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Zhou, Yao. "Study on genetic algorithm improvement and application." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-211907/.
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Dingwall, Austin Gregory. "Testing the impact of using cumulative data with genetic algorithms for the analysis of building energy performance and material cost." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45952.
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The demand for energy and cost efficient buildings has made architects and contractors more aware of the resources consumed by the built environment. While the actual economic and environmental costs of future construction can never be completely predicted, energy simulations and cost modeling have become accepted ways to guide the design and construction process by comparing possible outcomes. These tools are now commonplace in the construction industry, and researchers are continuing to develop new and innovative strategies to optimize building design and construction. Previous research has proven that genetic algorithms are effective methods to evaluate and optimize building design in situations that contain a large number of possible solutions. The technique makes a computationally difficult multi-optimization process possible but is still a reactive and time consuming process that focuses on evaluation rather than solution generation. This research presented in this paper builds upon established multi-objective optimization techniques that use an energy simulator to estimate a conceptual building’s energy use as well as construction cost. The study compares simulations of a simplified model of a 3-story inpatient hospital located in Atlanta, Georgia using a defined set of variables. A combined global minimum of annual energy consumption and total construction is sought after using a method that utilizes a genetic algorithm. The second phase of this research uses a modified approach that combines the traditional genetic algorithm with a seeding method that utilizes previous results. A new set of simulations were established that duplicates the initial trials using a slightly modified set of design variables. The simulation was altered, and the phase one trials were utilized as the first generation of simulated solutions. The objective of this thesis is to explore one method of making energy use and cost estimating more accessible to the construction industry by combining simulation optimization and indexing. The results indicate that this study’s proposed augmented approach has potential benefits to building design optimization, although more research is required to validate this hypothesis in its entirety. This study concludes that the proposed approach can potentially reduce the time needed for individual optimization exercises by creating a cumulative, robust catalog of previous computations that will inform and seed future analyses. The research was conducted in five general stages. The first part defines the research problem and scope of research to be conducted. In the second part, the concepts of genetic algorithms and energy simulation are explored in a comprehensive literature review. The remaining parts explain the trial simulations performed in this study. Part three explains the experiment’s methodology, and part four describes the simulation results. The fifth and final part looks at what the possible conclusions that can be made from analyzing the study’s results.
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Khalid, Adeel S. "Development and Implementation of Rotorcraft Preliminary Design Methodology using Multidisciplinary Design Optimization." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14013.
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A formal framework is developed and implemented in this research for preliminary rotorcraft design using IPPD methodology. All the technical aspects of design are considered including the vehicle engineering, dynamic analysis, stability and control, aerodynamic performance, propulsion, transmission design, weight and balance, noise analysis and economic analysis. The design loop starts with a detailed analysis of requirements. A baseline is selected and upgrade targets are identified depending on the mission requirements. An Overall Evaluation Criterion (OEC) is developed that is used to measure the goodness of the design or to compare the design with competitors. The requirements analysis and baseline upgrade targets lead to the initial sizing and performance estimation of the new design. The digital information is then passed to disciplinary experts. This is where the detailed disciplinary analyses are performed. Information is transferred from one discipline to another as the design loop is iterated. To coordinate all the disciplines in the product development cycle, Multidisciplinary Design Optimization (MDO) techniques e.g. All At Once (AAO) and Collaborative Optimization (CO) are suggested. The methodology is implemented on a Light Turbine Training Helicopter (LTTH) design. Detailed disciplinary analyses are integrated through a common platform for efficient and centralized transfer of design information from one discipline to another in a collaborative manner. Several disciplinary and system level optimization problems are solved. After all the constraints of a multidisciplinary problem have been satisfied and an optimal design has been obtained, it is compared with the initial baseline, using the earlier developed OEC, to measure the level of improvement achieved. Finally a digital preliminary design is proposed. The proposed design methodology provides an automated design framework, facilitates parallel design by removing disciplinary interdependency, current and updated information is made available to all disciplines at all times of the design through a central collaborative repository, overall design time is reduced and an optimized design is achieved.
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Sheng, Lizeng. "Finite Element Analysis and Genetic Algorithm Optimization Design for the Actuator Placement on a Large Adaptive Structure." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/30184.
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The dissertation focuses on one of the major research needs in the area of adaptive /intelligent/smart structures, the development and application of finite element analysis and genetic algorithms for optimal design of large-scale adaptive structures. We first review some basic concepts in finite element method and genetic algorithms, along with the research on smart structures. Then we propose a solution methodology for solving a critical problem in the design of a next generation of large-scale adaptive structures -- optimal placements of a large number of actuators to control thermal deformations. After briefly reviewing the three most frequently used general approaches to derive a finite element formulation, the dissertation presents techniques associated with general shell finite element analysis using flat triangular laminated composite elements. The element used here has three nodes and eighteen degrees of freedom and is obtained by combining a triangular membrane element and a triangular plate bending element. The element includes the coupling effect between membrane deformation and bending deformation. The membrane element is derived from the linear strain triangular element using Cook's transformation. The discrete Kirchhoff triangular (DKT) element is used as the plate bending element. For completeness, a complete derivation of the DKT is presented. Geometrically nonlinear finite element formulation is derived for the analysis of adaptive structures under the combined thermal and electrical loads. Next, we solve the optimization problems of placing a large number of piezoelectric actuators to control thermal distortions in a large mirror in the presence of four different thermal loads. We then extend this to a multi-objective optimization problem of determining only one set of piezoelectric actuator locations that can be used to control the deformation in the same mirror under the action of any one of the four thermal loads. A series of genetic algorithms, GA Version 1, 2 and 3, were developed to find the optimal locations of piezoelectric actuators from the order of 1021 ~ 1056 candidate placements. Introducing a variable population approach, we improve the flexibility of selection operation in genetic algorithms. Incorporating mutation and hill climbing into micro-genetic algorithms, we are able to develop a more efficient genetic algorithm. Through extensive numerical experiments, we find that the design search space for the optimal placements of a large number of actuators is highly multi-modal and that the most distinct nature of genetic algorithms is their robustness. They give results that are random but with only a slight variability. The genetic algorithms can be used to get adequate solution using a limited number of evaluations. To get the highest quality solution, multiple runs including different random seed generators are necessary. The investigation time can be significantly reduced using a very coarse grain parallel computing. Overall, the methodology of using finite element analysis and genetic algorithm optimization provides a robust solution approach for the challenging problem of optimal placements of a large number of actuators in the design of next generation of adaptive structures.Ph. D.
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Gagliano, Joseph R. "Orbital Constellation Design and Analysis Using Spherical Trigonometry and Genetic Algorithms: A Mission Level Design Tool for Single Point Coverage on Any Planet." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1877.
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Recent interest surrounding large scale satellite constellations has increased analysis efforts to create the most efficient designs. Multiple studies have successfully optimized constellation patterns using equations of motion propagation methods and genetic algorithms to arrive at optimal solutions. However, these approaches are computationally expensive for large scale constellations, making them impractical for quick iterative design analysis. Therefore, a minimalist algorithm and efficient computational method could be used to improve solution times. This thesis will provide a tool for single target constellation optimization using spherical trigonometry propagation, and an evolutionary genetic algorithm based on a multi-objective optimization function. Each constellation will be evaluated on a normalized fitness scale to determine optimization. The performance objective functions are based on average coverage time, average revisits, and a minimized number of satellites. To adhere to a wider audience, this design tool was written using traditional Matlab, and does not require any additional toolboxes.
To create an efficient design tool, spherical trigonometry propagation will be utilized to evaluate constellations for both coverage time and revisits over a single target. This approach was chosen to avoid solving complex ordinary differential equations for each satellite over a long period of time. By converting the satellite and planetary target into vectors of latitude and longitude in a common celestial sphere (i.e. ECI), the angle can be calculated between each set of vectors in three-dimensional space. A comparison of angle against a maximum view angle, , controlled by the elevation angle of the target and the satellite’s altitude, will determine coverage time and number of revisits during a single orbital period.
Traditional constellations are defined by an altitude (a), inclination (I), and Walker Delta Pattern notation: T/P/F. Where T represents the number of satellites, P is the number of orbital planes, and F indirectly defines the number of adjacent planes with satellite offsets. Assuming circular orbits, these five parameters outline any possible constellation design. The optimization algorithm will use these parameters as evolutionary traits to iterate through the solutions space. This process will pass down the best traits from one generation to the next, slowly evolving and converging the population towards an optimal solution. Utilizing tournament style selection, multi-parent recombination, and mutation techniques, each generation of children will improve on the last by evaluating the three performance objectives listed. The evolutionary algorithm will iterate through 100 generations (G) with a population (n) of 100.
The results of this study explore optimal constellation designs for seven targets evenly spaced from 0° to 90° latitude on Earth, Mars and Jupiter. Each test case reports the top ten constellations found based on optimal fitness. Scatterplots of the constellation design solution space and the multi-objective fitness function breakdown are provided to showcase convergence of the evolutionary genetic algorithm. The results highlight the ratio between constellation altitude and planetary radius as the most influential aspects for achieving optimal constellations due to the increased field of view ratio achievable on smaller planetary bodies. The multi-objective fitness function however, influences constellation design the most because it is the main optimization driver. All future constellation optimization problems should critically determine the best multi-objective fitness function needed for a specific study or mission.
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Júnior, Paulo Roberto Caixeta. "Otimização multidisciplinar em projeto de asas flexíveis." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18135/tde-22122006-111540/.
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A indústria aeronáutica vem promovendo avanços tecnológicos em velocidades crescentes, para sobreviver em mercados extremamente competitivos. Neste cenário, torna-se imprescindível o uso de ferramentas de projeto que agilizem o desenvolvimento de novas aeronaves. Os atuais recursos computacionais permitiram um grande aumento no número de ferramentas que auxiliam o trabalho de projetistas e engenheiros. O projeto de uma aeronave é uma tarefa multidisciplinar por essência, o que logo incentivou o desenvolvimento de ferramentas computacionais que trabalhem com várias áreas ao mesmo tempo. Entre elas se destaca a otimização multidisciplinar em projeto, que une métodos de otimização à modelos matemáticos de áreas distintas de um projeto para encontrar soluções de compromisso. O presente trabalho introduz a otimização multidisciplinar em projeto (Multidisciplinary Design Optimization - MDO) e discorre sobre algumas aplicações possíveis desta metodologia. Foi realizada a implementação de um sistema de MDO para o projeto de asas flexíveis, considerando restrições de aeroelasticidade dinâmica e massa estrutural. Como meta, deseja-se encontrar distribuições ideais de rigidezes flexional e torcional da estrutura da asa, para maximizar a velocidade crítica de flutter e minimizar a massa estrutural. Para tanto, foram utilizados um modelo dinâmico-estrutural baseado no método dos elementos finitos, um modelo aerodinâmico não-estacionário baseado na teoria das faixas e nas soluções bidimensionais de Theodorsen, um modelo de previsão de flutter que utiliza o método K e, por fim, um otimizador baseado no método de algoritmos genéticos (AGs). São apresentados os detalhes empregados em cada modelo, as restrições aplicadas e a maneira como eles interagem ao longo da otimização. É feita uma análise para a escolha dos parâmetros de otimização por AG e em seguida a avaliação de dois casos, para verificação da funcionalidade do sistema implementado. Os resultados obtidos demonstram uma metodologia eficiente, que é capaz de buscar soluções ótimas para problemas propostos, que com devidos ajustes pode ter enorme valor para acelerar o desenvolvimento de novas aeronaves.The aeronautical industry is always trying to speed up technological advances in order to survive in extremely competitive markets. In this scenario, the use of design tools to accelerate the development of new aircraft becomes essential. Current computational resources allow greater increase in the number of design tools to assist the work of aeronautical engineers. In essence, the design of an aircraft is a multidisciplinary task, which stimulates the development of computational tools that work with different areas at the same time. Among them, the multidisciplinary design optimization (MDO) can be distinguished, which combines optimization methods to mathematical models of distinct areas of a design to find compromise solutions. The present work introduces MDO and discourses on some possible applications of this methodology. The implementation of a MDO system for the design of flexible wings, considering dynamic aeroelasticity restrictions and the structural mass, was carried out. As goal, it is desired to find ideal flexional and torsional stiffness distributions of the wing structure, that maximize the critical flutter speed and minimize the structural mass. To do so, it was employed a structural dynamics model based on the finite element method, a nonstationary aerodynamic model based on the strip theory and Theodorsens two-dimensional solutions, a flutter prediction model based on the K method and a genetic algorithm (GA). Details on the model, restrictions applied and the way the models interact to each other through the optimization are presented. It is made an analysis for choosing the GA optimization parameters and then, the evaluation of two cases to verify the functionality of the implemented system. The results obtained illustrate an efficient methodology, capable of searching optimal solutions for proposed problems, that with the right adjustments can be of great value to accelerate the development of new aircraft.
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Júnior, Paulo Roberto Caixeta. "Otimização multidisciplinar em projeto de asas flexíveis utilizando metamodelos." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/18/18148/tde-28092011-103532/.
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A Otimização Multidisciplinar em Projeto (em inglês, Multidisciplinary Design Optimization - MDO) é uma ferramenta de projeto importante e versátil e seu uso está se expandindo em diversos campos da engenharia. O foco desta metodologia é unir disciplinas envolvidas no projeto para que trabalhem suas variáveis concomitantemente em um ambiente de otimização, para obter soluções melhores. É possível utilizar MDO em qualquer fase do projeto, seja a fase conceitual, preliminar ou detalhada, desde que os modelos numéricos sejam ajustados às necessidades de cada uma delas. Este trabalho descreve o desenvolvimento de um código de MDO para o projeto conceitual de asas flexíveis de aeronaves, com restrição quanto ao fenômeno denominado flutter. Como uma ferramenta para o projetista na fase conceitual, os modelos numéricos devem ser razoavelmente precisos e rápidos. O intuito deste estudo é analisar o uso de metamodelos para a previsão do flutter de asas de aeronaves no código de MDO, ao invés de um modelo convencional, o que pode alterar significativamente o custo computacional da otimização. Para este fim são avaliados três técnicas diferentes de metamodelagem, que foram escolhidas por representarem duas classes básicas de metamodelos, a classe de métodos de interpolação e a de métodos de aproximação. Para representá-las foram escolhidos o método de interpolação por funções de base radial e o método de redes neurais artificiais, respectivamente. O terceiro método, que é considerado um método híbrido dos dois anteriores, é chamado de redes neurais por funções de bases radiais e é uma tentativa de acoplar as características de ambos em um único metamodelo. Os metamodelos são preparados utilizando um código para solução aeroelástica baseado no método dos elementos finitos acoplado com um modelo aerodinâmico linear de faixas. São apresentados resultados de desempenho dos três metamodelos, de onde se pode notar que a rede neural artificial é a mais adequada para previsão de flutter. O processo de MDO é realizado com o uso de um algoritmo genético multi-objetivo baseado em não-dominância, cujos objetivos são a maximização da velocidade crítica de flutter e a minimização da massa estrutural. Dois estudos de caso são apresentados para avaliar o desempenho do código de MDO, revelando que o processo global de otimização realiza de fato a busca pela fronteira de Pareto.The Multidisciplinary Design Optimization, MDO, is an important and versatile design tool and its use is spreading out in several fields of engineering. The focus of this methodology is to put together disciplines involved with the design to work all their variables concomitantly, at an optimization environment to obtain better solutions. It is possible to use MDO in any stage of the design process, that is in the conceptual, preliminary or detailed design, as long as the numerical models are fitted to the needs of each of these stages. This work describes the development of a MDO code for the conceptual design of flexible aircraft wings, with restrictions regarding the phenomenon called flutter. As a tool for the designer at the conceptual stage, the numerical models must be fairly accurate and fast. The aim of this study is to analyze the use of metamodels for the flutter prediction of aircraft wings in the MDO code, instead of a conventional model itself, what may affect significantly the computational cost of the optimization. For this purpose, three different metamodeling techniques have been evaluated, representing two basic metamodel classes, that are, the interpolation and the approximation class. These classes are represented by the radial basis function interpolation method and the artificial neural networks method, respectively. The third method, which is considered as a hybrid of the other two, is called radial basis function neural networks and is an attempt of coupling the features of both in single code. Metamodels are prepared using an aeroelastic code based on finite element model coupled with linear aerodynamics. Results of the three metamodels performance are presented, from where one can note that the artificial neural network is best suited for flutter prediction. The MDO process is achieved using a non-dominance based multi-objective genetic algorithm, whose objectives are the maximization of critical flutter speed and minimization of structural mass. Two case studies are presented to evaluate the performance of the MDO code, revealing that overall optimization process actually performs the search for the Pareto frontier.
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Hinds, Christopher Alan. "A PARETO-FRONTIER ANALYSIS OF PERFORMANCE TRENDS FOR SMALL REGIONAL COVERAGE LEO CONSTELLATION SYSTEMS." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1342.
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As satellites become smaller, cheaper, and quicker to manufacture, constellation systems will be an increasingly attractive means of meeting mission objectives. Optimizing satellite constellation geometries is therefore a topic of considerable interest. As constellation systems become more achievable, providing coverage to specific regions of the Earth will become more common place. Small countries or companies that are currently unable to afford large and expensive constellation systems will now, or in the near future, be able to afford their own constellation systems to meet their individual requirements for small coverage regions.
The focus of this thesis was to optimize constellation geometries for small coverage regions with the constellation design limited between 1-6 satellites in a Walker-delta configuration, at an altitude of 200-1500km, and to provide remote sensing coverage with a minimum ground elevation angle of 60 degrees. Few Pareto-frontiers have been developed and analyzed to show the tradeoffs among various performance metrics, especially for this type of constellation system. The performance metrics focus on geometric coverage and include revisit time, daily visibility time, constellation altitude, ground elevation angle, and the number of satellites. The objective space containing these performance metrics were characterized for 5 different regions at latitudes of 0, 22.5, 45, 67.5, and 90 degrees. In addition, the effect of minimum ground elevation angle was studied on the achievable performance of this type of constellation system. Finally, the traditional Walker-delta pattern constraint was relaxed to allow for asymmetrical designs. These designs were compared to see how the Walker-delta pattern performs compared to a more relaxed design space.
The goal of this thesis was to provide both a framework as well as obtain and analyze Pareto-frontiers for constellation performance relating to small regional coverage LEO constellation systems. This work provided an in-depth analysis of the trends in both the design and objective space of the obtained Pareto-frontiers. A variation on the εNSGA-II algorithm was utilized along with a MATLAB/STK interface to produce these Pareto-frontiers. The εNSGA-II algorithm is an evolutionary algorithm that was developed by Kalyanmoy Deb to solve complex multi-objective optimization problems.
The algorithm used in this study proved to be very efficient at obtaining various Pareto-frontiers. This study was also successful in characterizing the design and solution space surrounding small LEO remote sensing constellation systems providing small regional coverage.
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Abdalla, Alvaro Martins. "OMPP para projeto conceitual de aeronaves, baseado em heurísticas evolucionárias e de tomadas de decisões." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/18/18148/tde-13012011-113940/.
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Este trabalho consiste no desenvolvimento de uma metodologia de otimização multidisciplinar de projeto conceitual de aeronaves. O conceito de aeronave otimizada tem como base o estudo evolutivo de características das categorias imediatas àquela que se propõe. Como estudo de caso, foi otimizada uma aeronave de treinamento militar que faça a correta transição entre as fases de treinamento básico e avançado. Para o estabelecimento dos parâmetros conceituais esse trabalho integra técnicas de entropia estatística, desdobramento da função de qualidade (QFD), aritmética fuzzy e algoritmo genético (GA) à aplicação de otimização multidisciplinar ponderada de projeto (OMPP) como metodologia de projeto conceitual de aeronaves. Essa metodologia reduz o tempo e o custo de projeto quando comparada com as técnicas tradicionais existentes.This work is concerned with the development of a methodology for multidisciplinary optimization of the aircraft conceptual design. The aircraft conceptual design optimization was based on the evolutionary simulation of the aircraft characteristics outlined by a QFD/Fuzzy arithmetic approach where the candidates in the Pareto front are selected within categories close to the target proposed. As a test case a military trainer aircraft was designed target to perform the proper transition from basic to advanced training. The methodology for conceptual aircraft design optimization implemented in this work consisted on the integration of techniques such statistical entropy, quality function deployment (QFD), arithmetic fuzzy and genetic algorithm (GA) to the weighted multidisciplinary design optimization (WMDO). This methodology proved to be objective and well balanced when compared with traditional design techniques.
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Burger, Christoph Hartfield Roy J. "Propeller performance analys and multidisciplinary optimization using a genetic algorithm." Auburn, Ala, 2007. http://repo.lib.auburn.edu/2007%20Fall%20Dissertations/Burger_Christoph_57.pdf.
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Carlson, Susan Elizabeth. "Component selection optimization using genetic algorithms." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/17886.
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Whellens, Matthew W. "Multidisciplinary optimisation of aero-engines using genetic algorithms and preliminary design tools." Thesis, Cranfield University, 2003. http://dspace.lib.cranfield.ac.uk/handle/1826/10510.
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This study investigates a novel methodology for the preliminary design of aeroengines.
This involves the modelling of the disciplines that affect the engine's requirements
and constraints, their implementation in software format and their coupling into a single
unit. Subsequently, this unit is interfaced with an optimiser software. The resulting
multidisciplinary optimisation (MDO) tool allows the automation of the traditional,
human-based preliminary design process.
The investigation of the above-mentioned novel methodology is carried out
through the development of a "pilot" MDO tool and its subsequent utilisation in three case
studies, characterised by different optimisation scenarios. The selection of each case study
is motivated by current research questions, such as aviation's contribution to climate
change or the attractiveness of specific novel propulsion concepts.
The outcome of the pilot MDO study is considered successful and has been well
received by several academic and industrial aero-engine organisations. The choice of the
disciplines and of their modelling fidelity allowed a realistic representation of the main
disciplinary interactions and tradeoffs that characterise the important phase of preliminary
design. The computational effort involved in the solution of the optimisation studies was
found to be acceptable, and no major reprogramming was required when different
optimisation scenarios were considered. The case studies were investigated with an ease
and comprehensiveness that would not have been achievable through a human-based
parametric analysis.
The positive experience with the pilot MDO tool suggests that an automated
methodology for the preliminary design of aero-engines is feasible, applicable and valuable.
Its adoption can provide substantial advantages over the traditional human-based
approach, such as a reduction in human effort, costs and risk. From this perspective, the
pilot study constitutes a first step towards the development of a full-scale MDO tooL
usable by aero-engine manufacturers. In the near future, issues like climate change could
drive significant modifications in airframe and engine design. A preliminary design MDO
tool is therefore timely, and has the potential of making a significant contribution.
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Ganguly, Sandipan. "Algorithmic Modifications to a Multidisciplinary Design Optimization Model of Containerships." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/32483.
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When designing a ship, a designer often begins with â an ideaâ of what the ship might look like and what specifications the ship should meet. The multidisciplinary design optimization model is a tool that combines an analysis and an optimization process and uses a measure of merit to obtain what it infers to be the best design. All that the designer has to know is the range of values of certain design variables that confine the design within a lower and an upper bound. The designer then feeds the MDO model with any arbitrary design within the bounds and the model searches for the best design that minimizes or maximizes a measure of merit and also meets a set of structural and stability requirements.
The model is multidisciplinary because the analysis process, which calculates the measure of merit and other performance parameters, can be a combination of sub-processes used in various fields of engineering. The optimization process can also be a variety of mathematical programming techniques depending on the type of the design problem. The container ship design problem is a combination of discreet and continuous sub-problems. But to avail the advantages of gradient-based optimization algorithms, the design problem is molded into a fully continuous problem.
The efficiency and effectiveness with which an optimization process achieves the best design depends on how well the design problem is posed for the optimizer and how well that particular optimization algorithm tackles the type of design problems posed before it. This led the author to investigate the details of the analysis and the optimization process within the MDO model and make modifications to each of the processes, so that the two become more compatible towards achieving a better final design. Modifications made within the optimization algorithm were then used to develop a generalized modification method that can be used to improve any gradient-based optimization algorithm.Master of Science
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Unalmis, Dilek. "Design Optimization Of Truss Structures Using Genetic Algorithms." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614932/index.pdf.
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Design optimization of truss structures is a popular topic in aerospace,
mechanical, civil, and structural engineering due to benefits to industry.
Common design problem for the structures is the weight minimization.
Especially in aerospace engineering the minimization of the weight of the total
structure gets the highest importance in the design.
This study focuses on the design optimization of 2D and 3D truss structures.
The objective function is the total mass of the structure which is subjected to
stress and nodal displacement constraints. To optimize the design, Genetic
Algorithm (GA) is preferred due to its efficiency in dealing with problems with
discrete design variables as in the case of truss structures. This technique
yields more realistic results than linear programming methods.
In the thesis, a finite element code is developed for the analysis of planar and
space truss structures. The developed finite element solver is coupled with a
genetic algorithm optimization code which is also developed as a part of the
thesis study. Different truss optimization case studies are performed to
demonstrate the performance of the finite element solver and the genetic
algorithm optimization code that are developed. It is shown that with the use
of adaptive penalty function employing scaled fitnesses, the arbitrariness
issue of the factor multiplying the error term in the augmented fitness
function can be resolved. It is also shown that significant weight reduction can
v
be achieved by employing shape optimization together with size optimization
compared to pure size optimization.
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Soremekun, Grant A. E. "Genetic Algorithms for Composite Laminate Design and Optimization." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36699.
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Genetic algorithms are well known for being expensive optimization tools, especially if the cost for the analysis of each individual design is high. In the past few years, significant effort has been put forth in addressing the high computational cost GAs. The research conducted in the first part of this thesis continues this effort by implementing new multiple elitist and variable elitist selection schemes for the creation of successive populations in the genetic search process. The new selection schemes allow the GA to take advantage of a greater amount of important genetic information that may be contained in the parent designs, information that is not utilized when using a traditional elitist method selection scheme. By varying the amount of information that may be passed to successive generations from the parent population, the explorative and exploitative characteristics of the GA can be adjusted throughout the genetic search also. The new schemes provided slight reductions in the computational cost of the GA and produced many designs with good fitness' in the final population, while maintaining a high level of reliability. Genetic algorithms can be easily adapted to many different optimization problems also. This capability is demonstrated by modifying the basic GA, which utilizes a single chromosome string, to include a second string so that composite laminates comprised of multiple materials can be studied with greater efficiently. By using two strings, only minor adjustments to the basic GA were required. The modified GA was used to simultaneously minimize the cost and weight of a simply supported composite plate under different combinations of axial loading. Two materials were used, with one significantly stronger, but more expensive than the other. The optimization formulation was implemented by using convex combinations of cost and weight objective functions into a single value for laminate fitness, and thus required no additional modifications to the GA. To obtain a Pareto-optimal set of designs, the influence of cost and weight on the overall fitness of a laminate configuration was adjusted from one extreme to the other by adjusting the scale factors accordingly. The modified GA provided a simple yet reliable means of designing high performance composite laminates at costs lower than laminates comprised of one material.Master of Science
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Yucel, Osman. "Ballistic Design Optimization Of Three-dimensional Grains Using Genetic Algorithms." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614857/index.pdf.
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Within the scope of this thesis study, an optimization tool for the ballistic design of three-dimensional grains in solid propellant rocket motors is developed. The modeling of grain geometry and burnback analysis is performed analytically by using basic geometries like cylinder, cone, sphere, ellipsoid, prism and torus. For the internal ballistic analysis, a quasi-steady zero-dimensional flow solver is used. Genetic algorithms have been studied and implemented to the design process as an optimization algorithm. Lastly, the developed optimization tool is validated with the predesigned rocket motors.
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MESSER, BRUNO. "MULTILATERAL WELLS DESIGN IN OIL RESERVOIR THROUGH GENETIC ALGORITHMS OPTIMIZATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14734@1.
Full textAbstract:
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORUm dos fatores mais importantes para recuperação de óleo de reservatórios petrolíferos é a configuração dos poços. Atualmente, na indústria, esse processo é feito de forma manual onde um especialista gera algumas poucas opções de configurações e utiliza a de melhor resultado. Este trabalho se propõe a investigar um sistema de apoio à decisão para otimizar a configuração dos poços utilizando Algoritmos Genéticos e o simulador de reservatórios IMEX. Os parâmetros otimizados são: o número de poços produtores e injetores, a posição, a inclinação, a direção e o comprimento de cada poço, o número de laterais de cada poço e o ponto da junta, a inclinação relativa ao poço, a direção e o comprimento de cada lateral. Na busca pela configuração ótima dos poços, o objetivo da otimização é minimizar o investimento inicial, minimizar a produção de água e maximizar a produção de óleo buscando maximizar o VPL do empreendimento. A otimização é conduzida respeitando as restrições de projeto, dadas por um engenheiro, e restrições de simulação, dadas pelo próprio modelo de reservatório. O modelo proposto foi avaliado utilizando-se sete reservatórios. Cinco destes são sintéticos cujas configurações ótimas são conhecidas, um semi-sintético e um reservatório real. Foram conduzidos testes de convergência onde o modelo se mostrou capaz de localizar e otimizar as zonas produtoras, chegando à alternativa ótima até 80% das vezes. Nos últimos dois reservatórios os resultados indicam que o sistema consegue encontrar configurações de poços com altos valores de VPL, superiores a soluções propostas por especialistas e por outros sistemas de otimização, com ganhos de VPL de até 37% sobre a alternativa proposta por um especialista para o reservatório real.
One of the most important factors for recovering oil from oil reservoirs is the wells configuration. Now a days, on the industry, this process is conduced manually, where a specialist generates a few configuration options and uses the best one with best results. This work proposes to investigate a decision support system to optimize the wells’ configuration using Genetic Algorithms and the reservoir simulator IMEX. The optimized parameters include: the number of producers and injectors wells, the position, the inclination, the direction and the length of each well, the number of laterals for each well and the junction point, the inclination relative to the well and the length of each lateral. On the search of the optimal configuration of wells, the objective of the optimization is to minimize the initial investment, minimize the water production and maximize the oil production towards the maximization of the venture`s NPV. The optimization is conduced respecting the project`s restrictions, stated by an engineer, and the simulation`s restrictions, imposed by the reservoir model. The optimization model proposed was evaluated using seven reservoirs. Five of them are synthetic which the optimum well`s configuration are known, one semi-synthetic and one real reservoir. Convergence tests were conducted where the model confirmed to be able to locate and optimize the production zones, achieving the optimum alternative 80% of the times. On the last two reservoirs the results indicate that the system was able to achieve well configurations with high values of NPV, superiors from solutions given by specialists and by other optimization systems, with NPV´s increase reaching 37% over the specialist`s purposed alternative for the real reservoir case.
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Linden, Derek S. (Derek Scott). "Automated design and optimization of wire antennas using genetic algorithms." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10207.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 136-137).
by Derek S. Linden.
Ph.D.
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Sevinc, Ender. "Genetic Algorithms For Distributed Database Design And Distributed Database Query Optimization." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611194/index.pdf.
Full textAbstract:
The increasing performance of computers, reduced prices and ability to connect systems with low cost gigabit ethernet LAN and ATM WAN networks make distributed database systems an attractive research area. However, the complexity of
distributed database query optimization is still a limiting factor. Optimal techniques, such as dynamic programming, used in centralized database query optimization are not feasible because of the increased problem size. The recently developed genetic algorithm (GA) based optimization techniques presents a promising alternative. We compared the best known GA with a random algorithm and showed that it achieves almost no improvement over the random search algorithm generating an equal number of random solutions. Then, we analyzed a set of possible GA parameters and determined that two-point truncate technique using GA gives the best results.
New mutation and crossover operators defined in our GA are experimentally analyzed within a synthetic distributed database having increasing the numbers of relations and nodes. The designed synthetic database replicated relations, but there was no horizontal/vertical fragmentation. We can translate a select-project-join query including a fragmented relation with N fragments into a corresponding query with N relations. Comparisons with optimal results found by exhaustive search are only 20% off the results produced by our new GA formulation showing a 50% improvement over the previously known GA based algorithm.
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Dyer, John David Hartfield Roy J. "Aerospace design optimization using a real coded genetic algorithm." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Aerospace_Engineering/Thesis/Dyer_John_31.pdf.
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Abdelkhalik, Osama Mohamed Omar. "Orbit design and estimation for surveillance missions using genetic algorithms." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3126.
Full textAbstract:
The problem of observing a given set of Earth target sites within an assigned time
frame is examined. Attention is given mainly to visiting these sites as sub-satellite
nadir points. Solutions to this problem in the literature require thrusters to continuously
maneuver the satellite from one site to another. A natural solution is proposed.
A natural solution is a gravitational orbit that enables the spacecraft to satisfy the
mission requirements without maneuvering. Optimization of a penalty function is
performed to find natural solutions for satellite orbit configurations. This penalty
function depends on the mission objectives. Two mission objectives are considered:
maximum observation time and maximum resolution. The penalty function poses
multi minima and a genetic algorithm technique is used to solve this problem. In
the case that there is no one orbit satisfying the mission requirements, a multi-orbit
solution is proposed. In a multi-orbit solution, the set of target sites is split into
two groups. Then the developed algorithm is used to search for a natural solution
for each group. The satellite has to be maneuvered between the two solution orbits.
Genetic algorithms are used to find the optimal orbit transfer between the two orbits
using impulsive thrusters. A new formulation for solving the orbit maneuver problem
using genetic algorithms is developed. The developed formulation searches for a mini mum fuel consumption maneuver and guarantees that the satellite will be transferred
exactly to the final orbit even if the solution is non-optimal. The results obtained
demonstrate the feasibility of finding natural solutions for many case studies.
The problem of the design of suitable satellite constellation for Earth observing
applications is addressed. Two cases are considered. The first is the remote sensing
missions for a particular region with high frequency and small swath width. The second
is the interferometry radar Earth observation missions. In satellite constellations
orbit's design, a new set of compatible orbits, called the "Two-way orbits",whose
ground track path is a closed-loop trajectory that intersects itself, in some points,
with tangent intersections is introduced. Conditions are derived on the orbital elements
such that these Two-way Orbits exist and satellites flying in these orbits pass
the tangent intersection points at the same time. Finally, the recently proposed concept
of observing a space object from onboard a spacecraft using a star tracker is
considered. The measurements of the star tracker provide directions to the target in
space and do not provide range measurements. Estimation for the orbit of the target
space object using the measurements of the star tracker is developed. An observability
analysis is performed to derive conditions on the observability of the system states.
The Gaussian Least Squares Differential Correction Technique is implemented. The
results obtained demonstrate the feasibility of using the measurements of the star
tracker to get a good estimate for the target orbit within a period of measurements
ranging from about 20 percent to 50 percent of the orbital period depending on the
two orbits.
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Bhadauria, Ravi. "DESIGN AND OPTIMIZATION OF PERISTALTIC MICROPUMPS USING EVOLUTIONARY ALGORITHMS." VCU Scholars Compass, 2009. http://scholarscompass.vcu.edu/etd/1944.
Full textAbstract:
A design optimization based on coupled solid–fluid analysis is investigated in this work to achieve specific flow rate through a peristaltic micropump. A micropump consisting of four pneumatically actuated nozzle/diffuser shaped moving actuators on the sidewalls is considered for numerical study. These actuators are used to create pressure difference in the four pump chambers, which in turn drives the fluid through the pump in one direction. Genetic algorithms along with artificial neural networks are used for optimizing the pump geometry and the actuation frequency. A simple example with moving walls is considered for validation by developing an exact analytical solution of Navier–Stokes equation and comparing it with numerical simulations. Possible applications of these pumps are in microelectronics cooling and drug delivery. Based on the results obtained from the fluid–structure interaction analysis, three optimized geometries result in flow rates which match the predicted flow rates with 95% accuracy. These geometries need further investigation for fabrication and manufacturing issues.
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Oksuz, Ozhan. "Multiploid Genetic Algorithms For Multi-objective Turbine Blade Aerodynamic Optimization." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609196/index.pdf.
Full textAbstract:
To decrease the computational cost of genetic algorithm optimizations,
surrogate models are used during optimization. Online update of surrogate
models and repeated exchange of surrogate models with exact model during
genetic optimization converts static optimization problems to dynamic ones.
However, genetic algorithms fail to converge to the global optimum in
dynamic optimization problems. To address these problems, a multiploid
genetic algorithm optimization method is proposed. Multi-fidelity surrogate
models are assigned to corresponding levels of fitness values to sustain the
static optimization problem. Low fidelity fitness values are used to decrease
the computational cost. The exact/highest-fidelity model fitness value is used for converging to the global optimum. The algorithm is applied to
single and multi-objective turbine blade aerodynamic optimization
problems. The design objectives are selected as maximizing the adiabatic
efficiency and torque so as to reduce the weight, size and the cost of the gas
turbine engine. A 3-D steady Reynolds-Averaged Navier-Stokes solver is
coupled with an automated unstructured grid generation tool. The solver is
validated by using two well known test cases. Blade geometry is modelled
by 37 design variables. Fine and coarse grid solutions are respected as high
and low fidelity surrogate models, respectively. One of the test cases is
selected as the baseline and is modified in the design process. The effects of
input parameters on the performance of the multiploid genetic algorithm are
studied. It is demonstrated that the proposed algorithm accelerates the
optimization cycle while providing convergence to the global optimum for
single and multi-objective problems.
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Garcia, Sandrine. "Experimental Design Optimization and Thermophysical Parameter Estimation of Composite Materials Using Genetic Algorithms." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/28076.
Full textAbstract:
Thermophysical characterization of anisotropic composite materials is extremely important in the control of today fabrication processes and in the prediction of structure failure due to thermal stresses. Accuracy in the estimation of the thermal properties can be improved if the experiments are designed carefully. However, on one hand, the typically used parametric study for the design optimization is tedious and time intensive. On the other hand, commonly used gradient-based estimation methods show instabilities resulting in nonconvergence when used with models that contain correlated or nearly correlated parameters.
The objectives of this research were to develop systematic and reliable methodologies for both Experimental Design Optimization (EDO) used for the determination of thermal properties, and Simultaneous Parameter Estimation (SPE). Because of their advantageous features, Genetic Algorithms (GAs) were investigated for use as a strategy for both EDO and SPE. The EDO and SPE approaches used involved the maximization of an optimality criterion associated with the sensitivity matrix of the unknown parameters, and the minimization of the ordinary least squares error, respectively. Two versions of a general-purpose genetic-based program were developed: one is designed for the analysis of any EDO / SPE problems for which a mathematical model can be provided, while the other incorporates a control-volume finite difference scheme allowing for the practical analysis of complex problems. The former version was used to illustrate the genetic performance on the optimization of a difficult mathematical test function.
Two test cases previously solved in the literature were first analyzed to demonstrate and assess the GA-based {EDO/SPE} methodology. These problems included the optimization of one and two dimensional designs for the estimation at ambient temperature of two and three thermal properties, respectively (effective thermal conductivity parallel and perpendicular to the fibers plane and effective volumetric heat capacity), of anisotropic carbon/epoxy composite materials. The two dimensional case was further investigated to evaluate the effects of the optimality criterion used for the experimental design on the accuracy of the estimated properties.
The general-purpose GA-based program was then successively applied to three advanced studies involving the thermal characterization of carbon/epoxy anisotropic composites. These studies included the SPE of successively three, seven and nine thermophysical parameters, with for the latter case, a two dimensional EDO with seven experimental key parameters. In two of the three studies, the parameters were defined to represent the dependence of the thermal properties with temperature. Finally, the kinetic characterization of the curing of three thermosetting materials (an epoxy, a polyester and a rubber compound) was accomplished resulting in the SPE of six kinetic parameters.
Overall, the GA method was found to perform extremely well despite the high degree of correlation and low sensitivity of many parameters in all cases studied. This work therefore validates the use of GAs for the thermophysical characterization of anisotropic composite materials. The significance in using such algorithms is not only the solution to ill-conditioned problems but also, a drastically cost savings in both experimental and time expenses as they allow for the EDO and SPE of several parameters at once.Ph. D.
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Steele, Steven Cory Wyatt. "Optimal Engine Selection and Trajectory Optimization using Genetic Algorithms for Conceptual Design Optimization of Resuable Launch Vehicles." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/51771.
Full textAbstract:
Proper engine selection for Reusable Launch Vehicles (RLVs) is a key factor in the design of low cost reusable launch systems for routine access to space. RLVs typically use combinations of different types of engines used in sequence over the duration of the flight. Also, in order to properly choose which engines are best for an RLV design concept and mission, the optimal trajectory that maximizes or minimizes the mission objective must be found for that engine configuration. Typically this is done by the designer iteratively choosing engine combinations based on his/her judgment and running each individual combination through a full trajectory optimization to find out how well the engine configuration performed on board the desired RLV design.
This thesis presents a new method to reliably predict the optimal engine configuration and optimal trajectory for a fixed design of a conceptual RLV in an automated manner. This method is accomplished using the original code Steele-Flight. This code uses a combination of a Genetic Algorithm (GA) and a Non-Linear Programming (NLP) based trajectory optimizer known as GPOPS II to simultaneously find the optimal engine configuration from a user provided selection pool of engine models and the matching optimal trajectory. This method allows the user to explore a broad range of possible engine configurations that they wouldn't have time to consider and do so in less time than if they attempted to manually select and analyze each possible engine combination.
This method was validated in two separate ways. The codes ability to optimize trajectories was compared to the German trajectory optimizer suite known as ASTOS where only minimal differences in the output trajectory were noticed. Afterwards another test was performed to verify the method used by Steele-Flight for engine selection. In this test, Steele-Flight was provided a vehicle model based on the German Saenger TSTO RLV concept and models of turbofans, turbojets, ramjets, scramjets and rockets. Steele-Flight explored the design space through the use of a Genetic Algorithm to find the optimal engine combination to maximize payload. The results output by Steele-Flight were verified by a study in which the designer manually chose the engine combinations one at a time, running each through the trajectory optimization routine to determine the best engine combination. For the most part, these methods yielded the same optimal engine configurations with only minor variation.
The code itself provides RLV researchers with a new tool to perform conceptual level engine selection from a gathering of user provided conceptual engine data models and RLV structural designs and trajectory optimization for fixed RLV designs and fixed mission requirement.Master of Science
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Buonanno, Michael Alexander. "A Method for Aircraft Concept Exploration using Multicriteria Interactive Genetic Algorithms." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7571.
Full textAbstract:
The problem of aircraft concept selection has become increasingly difficult in recent years due to changes in the primary evaluation criteria of concepts. In the past, performance was often the primary discriminator whereas modern programs have placed increased emphasis on factors such as environmental impact, economics, supportability, aesthetics, and other metrics. The revolutionary nature of the vehicles required to simultaneously meet these conflicting requirements has prompted a shift from design using historical data regression techniques for metric prediction to the use of sophisticated physics-based analysis tools that are capable of analyzing designs outside of the historical database. The use of optimization methods with these physics-based tools, however, has proven difficult because of the tendency of optimizers to exploit assumptions present in the models and drive the design towards a solution which, while promising to the computer, may be infeasible due to factors not considered by the computer codes. In addition to this difficulty, the number of discrete options available at this stage may be unmanageable due to the combinatorial nature of the concept selection problem, leading the analyst to select a sub-optimum baseline vehicle. Some extremely important concept decisions, such as the type of control surface arrangement to use, are frequently made without sufficient understanding of their impact on the important system metrics due to a lack of historical guidance, computational resources, or analysis tools.
This thesis discusses the difficulties associated with revolutionary system design, and introduces several new techniques designed to remedy them. First, an interactive design method has been developed that allows the designer to provide feedback to a numerical optimization algorithm during runtime, thereby preventing the optimizer from exploiting weaknesses in the analytical model. This method can be used to account for subjective criteria, or as a crude measure of un-modeled quantitative criteria. Other contributions of the work include a modified Structured Genetic Algorithm that enables the efficient search of large combinatorial design hierarchies and an improved multi-objective optimization procedure that can effectively optimize several objectives simultaneously. A new conceptual design method has been created by drawing upon each of these new capabilities and aspects of more traditional design methods.
The ability of this new technique to assist in the design of revolutionary vehicles has been demonstrated using a problem of contemporary interest: the concept exploration of a supersonic business jet. This problem was found to be a good demonstration case because of its novelty and unique requirements, and the results of this proof of concept exercise indicate that the new method is effective at providing additional insight into the relationship between a vehicle's requirements and its favorable attributes.
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Tiene, Sara. "Genetic algorithms for construction management: the case study of a building envelope design optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
Find full textAbstract:
The present work deals with the study and analysis of the simultaneous optimization of time, cost and quality by using artificial intelligence technique; The approach is based on the use of genetic algorithms implemented in Matlab applied to a case study, the construction of a new University Campus in Cesena, focusing on the external walls of the building itself. The objective is to find a set of optimal solutions, equally valid, for the realization of stratigraphies of the different types of external masonry; It will then be the task of the designer to choose among possible solutions which he believes to be most appropriate, based on the requirements of the project, which may be in terms of quality, cost, time or a combination of two or more of these evaluation parameters. It will thus illustrate how different solutions provided by the program can be used and collected in a three-dimensional graph.
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Younes, Abdunnaser. "Adapting Evolutionary Approaches for Optimization in Dynamic Environments." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2835.
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Many important applications in the real world that can be modelled as combinatorial optimization problems are actually dynamic in nature. However, research on dynamic optimization focuses on continuous optimization problems, and rarely targets combinatorial problems. Moreover, dynamic combinatorial problems, when addressed, are typically tackled within an application context. In this thesis, dynamic combinatorial problems are addressed collectively by adopting an evolutionary based algorithmic approach. On the plus side, their ability to manipulate several solutions at a time, their robustness and their potential for adaptability make evolutionary algorithms a good choice for solving dynamic problems. However, their tendency to converge prematurely, the difficulty in fine-tuning their search and their lack of diversity in tracking optima that shift in dynamic environments are drawbacks in this regard.
Developing general methodologies to tackle these conflicting issues constitutes the main theme of this thesis. First, definitions and measures of algorithm performance are reviewed. Second, methods of benchmark generation are developed under a generalized framework. Finally, methods to improve the ability of evolutionary algorithms to efficiently track optima shifting due to environmental changes are investigated. These methods include adapting genetic parameters to population diversity and environmental changes, the use of multi-populations as an additional means to control diversity, and the incorporation of local search heuristics to fine-tune the search process efficiently.
The methodologies developed for algorithm enhancement and benchmark generation are used to build and test evolutionary models for dynamic versions of the travelling salesman problem and the flexible manufacturing system. Results of experimentation demonstrate that the methods are effective on both problems and hence have a great potential for other dynamic combinatorial problems as well.
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Kediyal, Prashant C. "Comparison of possible optimization methods for design of optical filters /." Electronic thesis, 2004. http://etd.wfu.edu/theses/available/etd-05022004-113931/.
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Moreschi, Luis M. "Seismic design of energy dissipation systems for optimal structural perfromance." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/28279.
Full textAbstract:
The usefulness of supplementary energy dissipation devices is now quite well-known in the earthquake structural engineering community for reducing the earthquake-induced response of structural systems. However, systematic design procedures for optimal sizing and placement of these protective systems in structural systems are needed and are not yet available. The main objective of this study is, therefore, to formulate a general framework for the optimal design of passive energy dissipation systems for seismic structural applications. The following four types passive energy dissipation systems have been examined in the study: (1) viscous fluid dampers, (2) viscoelastic dampers, (3) yielding metallic dampers and, (4) friction dampers. For each type of energy dissipation system, the study presents the (a) formulation of the optimal design problem, (b) consideration of several meaningful performance indices, (c) analytical and numerical procedures for seismic response and performance indices calculations, (d) procedures for obtaining the optimal design by an appropriate optimization scheme and, (e) numerical results demonstrating the effectiveness of the procedures and the optimization-based design approach.
For building structures incorporating linear damping devices, such as fluid and solid viscoelastic dampers, the seismic response and performance evaluations are done by a random vibration approach for a stochastic characterization of the earthquake induced ground motion. Both the gradient projection technique and genetic algorithm approach can be conveniently employed to determine the required amount of damping material and its optimal distribution within a building structure to achieve a desired performance criterion. An approach to evaluate the sensitivity of the optimum solution and the performance function with respect to the problem parameters is also described. Several sets of numerical results for different structural configurations and for different performance indices are presented to demonstrate the effectiveness and applicability of the approach.
For buildings installed with nonlinear hysteretic devices, such as yielding metallic elements or friction dampers, the computation of the seismic structural response and performance must be performed by time history analysis. For such energy dissipation devices, the genetic algorithm is more convenient to solve the optimal design problem. It avoids the convergence to a local optimal solution. To formulate the optimization problem within the framework of the genetic algorithm, the study presents the discretization procedures for various parameters of these nonlinear energy dissipation devices. To include the uncertainty about the seismic input motion in the search for optimal design, an ensemble of artificially generated earthquake excitations are considered. The similarities of the optimal design procedure with yielding metallic devices and friction devices are clearly established. Numerical results are presented to illustrate the applicability of the proposed optimization-based approach for different forms of performance indices and types of building structures.Ph. D.
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Dale, Brian M. "Optimal Design of MR Image Acquisition Techniques." Case Western Reserve University School of Graduate Studies / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=case1081556784.
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Fernando, Pradeep Ruben. "Genetic Algorithm Based Design and Optimization of VLSI ASICs and Reconfigurable Hardware." Scholar Commons, 2008. https://scholarcommons.usf.edu/etd/1963.
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Rapid advances in integration technology have tremendously increased the design complexity of very large scale integrated (VLSI) circuits, necessitating robust optimization techniques in many stages of VLSI design. A genetic algorithm (GA) is a stochastic optimization technique that uses principles derived from the evolutionary process in nature. In this work, genetic algorithms are used to alleviate the hardware design process of VLSI application specific integrated circuits (ASICs) and reconfigurable hardware.
VLSI ASIC design suffers from high design complexity and a large number of optimization objectives requiring hierarchical design approaches and multi-objective optimization techniques. The floorplanning stage of the design cycle becomes highly important in hierarchical design methods. In this work, a multi-objective genetic algorithm based floorplanner has been developed with novel crossover operators to address the multi-objective floorplanning problem for VLSI ASICs. The genetic floorplanner achieves significant wirelength savings (>19% on average) with little or no increase in area ( < 3% penalty) over previous floorplanners that perform simultaneous area and wirelength minimization.
Hardware implementation of genetic algorithms is gaining importance because of their proven effectiveness as optimization engines for real-time applications. Earlier hardware implementations suffer from major drawbacks such as absence of GA parameter programmability, rigid pre-defined system architecture, and lack of support for multiple fitness functions. A compact IP core that implements a general purpose GA engine has been designed to realize evolvable hardware in field programmable gate array devices. The designed GA core achieved a speedup of around 5.16x over an analogous software implementation.
Novel reconfigurable analog architectures have been proposed to realize extreme environment analog electronics. In this work, a digital framework has been developed to realize self reconfigurable analog arrays (SRAA) where genetic algorithms are used to evolve the required analog functionality and compensate performance degradation in extreme environments. The framework supports two methods of compensation, namely, model based lookup and genetic algorithm based compensation and is scalable in terms of the number of fitness evaluation modules. The entire framework has been implemented as a digital ASIC in a leading industrystrength silicon-on-insulator (SOI) technology to obtain high performance and a small form factor.
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Yildiz, Burhan. "Optimum Design Of Slurry Pipelines." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12611321/index.pdf.
Full textAbstract:
There exist various applications of transportation of slurries through pipelines all over the world. In the present study, the problem is formulated as a "transportation problem"
to determine the pipe diameters and amounts of slurry to be transported from the demand (production) points to the processing (factory) points. The minimization of the cost consisting of the pipe and energy cost terms is considered as the objective function to determine the stated decision variables. Pipe cost is given as the function of pipe diameters and the energy cost is defined as function of pipe diameters and slurry amounts. Energy cost is obtained by using the relation that is previously determined after the experimental studies made for the magnetite ore. The optimization method used in the study is genetic algorithm method. A commercially available software written in C language is used and modified for the present study The proposed methodology to solve this nonlinear programming problem is applied to a transportation system and it is seen that the methodology made the complex, labor intensive equation solution process very convenient for the users.
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Pratap, Rana Jitendra. "Design and Optimization of Microwave Circuits and Systems Using Artificial Intelligence Techniques." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7225.
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In this thesis, a new approach combining neural networks and genetic algorithms is presented for microwave design. In this method, an accurate neural network model is developed from the experimental data. This neural network model is used to perform sensitivity analysis and derive response surfaces. An innovative technique is then applied in which genetic algorithms are coupled with the neural network model to assist in synthesis and optimization. The proposed method is used for modeling and analysis of circuit parameters for flip chip interconnects up to 35 GHz, as well as for design of multilayer inductors and capacitors at 1.9 GHz and 2.4 GHz. The method was also used to synthesize mm wave low pass filters in the range of 40-60 GHz. The devices obtained from layout parameters predicted by the neuro-genetic design method yielded electrical response close to the desired value (95% accuracy). The proposed method also implements a weighted priority scheme to account for tradeoffs in microwave design. This scheme was implemented to synthesize bandpass filters for 802.11a and HIPERLAN wireless LAN applications in the range of 5-6 GHz.
This research also develops a novel neuro-genetic design centering methodology for yield enhancement and design for manufacturability of microwave devices and circuits. A neural network model is used to calculate yield using Monte Carlo methods. A genetic algorithm is then used for yield optimization. The proposed method has been used for yield enhancement of SiGe heterojunction bipolar transistor and mm wave voltage-controlled oscillator. It results in significant yield enhancement of the SiGe HBTs (from 25 % to 75 %) and VCOs (from 8 % to 85 %). The proposed method is can be extended for device, circuit, package, and system level integrated co-design since it can handle a large number of design variables without any assumptions about the component behavior. The proposed algorithm could be used by microwave community for design and optimization of microwave circuits and systems with greater accuracy while consuming less computational time.
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Gillman, Kevin M. "Optimization of Shape, Size, and Topology Design Variables in Trusses with a Genetic Algorithm." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd683.pdf.
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Chong, Ian Ian. "Vibration control and genetic algorithm based design optimization on self-sensing active constrained layer damped rotating plates." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493698.
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Bernshteyn, Mikhail. "Simulation optimization methods that combine multiple comparisons and genetic algorithms with applications in design for computer and supersaturated experiments /." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486397841221374.
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Bowman, Kelly Eric. "Optimization Constrained CAD Framework with ISO-Performing Design Generator." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2599.pdf.
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Kalkan, Sinan. "A Comparative Study Of Evolutionary Network Design." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1097518/index.pdf.
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In network design,
a communication network is optimized for a given set of parameters like
cost, reliability and delay.
This study analyzes network design problem using Genetic Algorithms in detail and makes
comparison of different approaches and representations.
Encoding of a problem is one of the most crucial design choices in
Genetic Algorithms. For network design problem, this study compares
adjacency matrix representation with list of edges representation.
Also, another problem is defining a fair fitness function that will
not favor one optimization parameter to the other. Multi-objective
optimization is a recommended solution for such problems. This study
describes and compares some of those approaches for different combinations
in network design problem.
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Henderson, Joseph Lynn. "Combined structural and manufacturing optimization of stiffened composite panels." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-09182008-063429/.
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Sarac, Yavuz. "Optimum Design Of Pin-jointed 3-d Dome Structures Using Global Optimization Techniques." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606793/index.pdf.
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Difficult gradient calculations, converging to a local optimum without exploring the design space adequately, too much dependency on the starting solution, lacking capabilities to treat discrete and mixed design variables are the main drawbacks of conventional optimization techniques. So evolutionary optimization methods received significant interest amongst researchers in the optimization area. Genetic
algorithms (GAs) and simulated annealing (SA) are the main representatives of evolutionary optimization methods. These techniques emerged as powerful and modern strategies to efficiently deal with the difficulties encountered in conventional techniques, and therefore rightly attracted a substantial interest and popularity. The underlying concepts of these techniques and thus their algorithmic models have been devised by establishing between the optimization task and events occurring in nature. While, Darwin’
s survival of the fittest theory is mimicked by GAs, annealing process of physical systems are employed to SA. On the other hand, dome structures are among the most preferred types of structures for large unobstructed areas. Domes have been of a special interest in the sense that they enclose a maximum amount of space with a minimum surface. This feature provides economy in terms of consumption of constructional materials. So merging these two concepts make it possible to obtain optimum designs of dome structures. This thesis is concerned with the use of GAs and SA in optimum structural design of dome structures, which range from some relatively simple problems to the problems of increased complexity. In this thesis, firstly both techniques are investigated in terms of their practicality and applicability to the problems of interest. Then numerous test problems taken from real life conditions are studied for comparing the success of the proposed GA and SA techniques with other discrete and continuous optimization methods. The results are discussed in detail to reach certain recommendations contributing to a more efficient use of the techniques in optimum structural design of pin-jointed 3-D dome structures.
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Long, Craig Stephen. "Optimal structural design for a planar parallel platform for machining." Diss., Pretoria : [S.n], 2002. http://upetd.up.ac.za/thesis/available/etd-11302005-093541/.
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Zhang, Xiaoqin. "THERMAL-ECONOMIC OPTIMIZATION AND STRUCTURAL EVALUATION FOR AN ADVANCED INTERMEDIATE HEAT EXCHANGER DESIGN." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1462891005.
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Bilal, Mohd. "A Heuristic Search Algorithm for Asteroid Tour Missions." Thesis, Luleå tekniska universitet, Rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71361.
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Since the discovery of Ceres, asteroids have been of immense scientific interest and intrigue. They hold answers to many of the fundamental questionsabout the formation and evolution of the Solar System. Therefore, a missionsurveying the asteroid belt with close encounter of carefully chosen asteroidswould be of immense scientific benefit. The trajectory of such an asteroidtour mission needs to be designed such that asteroids of a wide range ofcompositions and sizes are encountered; all with an extremely limited ∆Vbudget.This thesis presents a novel heuristic algorithm to optimize trajectoriesfor an asteroid tour mission with close range flybys (≤ 1000 km). The coresearch algorithm efficiently decouples combinatorial (i.e. choosing the asteroids to flyby)and continuous optimization (i.e. optimizing critical maneuversand events) of what is essentially a mixed integer programming problem.Additionally, different methods to generate a healthy initial population forthe combinatorial optimization are presented.The algorithm is used to generate a set of 1800 feasible trajectories withina 2029+ launch frame. A statistical analysis of these set of trajectories isperformed and important metrics for the search are set based on the statistics.Trajectories allowing flybys to prominent families of asteroids like Flora andNysa with ∆V as low as 4.99 km/s are obtained.Two modified implementations of the algorithm are presented. In a firstiteration, a large sample of trajectories is generated with a limited numberof encounters to the most scientifically interesting targets. While, a posteriori, trajectories are filled in with as many small targets as possible. Thisis achieved in two different ways, namely single step extension and multiplestep extension. The former fills in the trajectories with small targets in onestep, while the latter optimizes the trajectory by filling in with one asteroid per step. The thesis also presents detection of asteroids for successfullyperforming flybys. A photometric filter is developed which prunes out badlyilluminated asteroids. The best trajectory is found to perform well againstthis filter such that nine out of the ten planned flybys are feasible.
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Shang, Jing. "MULTI-DOMAIN, MULTI-OBJECTIVE-OPTIMIZATION-BASED APPROACH TO THE DESIGN OF CONTROLLERS FOR POWER ELECTRONICS." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/52.
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Power converter has played a very important role in modern electric power systems. The control of power converters is necessary to achieve high performance. In this study, a dc-dc buck converter is studied. The parameters of a notional proportional-integral controller are to be selected. Genetic algorithms (GAs), which have been widely used to solve multi-objective optimization problems, is used in order to locate appropriate controller design. The control metrics are specified as phase margin in frequency domain and voltage error in time-domain. GAs presented the optimal tradeoffs between these two objectives. Three candidate control designs are studied in simulation and experimentally. There is some agreement between the experimental results and the simulation results, but there are also some discrepancies due to model error. Overall, the use of multi-domain, multi-objective-optimization-based approach has proven feasible.
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Ittiwattana, Waraporn. "A Method for Simulation Optimization with Applications in Robust Process Design and Locating Supply Chain Operations." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1030366020.
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Zuo, Zihao, and Zhihao zuo@rmit edu au. "Topology optimization of periodic structures." RMIT University. Civil, Environmental and Chemical Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091217.151415.
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This thesis investigates topology optimization techniques for periodic continuum structures at the macroscopic level. Periodic structures are increasingly used in the design of structural systems and sub-systems of buildings, vehicles, aircrafts, etc. The duplication of identical or similar modules significantly reduces the manufacturing cost and greatly simplifies the assembly process. Optimization of periodic structures in the micro level has been extensively researched in the context of material design, while research on topology optimization for macrostructures is very limited and has great potential both economically and intellectually. In the present thesis, numerical algorithms based on the bi-directional evolutionary structural optimization method (BESO) are developed for topology optimization for various objectives and constraints. Soft-kill (replacing void elements with soft elements) formulations of topology optimization problems for solid-void solutions are developed through appropriate material interpolation schemes. Incorporating the optimality criteria and algorithms for mesh-independence and solution-convergence, the present BESO becomes a reliable gradient based technique for topology optimization. Additionally, a new combination of genetic algorithms (GAs) with BESO is developed in order to stochastically search for the global optima. These enhanced BESO algorithms are applied to various optimization problems with the periodicity requirement as an extra constraint aiming at producing periodicity in the layout. For structures under static loading, the present thesis addresses minimization of the mean compliance and explores the applications of conventional stiffness optimization for periodic structures. Furthermore, this thesis develops a volume minimization formulation where the maximum deflection is constrained. For the design of structures subject to dynamic loading, this thesis develops two different approaches (hard-kill and soft-kill) to resolving the problem of localized or artificial modes. In the hard-kill (completely removing void elements) approach, extra control measures are taken in order to eliminate the localized modes in an explicit manner. In the soft-kill approach, a modified power low material model is presented to prevent the occurrence of artificial and localized modes. Periodic stress and strain fields cannot be assumed in structures under arbitrary loadings and boundaries at the macroscopic level. Therefore being different from material design, no natural base cell can be directly extracted from macrostructures. In this thesis, the concept of an imaginary representative unit cell (RUC) is presented. For situations when the structure cannot be discretized into equally-sized elements, the concept of sensitivity density is developed in order for mesh-independent robust solutions to be produced. The RUC and sensitivity density based approach is incorporated into various topology optimization problems to obtain absolute or scaled periodicities in structure layouts. The influence of this extra constraint on the final optima is investigated based on a large number of numerical experiments. The findings shown in this thesis have established appropriate techniques for designing and optimizing periodic structures. The work has provided a solid foundation for creating a practical design tool in the form of a user-friendly computer program suitable for the conceptual design of a wide range of structures.
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Elseifi, Mohamed A. "A new scheme for the optimum design of stiffened composite panels with geometric imperfections." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/29250.
Full textAbstract:
Thin walled stiffened composite panels, which are among the most utilized structural elements in engineering, possess the unfortunate property of being highly sensitive to geometrical imperfections. Existing analysis codes are able to predict the nonlinear postbuckling behavior of a structure with specified imperfections. However, it is impossible to determine the geometric imperfection profile of a nonexistent composite panel early in the design. This is due to the variety of uncertainties that are involved in the manufacturing of these panels. As a mater of fact, due to the very nature of the manufacturing processes, it is hard to imagine that a given manufacturing process could ever produce two identical panels.
The objective of this study is to introduce a new design methodology in which a manufacturing model and a convex model for uncertainties are used in conjunction with a nonlinear design tool in order to obtain a more realistic, better performing final design. First a finite element code for the nonlinear postbuckling analysis of stiffened panels is introduced. Next, a manufacturing model for the simulation of the autoclave curing of epoxy matrix composites is presented. A convex model for the uncertainties in the imperfections is developed in order to predict the weakest panel profile among a family of panels. Finally, the previously developed tools are linked in a closed loop design scheme aimed at obtaining a final design that incorporates the manufacturing tolerances information through more realistic imperfections.Ph. D.
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Cott, Andrew. "An examination of analysis and optimization procedures within a PBSD framework." Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/2318.
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Abdussalam, Fathi M. A. "Antenna design using optimization techniques over various computaional electromagnetics. Antenna design structures using genetic algorithm, Particle Swarm and Firefly algorithms optimization methods applied on several electromagnetics numerical solutions and applications including antenna measurements and comparisons." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17217.
Full textAbstract:
Dealing with the electromagnetic issue might bring a sort of discontinuous and nondifferentiable
regions. Thus, it is of great interest to implement an appropriate optimisation
approach, which can preserve the computational resources and come up with a global
optimum. While not being trapped in local optima, as well as the feasibility to overcome some
other matters such as nonlinear and phenomena of discontinuous with a large number of
variables.
Problems such as lengthy computation time, constraints put forward for antenna
requirements and demand for large computer memory, are very common in the analysis due
to the increased interests in tackling high-scale, more complex and higher-dimensional
problems. On the other side, demands for even more accurate results always expand
constantly. In the context of this statement, it is very important to find out how the recently
developed optimization roles can contribute to the solution of the aforementioned problems.
Thereafter, the key goals of this work are to model, study and design low profile antennas for
wireless and mobile communications applications using optimization process over a
computational electromagnetics numerical solution. The numerical solution method could be
performed over one or hybrid methods subjective to the design antenna requirements and
its environment.
Firstly, the thesis presents the design and modelling concept of small uni-planer Ultra-
Wideband antenna. The fitness functions and the geometrical antenna elements required for
such design are considered. Two antennas are designed, implemented and measured. The
computed and measured outcomes are found in reasonable agreement. Secondly, the work
is also addressed on how the resonance modes of microstrip patches could be performed
using the method of Moments. Results have been shown on how the modes could be
adjusted using MoM. Finally, the design implications of balanced structure for mobile
handsets covering LTE standards 698-748 MHz and 2500-2690 MHz are explored through
using firefly algorithm method. The optimised balanced antenna exhibits reasonable
matching performance including near-omnidirectional radiations over the dual desirable
operating bands with reduced EMF, which leads to a great immunity improvement towards
the hand-held.General Secretariat of Education and Scientific Research Libya