Made available in DSpace on 2015-03-04T18:51:14Z (GMT). No. of bitstreams: 1 dissertacao_eduardo_krempser.pdf: 675502 bytes, checksum: 9909466baacd7f6cebcd6029c53ed6c4 (MD5) Previous issue date: 2009-03-04 / Conselho Nacional de Desenvolvimento Cientifico e Tecnologico / Optimization is a large area of knowledge concerned with the need of a better use of resources and activities, becoming indispensable in the solution of several problems which arise from the study and formulation of real-world problems. Furthermore, the constraints that must be respected for each situation introduce in the methodologies of optimization an additional complication. Differential Evolution, which in its original formulation is applied only to unconstrained optimization problems in continuous space, also provides good results when applied to constrained optimization with discrete and continuous variables. This work presents the necessary improvements to Differential Evolution for its proper application to this class of problems, and proposes a new combination of techniques for this application, as well as a mechanism for dynamic selection of the appropriate variant of the technique. The initial proposal is a combination of Differential Evolution with a technique of adaptive penalty (APM) and the second proposal concerns the dynamic selection of variants during the search process. Several computational experiments are carried out confirming the competitiveness of the proposed algorithms. / A otimização é uma grande área de conhecimento voltada para a necessidade de um melhor aproveitamento de recursos e atividades, tornando-se indispensável na resolução de grande parte dos problemas oriundos de estudos e formulações de problemas reais. Além disso, as restrições que devem ser respeitadas para cada situação introduzem nas metodologias de otimização um complicador adicional. A Evolução Diferencial, que em sua formulação original é aplicada somente a problemas de otimização irrestrita e em espaços contínuos, apresenta também bons resultados quando aplicada à otimização restrita com variáveis contínuas e discretas. Este trabalho apresenta os aperfeiçoamentos necessários à Evolução Diferencial para sua adequada aplicação sobre essa classe de problemas, além de propor uma nova combinação de técnicas para essa aplicação, bem como um mecanismo de seleção dinâmica da variante adequada da técnica. A proposta inicial é a combinação da Evolução Diferencial com uma técnica adaptativa de penalização (APM) e a segunda proposta visa a seleção dinâmica de variantes durante o processo de busca. Vários experimentos computacionais são executados confirmando a competitividade dos algoritmos propostos.

Submitted by Maria Cristina (library@lncc.br) on 2016-08-02T17:09:01Z No. of bitstreams: 1 dissertacao_vinicius.pdf: 1188979 bytes, checksum: dfd0b027f7accd5d2614f8b2e5746393 (MD5) / Approved for entry into archive by Maria Cristina (library@lncc.br) on 2016-08-02T17:09:15Z (GMT) No. of bitstreams: 1 dissertacao_vinicius.pdf: 1188979 bytes, checksum: dfd0b027f7accd5d2614f8b2e5746393 (MD5) / Made available in DSpace on 2016-08-02T17:09:26Z (GMT). No. of bitstreams: 1 dissertacao_vinicius.pdf: 1188979 bytes, checksum: dfd0b027f7accd5d2614f8b2e5746393 (MD5) Previous issue date: 2016-03-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes) / Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) / The objective of structural optimization is to obtain economical structures which also satisfy specific performance requirements. It involves the search for optimal values for the design variables in order to minimize, or maximize, some measure of interest modeled via an objective function. Frequently, such problems involve constraints which must be satisfied so that a solution can be considered feasible. In addition, the objective function and/or constraints are often not available as explicit expressions of the design variables, and must be evaluated via a simulator. Hence, the choice of the optimization technique must take into account such features. Differential Evolution can be seen as an interesting choice, since it has been obtaining good results in problems in different fields of study. In this work, the results obtained by different variants of the original DE algorithm, when applied to problems of mass minimization of trusses subject to constraints on their natural vibration frequency, are analysed and compared to those reported in the literature. In view of the practical interest in limiting the number of different values found for the design variables in the final solution, it is also investigated the effect of this cardinality constraint (not addressed in the literature) on the structural mass obtained by the algorithm. / A otimização estrutural visa a obtenção de estruturas econômicas e que satisfaçam aos requisitos de desempenho aplicáveis ao caso. Ela envolve a busca de valores ótimos para as variáveis de projeto, que venham a minimizar, ou maximizar, algum critério de interesse modelado através de uma função objetivo. Frequentemente estes problemas apresentam restrições, as quais devem ser respeitadas para que uma solução seja considerada válida. Além disso, muitas vezes a função objetivo e/ou restrições não estão disponíveis como expressões explícitas das variáveis de projeto, sendo avaliadas através de um simulador. Assim, a escolha da técnica de otimização deve levar em conta tais características. A chamada Evolução Diferencial apresenta-se como uma escolha interessante, uma vez que vem obtendo bons resultados em problemas das mais diversas áreas. Neste trabalho os resultados obtidos por diferentes variantes do algoritmo original, quando aplicadas a problemas de minimização da massa de treliças sujeitas às restrições sobre as frequências naturais de vibração, são analisados e comparados àqueles reportados na literatura. Tendo em vista o interesse prático em limitar o número de diferentes valores encontrados para as variáveis de projeto na solução final, é então analisada a influência da introdução desta restrição de cardinalidade (ainda não reportada na literatura) sobre a massa da estrutura obtida pelo algoritmo ao final do processo.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / The infrastructure expansion planning in cellular networks, so called Base Station Placement (BSP) problem, is a challenging task that must consider a large set of aspects, and which cannot be expressed as a linear optimization function. The BSP is known to be a NP-hard problem unable to be solved by any deterministic method. Based on some fundamental assumptions of Long Term Evolution - Advanced (LTE-A) networks, this work proceeds to investigate the use of two methods for BSP optimization task: the Particle Swarm Optimization (PSO) and the Differential Evolution (DE), which were adapted for placement of many new network nodes simultaneously. The optimization process follows two multi-objective functions used as fitness criteria for measuring the performance of each node and of the network. The optimization process is performed in three scenarios where one of them presents actual data collected from a real city. For each scenario, the fitness performance of both methods as well as the optimized points found by each technique are presented. / O planejamento de expansÃo de infraestrutura em redes celulares à uma desafio que exige considerar diversos aspectos que nÃo podem ser separados em uma funÃÃo de otimizaÃÃo linear. Tal problema de posicionamento de estaÃÃes base à conhecido por ser do tipo NP-hard, que nÃo pode ser resolvido por qualquer mÃtodo determinÃstico. Assumindo caracterÃsticas bÃsicas da tecnologia Long Term Evolution (LTE)-Advanced (LTE-A), este trabalho procede à investigaÃÃo do uso de dois mÃtodos para otimizaÃÃo de posicionamento de estaÃÃes base: OtimizaÃÃo por Enxame de PartÃculas â Particle Swarm Optimization (PSO) â e EvoluÃÃo Diferencial â Differential Evolution (DE) â adaptados para posicionamento de mÃltiplas estaÃÃes base simultaneamente. O processo de otimizaÃÃo à orientado por dois tipos de funÃÃes custo com multiobjetivos, que medem o desempenho dos novos nÃs individualmente e de toda a rede coletivamente. A otimizaÃÃo à realizada em trÃs cenÃrios, dos quais um deles apresenta dados reais coletados de uma cidade. Para cada cenÃrio, sÃo exibidos o desempenho dos dois algoritmos em termos da melhoria na funÃÃo objetivo e os pontos encontrados no processo de otimizaÃÃo por cada uma das tÃcnicas

During the operation in a modern power distribution system, some abnormal events may happen, such as over-voltage, faults, under-frequency and overloading, and so on. These abnormal events may cause a power outage in a distribution system or damages on the equipment in a distribution system. Hence these abnormal events should be identified and isolated by protection systems as quickly as possible to make sure we can maintain a stable and reliable distribution system to supply adequate electric power to the largest number of consumers as we can. To sum up, we need stable and reliable protection systems to satisfy this requirement. Chapter 1 of the dissertation is a brief introduction to my research contents. Firstly, the background of a distribution system and the protection systems in a power system will be introduced in the first subchapter. Then there will be a review of existing methods of optimum coordination of overcurrent relays using different optimal techniques. The dissertation outline will be illustrated in the end. Chapter 2 of the dissertation describes a novel method of optimum online adaptive coordination of overcurrent relays using the genetic algorithm. In this chapter, the basic idea of the proposed methods will be explained in the first subchapter. It includes the genetic algorithm concepts and details about how it works as an optimal technique. Then three different types of simulation systems will be used in this part. The first one is a basic distribution system without distributed generations (DGs); the second one is similar to the first one but with load variations; the last simulation system is similar to the first one but with a distributed generation in it. Using three different simulation systems will demonstrate that the coordination of overcurrent relays is influenced by different operating conditions of the distribution system. In Chapter 3, a larger sized distribution system with more distributed generations and loads will be simulated and used for verifying the proposed method in a more realistic environment. In addition, the effects of fault location on the optimum coordination of overcurrent relays will be discussed here. In Chapter 4, the optimal differential evolution (DE) technique will be introduced. Because of the requirement of the online adaptive function, the optimal process needs to be accomplished as soon as possible. Through the comparison between genetic algorithm and differential evolution on the optimum coordination of overcurrent relays, we found that differential evolution is much faster than the genetic algorithm, especially when the size of the distribution system grows. Therefore, the differential evolution optimal technique is more suited than the genetic algorithm to realize online adaptive function. Chapter 5 presents the conclusion of the research work that has been done in this dissertation.

The objective of this study is to investigate the use and efficiency of Differential Evolution (DE) method on structural optimization. The solution algorithm developed with DE is computerized into software called SOP2011 using VB.NET. SOP2011 is automated to achieve size optimum design of steel structures consisting of 1-D elements such as trusses and frames subjected to design provisions according to ASD-AISC (2010) and LRFD-AISC (2010). SOP2011 works simultaneously with the structural analysis and design software SAP2000 in order to find the global or near optimum designs for real size truss and frame structures in which the optimization problem is classified as constrained, discrete size optimization. Software interacts with SAP2000 through the Open Application Programming Interface (OAPI), which provides an access to information of SAP2000 inputs and outputs. It is programmed for finding reasonable and optimized results for truss and frame steel structures by choosing appropriate ready sections for structural members considering the minimum weight via DE technique. Based on the comparison of the obtained results with the literature, DE algorithm with penalty function implementation is proved to be an efficient optimization technique amongst several major methods used for discrete constrained size optimization of real size steel structures. Also, it has been shown that by using optimized designs obtained by DE, weight of the structures can be reduced up to 67.9% for steel truss structures and 41.7% for steel frame structures compared to SAP2000 auto design procedure, hence resulting a significant saving of materials, cost, work hours and energy required for the project.

Orientador: Robson Pederiva / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-25T19:18:34Z (GMT). No. of bitstreams: 1 RubensGoncalvesSalsaJunior_M.pdf: 19298740 bytes, checksum: e9f9763c1d9d97af499156bb958078e0 (MD5) Previous issue date: 2014 / Resumo: O objetivo deste trabalho é apresentar uma forma computacional eficaz de manipular a curva que representa o perfil dos camos, objetivando a sua aplicação em simulações computacionais e rotinas de otimização de um trem de válvulas. Ao longo dos anos, os motores de combustão interna têm sido pesquisados e aprimorados, seja na busca de maior potência, seja na busca de menor consumo de combustível. Um subsistema automotivo que afeta diretamente o desempenho dos motores é o sistema de acionamento de válvulas, que permite controlar a entrada e saída dos gases da câmara de combustão. Diversos pesquisadores têm estudado a cinemática e dinâmica do sistema de acionamento de válvulas para melhorar o desempenho do motor, focando nas características construtivas do perfil dos camos: ele tem ação preponderante sobre a dinâmica do sistema. Neste trabalho foi aplicado o método de otimização da evolução diferencial de modo a otimizar a resposta dinâmica da válvula de exaustão de um motor Diesel, modelada por um sistema de cinco graus de liberdade, utilizando o perfil do camo como variável de projeto. Em um dos estudos de caso obteve-se redução de aproximadamente 60% nos picos de aceleração no fechamento da válvula. Em outro estudo de caso a área sob a curva de aceleração foi maximizada, aumentando aproximadamente 9%. Também Foi demonstrado um artifício matemático para que fossem considerados dois objetivos na otimização, já que os esforços para maximizar a área sob a curva de aceleração e minimizar a aceleração mostraram-se antagônicos. Por fim, mostrou-se que o perfil ótimo do camo varia com a rotação do motor / Abstract: The objective of this work is to present an efficient computational way to manipulate the curve representing the cam profile, aiming their application in computer simulations and optimization routines for a valvetrain. Over the years internal combustion engines have been researched and improved, be it in the search for more power or be it in the search for lower fuel consumption. An automotive subsystem that directly affects the performance of the engine is the valvetrain system. This system allows the control of the admittance and release of gases from the combustion chamber. Several researchers have studied the kinematics and dynamics of the valve actuation system to improve engine performance focusing in the design characteristics of the profile of the cams: it has a predominant action on the dynamics of the system. In this work the optimization method of differential evolution was applied to optimize a Diesel engine exhaust valve's dynamic response using the cam profile as a design variable. In one case of study the acceleration peak had a 60% reduction. In another case of study the area under the valve's displacement curve was increased by 9%. A mathematical scheme was demonstrated to consider tow objectives for the parameters acceleration and area showed to be ambivalent. In addition, it was also demonstrated that the optimal cam profile varies with the engine speed / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

This thesis deals with evolutionary and genetic algorithms and the possible ways of combining them. The theoretical part of the thesis describes genetic algorithms and neural networks. In addition, the possible combinations and existing algorithms are presented. The practical part of this thesis describes the implementation of the algorithm NEAT and the experiments performed. A combination with differential evolution is proposed and tested. Lastly, NEAT is compared to the algorithms backpropagation (for feed-forward neural networks) and backpropagation through time (for recurrent neural networks), which are used for learning neural networks. Comparison is aimed at learning speed, network response quality and their dependence on network size.

Modeling molecular structures is critical for understanding the principles that govern the behavior of molecules and for facilitating the exploration of potential pharmaceutical drugs and nanoscale designs. Biological molecules are flexible bodies that can adopt many different shapes (or conformations) until they reach a stable molecular state that is usually described by the minimum internal energy. A major challenge in modeling flexible molecules is the exponential explosion in computational complexity as the molecular size increases and many degrees of freedom are considered to represent the molecules' flexibility. This research work proposes a novel generic computational geometric approach called enhanced BioGeoFilter (g.eBGF) that geometrically interprets inter-atomic interactions to impose geometric constraints during molecular conformational search to reduce the time for identifying chemically-feasible conformations. Two new methods called Kinematics-Based Differential Evolution (kDE) and Biological Differential Evolution (BioDE) are also introduced to direct the molecular conformational search towards low energy (stable) conformations. The proposed kDE method kinematically describes a molecule's deformation mechanism while it uses differential evolution to minimize the inta-molecular energy. On the other hand, the proposed BioDE utilizes our developed g.eBGF data structure as a surrogate approximation model to reduce the number of exact evaluations and to speed the molecular conformational search. This research work will be extremely useful in enabling the modeling of flexible molecules and in facilitating the exploration of nanoscale designs through the virtual assembly of molecules. Our research work can also be used in areas such as molecular docking, protein folding, and nanoscale computer-aided design where rapid collision detection scheme for highly deformable objects is essential.

The paper describes the background and concepts behind a master’s thesis platform known as COMET (Constrained Optimization of Multiple-dimensions for Efficient Trajectories) created for mission designers to determine and evaluate suitable interplanetary trajectories. This includes an examination of the improvements to the global optimization algorithm, Differential Evolution, through a cascading search space pruning method and decomposition of optimization parameters. Results are compared to those produced by the European Space Agency’s Advanced Concept Team’s Multiple Gravity Assist Program. It was found that while discrepancies in the calculation of ΔV’s for flyby maneuvers exist between the two programs, COMET showed a noticeable improvement in its ability to avoid premature convergence and find highly isolated solutions.

No description available.