Multidisciplinary design optimization (MDO) can be used as an effective tool to improve the design of automotive structures. Large-scale MDO problems typically involve several groups who must work concurrently and autonomously for reasons of efficiency. When performing MDO, a large number of designs need to be rated. Detailed simulation models used to assess automotive design proposals are often computationally expensive to evaluate. A useful MDO process must distribute work to the groups involved and be computationally efficient. In this thesis, MDO methods are assessed in relation to the characteristics of automotive structural applications. Single-level optimization methods have a single optimizer, while multi-level optimization methods have a distributed optimization process. Collaborative optimization and analytical target cascading are possible choices of multi-level optimization methods for automotive structures. They distribute the design process, but are complex. One approach to handle the computationally demanding simulation models involves metamodel-based design optimization (MBDO), where metamodels are used as approximations of the detailed models during optimization studies. Metamodels can be created by individual groups prior to the optimization process, and therefore also offer a way of distributing work. A single-level optimization method in combination with metamodels is concluded to be the most straightforward way of implementing MDO into the development of automotive structures.

A determinação de propriedades físicas dos óleos essenciais é fundamental para sua aplicação na indústria de alimentos e também em projetos de equipamentos. A vasta quantidade de variáveis envolvidas no processo de desterpenação, tais como temperatura, pressão e composição, tornam a utilização de modelos preditivos de viscosidade necessária. Este trabalho teve como objetivo a obtenção de parâmetros para o modelo preditivo de viscosidade UNIFAC-VISCO com aplicação do método de otimização do gradiente descendente, a partir de dados de viscosidade de sistemas modelo que representam as fases que podem ser formadas em processos de desterpenação por extração líquido-líquido dos óleos essenciais de bergamota, limão e hortelã, utilizando como solvente uma mistura de etanol e água, em diferentes composições, a 25ºC. O experimento foi dividido em duas configurações; na primeira os parâmetros de interação previamente reportados na literatura foram mantidos fixos; na segunda todos os parâmetros de interação foram ajustados. O modelo e o método de otimização foram implementados em linguagem MATLAB®. O algoritmo de otimização foi executado 10 vezes para cada configuração, partindo de matrizes de parâmetros de interação iniciais diferentes obtidos pelo método de Monte Carlo. Os resultados foram comparados com o estudo realizado por Florido et al. (2014), no qual foi utilizado algoritmo genético como método de otimização. A primeira configuração obteve desvio médio relativo (DMR) de 1,366 e a segunda configuração resultou um DMR de 1,042. O método do gradiente descendente apresentou melhor desempenho para a primeira configuração em comparação com o método do algoritmo genético (DMR 1,70). Para a segunda configuração o método do algoritmo genético obteve melhor resultado (DMR 0,68). A capacidade preditiva do modelo UNIFAC-VISCO foi avaliada para o sistema de óleo essencial de eucalipto com os parâmetros determinados, obtendo-se DMR iguais a 17,191 e 3,711, para primeira e segunda configuração, respectivamente. Esses valores de DMR foram maiores do que os encontrados por Florido et al. (2014) (3,56 e 1,83 para primeira e segunda configuração, respectivamente). Os parâmetros de maior contribuição para o cálculo do DMR são CH-CH3 e OH-H2O para a primeira e segunda configuração, respectivamente. Os parâmetros que envolvem o grupo C não influenciam no valor do DMR, podendo ser excluído de análises futuras. / The determination of physical properties of essential oils is critical to their application in the food industry and also in equipment design. The large number of variables involved in deterpenation process, such as temperature, pressure and composition, to make use of viscosity predictive models required. This study aimed obtain parameters for the viscosity predictive model UNIFAC-VISCO using gradient descent as optimization method to model systems viscosity data representing the phases that can be formed in deterpenation processes for extraction liquid-liquid of bergamot, lemon and mint essential oils, using aqueous ethanol as solvente in different compositions at 25 º C. The work was divided in two configurations; in the first one the interaction parameters previously reported in the literature were kept fixed; in the second one all interaction parameters were adjusted. The model and the gradient descent method were implemented in MATLAB language. The optimization algorithm was runned 10 times for each configuration, starting from different arrays of initial interaction parameters obtained by the Monte Carlo method. The results were compared with the study carried out by Florido et al. (2014), which used genetic algorithm as optimization method. The first configuration provided an average deviation (DMR) of 1,366 and the second configuration resulted in a DMR 1,042. The gradient descent method showed better results for the first configuration comparing with the genetic algorithm method (DMR 1.70). On the other hand, for the second configuration the genetic algorithm method had a better result (DMR 0.68). The UNIFAC-VISCO model predictive ability was evaluated for eucalyptus essential oil system using the obtained parameters, providing DMR equal to 17.191 and 3.711, for the first and second configuration, respectively. The parameters determined by genetic algorithm presented lower DMR for the two settings (3.56 and 1.83 to the first and second configuration, respectively). The major parameters for calculating the DMR are CH-CH3 and OH-H2O to the first and second configuration, respectively. The parameters involving the C group did not influence the DMR and may be excluded from further analysis.

The results section of my thesis includes three chapters. The first two chapters are on theoretical game theory. In both chapters, by mathematical modelling and game theoretical tools, I am predicting the behaviour of the players in some real world issues. Hoteling-Downs model plays an important role in the modern political interpretations. The first chapter of this study investigates an extension of Hoteling-Downs model to have multi-dimensional strategy space and asymmetric candidates. Chapter 3 looks into the inspection game where the inspections are not the same in the series of sequential inspections. By modelling the game as a series of recursive zero-sum games I find the optimal strategy of the players in the equilibrium. The forth chapter investigates direct optimization methods for large scale problems. Using Matlab implementations of Genetic and Nelder-Mead algorithms, I compare the efficiency and accuracy of the most famous direct optimization methods for unconstraint optimization problems based on differing number of variables.

No presente trabalho, estudamos alguns conceitos relacionados ao desenvolvimento de programas paralelos, algumas formas de aplicar computação paralela em métodos de otimização contínua e dois métodos que envolvem o uso de otimização. O primeiro método que apresentamos, chamado PUMA (Pointwise Unconstrained Minimization Approach), recupera constantes óticas e espessuras de filmes finos a partir de valores de transmitância. O problema de recuperação é modelado como um problema inverso e resolvido com auxílio de um método de otimização. Através da paralelização do PUMA viabilizamos a recuperação empírica de constantes e espessuras de sistemas compostos por até dois filmes sobrepostos. Relatamos aqui os resultados obtidos e discutimos o desempenho da versão paralela e a qualidade dos resultados obtidos. O segundo método estudado tem o objetivo de obter configurações iniciais de moléculas para simulações de dinâmica molecular e é chamado PACKMOL. O problema de obter uma configuração inicial de moléculas é modelado como um problema de empacotamento e resolvido com o auxílio de um método de otimização. Construímos uma versão paralela do PACKMOL e mostramos os ganhos de desempenho obtidos com a paralelização. / In this work we studied some concepts of parallel programming, some ways of using parallel computing in continuous optimization methods and two optimization methods. The first method we present is called PUMA (Pointwise Unconstrained Minimization Approach), and it retrieves optical constants and thicknesses of thin films from transmitance data. The problem of retrieve thickness and optical constants is modeled as an inverse problem and solved with aid of an optimization method. Through the paralelization of PUMA we managed to retrieve optical constants and thicknesses of thin films in structures with one and two superposed films. We describe some results and discuss the performance of the parallel PUMA and the quality of the retrievals. The second studied method is used to build an initial configuration of molecules for molecular dynamics simulations and it is called PACKMOL. The problem of create an initial configuration of molecules is modeled as a packing problem and solved with aid of an optimization method. We developed a parallel version of PACKMOL and we show the obtained performance gains.

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / The application of heuristics optimization has proven quite effective when compared to traditional optimization methods by differentiation. It is known that in some cases, directly minimizing the functions involved in the process can be complicated or even impossible. This work aims to develop a Weibull curve fitting methodology, using Ant Colony Optimization method and the Particle Swarm Optimization method as far as the hybridization of these. The tipical stochastic characteristic should provide good results for any wind distribution, concentrated or dispersed, which would make it valid to use in coastal conditions, flat or complex terrain or even urban. The result obtained by the heuristic approach of two SONDA wind samples, referring to Petrolina, Pernambuco, Brazil and Sao Martinho da Serra, Rio Grande do Sul, Brazil was compared with eight other known methods and commercially applied: the least squares method, the moment method, empirical method, the maximum likelihood method, the modified maximum likelihood method, energy pattern method, equivalent energy method and the chi-squared method and the goodness of fit will be evaluated by RMSE tests, MAPE, R2, and the deviation in the forecast power density. Heuristic methods have proven competitive, with power forecast error values around 10−14%. / A aplicaÃÃo de mÃtodos heurÃsticos em otimizaÃÃo tem se mostrado bastante eficaz quando comparado aos tradicionais mÃtodos de otimizaÃÃo por diferenciaÃÃo. à sabido que, em alguns casos, minimizar de forma direta as funÃÃes envolvidas no processo pode ser complicado ou mesmo impossÃvel. Buscou-se com esse trabalho desenvolver uma metodologia para ajuste de curvas de Weibull para a caracterizaÃÃo do regime de ventos, utilizando a otimizaÃÃo pelo mÃtodo do Formigueiro e pelo mÃtodo do Enxame de PartÃculas (do inglÃs Ant Colony Optimization (ACO) e Particle Swarm Optimization (PSO), respectivamente) bem como a hibridizaÃÃo destes dois mÃtodos. Acredita-se que a caracterÃstica estocÃstica dos mÃtodos pode proporcionar resultados refinados para qualquer tipo de distribuiÃÃo de vento, seja concentrada ou dispersa, o que tornaria vÃlido utilizar o mÃtodo nas condiÃÃes de litoral, relevo plano, acidentado ou mesmo urbano, com obstÃculos na direÃÃo dos aerogeradores. O resultado obtido pela aproximaÃÃo heurÃstica de duas amostras de vento do projeto SONDA, referentes a Petrolina-PE e SÃo Martinho da Serra-RS foi comparado com outros oito mÃtodos jà conhecidos e comercialmente aplicados: mÃtodo dos mÃnimos quadrados, mÃtodo do momento, mÃtodo empÃrico, mÃtodo da mÃxima verossimilhanÃa, mÃtodo da mÃxima semelhanÃa, mÃtodo da energia padrÃo, mÃtodo da energia equivalente e mÃtodo do chi-quadrado e a qualidade do ajuste serà avaliada pelos testes de RMSE, MAPE, R2 e pelo desvio na previsÃo de densidade de potÃncia. Os mÃtodos heurÃsticos se mostraram competitivos, com valores de erro em previsÃo de potÃncia da ordem de 10−14%.

Visuomenė, internetas ir jame esanti informacija dabar turi vieną didžiausių įtakų mūsų asmeniniame gyvenime. Be interneto daugelis dabar, net nebeįsivaizduotų savo kasdienybės, nes jis mums ir padeda atlikti daugelį darbų. Tačiau vis augant duomenų kiekiams internete, informacijos paieška tampa vienu svarbiausių internete naudojamų funkcijų.Paieškos sistemos – tai sistemos, ieškančios dokumentų žiniatinklyje, naujienų grupių archyvuose, FTP saugyklose, kuriuos rado paieškos serveris ir įtraukė į savo duomenų bazes. Ieškant informacijos, vartotojai naudojasi šiomis sistemomis, todėl internetinių svetainių kūrėjai yra suinteresuoti, kad jų internetiniai puslapiai būti kuo dažniau randami paieškos sistemų. Todėl besiplečiant paieškos sistemoms išsiplėtojo SEO (Search Engine Optimization, lietuviškai „Optimizavimas paieškos sistemoms“). / Expansion of the Internet and the information contained therein formed such an organization which aims to help the internet users to quickly and efficiently find what he needs. One of them is the Google search engine. It is one of the largest search engine companies in the world. In this paper we will analyse the Google search engine, and its proposed content optimization methods, which it claims to helps achieve better search results positions. The paper will discuss techniques such as HTML meta tags, content headers, site maps, robots.txt file, canonical and Paging functions. These methods will be compared with each other, and we will decide which method is most useful for site optimization.

Evolutionary algorithms (EAs) are population based heuristic optimization methods used to solve single and multi-objective optimization problems. They can simultaneously search multiple regions to find global optimum solutions. As EAs do not require gradient information for the search, they can be applied to optimization problems involving functions of real, integer, or discrete variables. One of the drawbacks of EAs is that they require evaluations of numerous candidate solutions for convergence. Most real life engineering design optimization problems involve highly nonlinear objective and constraint functions arising out of computationally expensive simulations. For such problems, the computation cost of optimization using EAs can become quite prohibitive. This has stimulated the research into improving the efficiency of EAs reported herein. In this thesis, two major improvements are suggested for EAs. The first improvement is the use of spatial surrogate models to replace the expensive simulations for the evaluation of candidate solutions, and other is a novel constraint handling technique. These modifications to EAs are tested on a number of numerical benchmarks and engineering examples using a fixed number of evaluations and the results are compared with basic EA. addition, the spatial surrogates are used in the truss design application. A generic framework for using spatial surrogate modeling, is proposed. Multiple types of surrogate models are used for better approximation performance and a prediction accuracy based validation is used to ensure that the approximations do not misguide the evolutionary search. Two EAs are proposed using spatial surrogate models for evaluation and evolution. For numerical benchmarks, the spatial surrogate assisted EAs obtain significantly better (even orders of magnitude better) results than EA and on an average 5-20% improvements in the objective value are observed for engineering examples. Most EAs use constraint handling schemes that prefer feasible solutions over infeasible solutions. In the proposed infeasibility driven evolutionary algorithm (IDEA), a few infeasible solutions are maintained in the population to augment the evolutionary search through the infeasible regions along with the feasible regions to accelerate convergence. The studies on single and multi-objective test problems demonstrate the faster convergence of IDEA over EA. In addition, the infeasible solutions in the population can be used for trade-off studies. Finally, discrete structures optimization (DSO) algorithm is proposed for sizing and topology optimization of trusses. In DSO, topology optimization and sizing optimization are separated to speed up the search for the optimum design. The optimum topology is identified using strain energy based material removal procedure. The topology optimization process correctly identifies the optimum topology for 2-D and 3-D trusses using less than 200 function evaluations. The sizing optimization is performed later to find the optimum cross-sectional areas of structural elements. In surrogate assisted DSO (SDSO), spatial surrogates are used to accelerate the sizing optimization. The truss designs obtained using SDSO are very close (within 7% of the weight) to the best reported in the literature using only a fraction of the function evaluations (less than 7%).

Diss. (sammanfattning) Linköping : Univ., 2005. / Härtill 5 uppsatser.

Lic.-avh. (sammanfattning) Umeå : Sveriges lantbruksuniv. / Härtill 2 uppsatser.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2002. / Härtill 5 uppsatser.