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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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.

Decision making is an essential activity for humans and often becomes complex in the presence of uncertainty or insufficient knowledge. This research aims at estimating preferences using pairwise comparisons. A decision maker uses pairwise comparison when he/she is unable to directly assign criteria weights or scores to the available options. The judgments provided in pairwise comparisons may not always be consistent for several reasons. Experimentation has been used to obtain statistical evidence related to the widely-used consistency measures. The results highlight the need to propose new consistency measures. Two new consistency measures - termed congruence and dissonance - are proposed to aid the decision maker in the process of elicitation. Inconsistencies in pairwise comparisons are of two types i.e. cardinal and ordinal. It is shown that both cardinal and ordinal consistency can be improved with the help of these two measures. A heuristic method is then devised to detect and remove intransitive judgments. The results suggest that the devised method is feasible for improving ordinal consistency and is computationally more efficient than the optimization-based methods. There exist situations when revision of judgments is not allowed and prioritization is required without attempting to remove inconsistency. A new prioritization method has been proposed using the graph-theoretic approach. Although the performance of the proposed prioritization method was found to be comparable to other approaches, it has practical limitation in terms of computation time. As a consequence, the problem of prioritization is explored as an optimization problem. A new method based on multi-objective optimization is formulated that offers multiple non-dominated solutions and outperforms all other relevant methods for inconsistent set of judgments. A priority estimation tool (PriEsT) has been developed that implements the proposed consistency measures and prioritization methods. In order to show the benefits of PriEsT, a case study involving Telecom infrastructure selection is presented.

Optimisation is one of the most important and challenging part of any engineering design. In real world design problems one faces multiobjective optimisation under constraints. The optimal solution in these cases is not unique because the objectives can contradict each other. In such cases, a set of optimal solutions which forms a Pareto frontier in the objective space is considered. There are many algorithms to generate the Pareto frontier. However, only a few of them are potentially capable of providing an evenly distributed set of the solutions. Such a property is especially important in real-life design because a decision maker is usually able to analyse only a very limited quantity of solutions. This thesis consists of two main parts. At first, it develops and gives the detailed description of two different algorithms that are able to generate an evenly distributed Pareto set in a general formulation. One is a classical approach and called Directed Search Domain (DSD) and the other, the cylindrical constraint evolutionary algorithm (CCEA), is a hybrid population based method. The efficiency of the algorithms are demonstrated by a number of challenging test cases and the comparisons with the results of the other existing methods. It is shown that the proposed methods are successful in generating the Pareto solutions even when some existing methods fail. In real world design problems, deterministic approaches cannot provide a reliable solution as in the event of uncertainty, deterministic optimal solution would be infeasible in many instances. Therefore a solution less sensitive to problem perturbation is desirable. This leads to the robust solution which is the focus of the second part of the thesis. In the literature, there are some techniques tailored for robust optimisation. However, most of them are either computationally expensive or do not systematically articulate the designer preferences into a robust solution. In this thesis, by introducing a measure for robustness in multiobjective context, a tunable robust function (TRF) is presented. Including the TRF in the problem formulation, it is demonstrated that the desirable robust solution based on designer preferences can be obtained. This not only provides the robust solution but also gives a control over the robustness level. The method is efficient as it only increases the dimension of the problem by one irrespective of the dimension of the original problem.

Orientador: Paulo Augusto Valente Ferreira / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-24T07:32:44Z (GMT). No. of bitstreams: 1 Fontanini_Walcir_D.pdf: 1375688 bytes, checksum: e26761ae454a5f51d8d316afa0718881 (MD5) Previous issue date: 2013 / Resumo: O problema de programação de serviços web é considerado. O processo de tomada de decisão em ambientes de negócios web, descritos por tarefas sequenciais e/ou paralelas, envolve selecionar fornecedores de forma ótima dentre um conjunto potencial de provedores de serviços. Características dos serviços como custo, duração de execução, confiabilidade, disponibilidade e reputação são tratadas como múltiplos objetivos a atingir. Inicialmente, a escolha de provedores é feita por Otimização Multi-Objetivo Inteira-Mista, mais especificamente por meio de um modelo de Programação Alvo. Em seguida, o problema de programação de serviços passa a ser tratado no contexto da Teoria dos Jogos, como um jogo envolvendo provedores que buscam maximizar suas utilidades. Diferentes hipóteses sobre a interação entre provedores dão origem a diferentes noções de equilíbrio: Equilíbrio de Nash, Equilíbrio Correlacionado e Equilíbrio de Bayes-Nash. Finalmente, o problema de programação de serviços é modelado como um problema de Leilão de Segundo Preço, o Mecanismo de Leilão Vickrey-Clark-Grooves. A tese inclui exemplos numéricos ilustrativos para todos os modelos propostos / Abstract: The web services scheduling problem is considered. The decision making process in web-based business environments, described by sequential and/or parallel tasks, involves the optimal selection of suppliers over a set of potential service providers. Characteristics as cost, execution duration, reliability, availability and reputation are treated as multiple objectives to be reached. Initially, the selection of suppliers is performed by Mixed-Integer Multi-Objective Optimization, more specifically, by means of a Goal Programming model. Subsequently, the web services scheduling problem is handled in the Game Theory framework, as a game played by suppliers who aim at maximizing their own utilities. Different hypothesis about the interaction between the suppliers give rise to different equilibrium solutions: Nash Equilibrium, Correlated Equilibrium and Bayes-Nash Equilibrium. Finally, the web services scheduling problem is modeled as a Second Price Auction, the Vickrey-Clark-Grooves Auction Mechnism. The thesis includes illustrative numerical examples for all the models proposed / Doutorado / Automação / Doutor em Engenharia Elétrica

Orientadores: Ieda Geriberto Hidalgo, Paulo de Barros Correia / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-27T18:22:11Z (GMT). No. of bitstreams: 1 Fernandes_JessicaPillonTorralba_D.pdf: 6009989 bytes, checksum: a3f55f4b7f91827762cdfb4e83ebcf4c (MD5) Previous issue date: 2015 / Resumo: Em busca do desenvolvimento sustentável, a atividade de produção de energia iniciou o século XXI com foco em dois temas: eficiência energética e utilização de fontes de energia renováveis. O Brasil é um país privilegiado em termos de disponibilidade de recursos naturais para a geração de energia, principalmente através da água. Apesar da evolução de outras fontes renováveis de energia, como a biomassa e a eólica, é previsto um aumento da utilização de energia hidráulica na geração de eletricidade de forma sustentável. Para acompanhar esse aumento, existe a necessidade de expandir a oferta de energia através da instalação de novas usinas hidrelétricas e/ou otimização da operação das usinas hidrelétricas existentes. Neste contexto, esta tese apresenta uma metodologia para resolver o problema de despacho dinâmico de máquinas e geração com horizonte diário e discretização horária. Ela baseia-se na Programação por Metas Lexicográficas, utilizando Algoritmo Genético e Strength Pareto Evolutionary Algorithm. A formulação matemática do problema possui dois objetivos conflitantes. O primeiro consiste em maximizar a geração líquida da usina ao longo do dia. O segundo visa minimizar o número de partidas e paradas das unidades geradoras. A resolução é executada em duas etapas. Na Etapa 1, o Algoritmo Genético é utilizado para resolver o problema estático para cada hora. Na Etapa 2, Algoritmo Genético e Strength Pareto Evolutionary Algorithm são empregados para solucionar o problema dinâmico ao longo de um dia. As soluções encontradas são analisadas através da construção de uma curva de trade-offs. Os estudos de casos são realizados com as usinas Jupiá e Porto Primavera ,que pertencem ao Sistema Interligado Nacional. Os resultados mostram que a metodologia proposta apresenta soluções eficientes e econômicas para a programação diária de usinas hidrelétricas / Abstract: In pursuit of the sustainable development, the energy production activity began the 21st century with focus on two themes: energy efficiency and use of renewable energy sources. Brazil is a privileged country in terms of availability of natural resources to energy production, mainly through water. Despite the development of other renewable energy sources, such as biomass and wind power, hydro energy is expected to increase in the electricity generation in a sustainable way. To keep this growing, there is a need to increase the supply of energy by installing new hydroelectric plants and/or optimizing the operation of existing ones. In this context, this thesis presents a methodology to solve the dynamic dispatch problem of units and generation with a daily horizon and hourly discretization. It is based on Lexicographic Goal Programming using Genetic Algorithm and Strength Pareto Evolutionary Algorithm. The mathematical formulation of the problem has two conflicting goals. The first consists of maximizing the electric power output the plant throughout the day. The second aims to minimize the number of start-ups and shut-downs of the generating units. The resolution is divided in two steps. In Step 1, Genetic Algorithm is used to solve the static problem for each hour. Phase 2 employs Genetic Algorithm and Strength Pareto Evolutionary Algorithm to solve the dynamic problem throughout the day. The solutions are analyzed by building a trade-offs curve. The case studies are carried out with Jupiá and Porto Primavera hydroelectric power plants that belong to the National Interconnected System. The results show that the proposed methodology provides efficient and economic solutions for the daily operation of hydroelectric power plants / Doutorado / Planejamento de Sistemas Energeticos / Doutora em Planejamento de Sistemas Energéticos

The work is aimed to propose a methodology for creating an equivalent of composite materials used for construction of small aircraft. Such equivalent should enable to create numerical models of small aircraft in the simulation of precertication EMC tests for aircraft resistance against the lightning. Eliminating situations threatening the aircraft and passengers in the initial steps of the design will allow savings in production costs and contribute to the safety of air transport. In order to nd the equivalent of composite materials, global optimization methods will be used.

Multiobjective optimization system of exhaust manifold shapes including initial design has been developed. Space of possible solutions is explored by an evolutionary algorithm. Evaluation of exhaust drains shape comes  from drains length and sum of arc angles. Drains mustn't interfere in surrounding parts. System is tested on set of input data originated from practice. Further, performance of proposed evolutionary algorithm is evaluated.