and also the interactions between structural problem characteristics and the difficulty increase incurred by the high number of objectives.The last significant contribution from this thesis concerns the previously mentioned automatically generated MOEAs. In an initial feasibility study, we have shown that MOEAs automatically generated from our framework are able to consistently outperform the original MOEAs from where its components were gathered both for the MO-PFSP and for MOPs/MaOPs. The major contribution from this subset, however, regards continuous optimization, as we significantly advance the state-of-the-art for this field. To accomplish this goal, we have extended our framework to encompass approaches that are primarily used for this continuous problems, although the conceptual modeling we use is general enough to be applied to any domain. From this extended framework we have then automatically designed state-of- the-art MOEAs for a wide range of experimental scenarios. Moreover, we have conducted an in-depth analysis to explain their effectiveness, correlating the role of algorithmic components with experimental factors such as the stopping criterion or the performance metric adopted.Finally, we highlight that the contributions of this thesis have been increasingly recognized by the scientific community. In particular, the contributions to the research of MOEAs applied to continuous optimization are remarkable given that this is the primary application domain for MOEAs, having been extensively studied for a couple decades now. As a result, chapters from this work have been accepted for publication in some of the best conferences and journals from our field. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

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L’application aux procédés agroalimentaires des approches d'écoconception combinant modélisation et optimisation multiobjectif est freinée par un manque de modèles de procédé intégrant les caractéristiques du produit. Ce travail consiste à développer un cadre méthodologique d'écoconception de procédés agroalimentaires combinant modélisation et optimisation, avec pour support l'exemple du procédé d'évaporation du lait. Le procédé est modélisé à l’aide d’un simulateur de procédés commercial, auquel sont intégrés des modèles de propriétés du produit et de coefficient d'échange sélectionnés dans la littérature. Le nettoyage est pris en compte à l’aide d’un outil de calcul des inventaires d'une séquence de nettoyage en place. Des critères économiques et environnementaux sont calculés pour analyser les solutions de conception du procédé.L'optimisation multiobjectif est réalisée à l'aide d'un algorithme génétique, et une méthode d'aide à la décision permet d'identifier les meilleures solutions de compromis. Les potentialités du cadre méthodologique sont illustrées dans trois études d’écoconception. Les perspectives de ce travail portent sur l’écoconception de la production de poudre de lait incluant des opérations à membranes et du séchage, l’utilisation de méthodes d’intégration énergétique, et la prise en compte de la qualité du produit.L'optimisation multiobjectif est réalisée à l'aide d'un algorithme génétique, et une méthode d'aide à la décision permet d'identifier les meilleures solutions de compromis. Les potentialités du cadre méthodologique sont illustrées dans tro / The application of eco-design approaches to food processes is yet hampered by a lack of process models which incorporate product attributes. This thesis consists in developing a methodological framework for the eco-design of food processes, which combines simulation and multiobjective optimisation. This development is supported by the example of the evaporation of milk. The process is modelled with a commercial process simulator, to which property models of the product and heat transfer coefficient models, selected from the literature, are integrated. Cleaning is taken into account with a simplified tool for the calculation of the inventories of a cleaning-in-place sequence.Economic and environmental criteria are computed, so as to analyse the different design solutions. Multiobjective optimisation is carried out with a genetic algorithm, and a multicriteria decision-making method identifies the solutions which offer the best compromise. The potential of the framework is demonstrated through three eco-design studies. This work paves the way for the eco-design of the milk powder production including membrane operations and drying, the use of energy integration methods, and the integration of product quality.

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.

The motif discovery problem is becoming increasingly important for molecular biologists as new sequencing technologies are producing large amounts of data, at rates which are unprecedented. The solution space for DNA motifs is too large to search with naive methods, meaning there is a need for fast and accurate motif detection tools. We propose MotifGP, a multiobjective motif discovery tool evolving regular expressions that characterize overrepresented motifs in a given input dataset. This thesis describes and evaluates a multiobjective strongly typed genetic programming algorithm for the discovery of network expressions in DNA sequences. Using 13 realistic data sets, we compare the results of our tool, MotifGP, to that of DREME, a state-of-art program. MotifGP outperforms DREME when the motifs to be sought are long, and the specificity is distributed over the length of the motif. For shorter motifs, the performance of MotifGP compares favourably with the state-of-the-art method. Finally, we discuss the advantages of multi-objective optimization in the context of this specific motif discovery problem.