As tecnologias da informação, telecomunicações e mobilidade aliadas às mudanças econômicas e sociais acarretaram uma grande reestruturação da indústria. Entre estas mudanças, verifica-se um maior nível de descentralização e especialização das unidades produtivas, o aumento da automação dos processos e, em conseqüência, uma maior quantidade e complexidade nas interações de seus sub-sistemas. De forma a lidar com esta complexidade e facilitar o estudo e projeto de novos sistemas, faz-se necessário o uso de modelos, que são analisados por exemplo, através de simulação. Entre elas destaca-se a simulação distribuída, a qual trata da evolução de situações/cenários do sistema em computadores fisicamente dispersos, conectados através de uma rede de comunicação, visando, por exemplo, a redução do tempo de simulação, a simulação de grandes modelos(composto por muitos elementos), maior tolerânica a falhas e mesmo a distribuição geográfica. Visando contribuir para uma maior aplicabilidade das técnicas de modelagem e simulação, em especial a distribuída, propõe-se nesta tese (1) um novo método para a modelagem hierárquica de sistemas produtivos; e (2) um novo algoritmo para a sincronização da evolução do tempo de simulação de diferentes simuladores interagindo através de redes de comunicação (LAN, WAN). No método de modelagem proposto, utiliza-se uma abordagem top-down para a decomposição do sistema, partindo-se de um nível de abstração para um de maior detalhamento, permitindo, assim, um maior nível de conhecimento quanto ao comportamento dos elementos e suas interações. No nível de detalhamento desejado, utiliza-se a Rede de Petri na modelagem dos elementos básicos do sistema, que são, assim como na orientação a objetos, denominados classes. Em seguida, através de uma abordagem bottom-up, estes modelos são agrupados, formando modelos mais complexos: componentes e aplicativos. A fim de garantir a interação entre estes elementos, foram definidos um conjunto de interfaces, bem como suas regras de relacionamento. Este método foi aplicado a um estudo de caso para comprovar sua eficácia. No que diz respeito ao algoritmo proposto para sincronizar os tempos de simulação, utiliza-se como subsidio o mecanismo de gerenciamento da transmissão de dados em redes conhecido como Token Ring. Um simulador de eventos, distribuído, foi implementado com a finalidade de validar o algoritmo proposto. / Evolution on the information technology, telecommunications and transport systems, associated to social and economic changes around the world have caused a significative reorganization of the industry. In this context, a high level of decentralization and specialization of the productive units, as well as an increment of the automation level used in productive processes have been verified. It results on the increase of the amount and the complexity of the enterprise subsystems interactions. Modeling techniques are used with simulation to deal with the complexity, to analysis, and to design new productive systems. Among the simulation approaches, distributed simulation is distinguished. It deals with the execution of simulation in physically dispersed computers connected through a LAN (Local Area Network), providing, for example, the reduction of the simulation time, huge simulation models (models with many elements), fault tolerance, as well as geographic dispersion. To contribute for the evolution of modeling and simulation techniques, in special the distributed one, it is proposed on this work: (1) a new method for the hierarchical modeling of productive systems; and (2) a new time synchronization algorithm used to manage the time evolution of a set of distributed simulation software. On the proposed modeling method it is used a top-down approach to decompose the system in basic elements, starting in a high-level abstraction model, and ending with a set of basic models with high level of detail. Then, these models are modeled using Petri net. As well as on object-oriented languages, each model is called class. After that, using a bottom-up approach, these basic models are grouped to generate more complex models: component and application. A set of interfaces, as well as its relationship rules had been defined to guarantee the interaction among these elements. This method was applied to a case study to confirm its effectiveness. About the time synchronization algorithm, the token ring protocol is used as subsidy. An event based distributed simulator was implemented with the purpose to validate the proposed algorithm.

This study gives a description about the development of a computer model, RealPipe, which relates genetic algorithm (GA) to the well known problem of least-cost design of water distribution network. GA methodology is an evolutionary process, basically imitating evolution process of nature. GA is essentially an efficient search method basically for nonlinear optimization cases. The genetic operations take place within the population of chromosomes. By means of various operators, the genetic knowledge in chromosomes change continuously and the success of the population progressively increases as a result of these operations. GA optimization is also well suited for optimization of water distribution systems, especially large and complex systems. The primary objective of this study is optimization of a water distribution network by GA. GA operations are realized on a special program developed by the author called RealPipe. RealPipe optimizes given water network distribution systems by considering capital cost of pipes only. Five operators are involved in the program algorithm. These operators are generation, selection, elitism, crossover and mutation. Optimum population size is found to be between 30-70 depending on the size of the network (i.e. pipe number) and number of commercially available pipe size. Elitism rate should be around 10 percent. Mutation rate should be selected around 1-5 percent depending again on the size of the network. Multipoint crossover and higher rates are advisable. Also pressure penalty parameters are found to be much important than velocity parameters. Below pressure penalty parameter is the most important one and should be roughly 100 times higher than the other. Two known networks of the literature are examined using RealPipe and expected results are achieved. N8.3 network which is located in the northern side of Ankara is the case study. Total cost achieved by RealPipe is 16.74 percent lower than the cost of the existing network / it should be noted that the solution provided by RealPipe is hydraulically improved.

Tato práce se zabývá možností paralelizace Genetického Algoritmu a jeho ná-sledné evaluace pomocí testovacích účelových funkcí. První část je teoretická a shrnuje základní poznatky z oblasti Genetických Algoritmů, paralelních archi-tektur, paralelních výpočtů a optimalizace. A dále je tato část doplněna o mož-nosti paralelizace Genetického Algoritmu. V následující praktické části je rozebrán algoritmus paralelního Genetického Algoritmu, jenž je použitý při experimentu a také je diskutována struktura a účel zvoleného experimentu. Následně jsou diskutovány výsledky získané z běhu experimentu na Eridani Clusteru z pohledu zrychlení výpočtu, kvality nalezeného řešení a závislosti kvality řešení na migračním schématu.

No description available.

With continuously increasing urbanization, consumer demands and expansion of water supply systems, determination of efficient pump schedules became a more difficult task. Pumping energy costs constitute a significant part of the operational cost of the water distribution networks. This study aims to provide an effective daily pump schedule by minimizing the energy costs for constant and also for multi tariff of electricity (3 Kademeli Elektrik Tarifesi) in water distribution network. A case study has been performed in an area covering N8.3 and N7 pressure zones which are parts of Ankara water distribution network. Both pressure zones consists of 3 multiple pumps in pump station and one tank having 5000 m3 storage volume each. By using genetic algorithm based software (WaterCAD Darwin Scheduler) least-cost pump scheduling and operation policy for each pump station has been determined while satisfying target hydraulic performance requirements such as minimum and maximum service pressures, final water level of storage tank and maximum velocity in pipeline. 32 different alternative scenarios have been created which include multi tariff energy prices, constant tariff energy price, insulated system condition, uninsulated system condition and different pump combinations. The existing base scenario and alternative scenarios which were prepared by using optimal pump schedules have been compared and the achievements of optimizing pump operation have been analyzed. At the end of the study, a satisfying result has been observed that by using determined optimal pump schedule, minimum % 14 of total energy cost can be saved in existing water supply system.

This thesis presents an aerodynamic shape optimization methodology for rotor blades of horizontal axis wind turbines. Genetic Algorithm and Blade Element Momentum Theory are implemented in order to find maximum power production at a specific wind speed, rotor speed and rotor diameter. The potential flow solver, XFOIL, provides viscous aerodynamic data of the airfoils. Optimization variables are selected as the sectional chord length, the sectional twist and the blade profiles at root, mid and tip regions of the blade. The blade sections are defined by the NACA four digit airfoil series or arbitrary airfoil profiles defined by a Bezier curve. Firstly, validation studies are performed with the airfoils and the wind turbines having experimental data. Then, optimization studies are performed on the existing wind turbines. Finally, design optimization applications are carried out for a 1 MWwind turbine.

Im Folgenden wird ein Promotionsprojekt zur Implementierung und Optimierung von hybriden Indexstrukturen beschrieben. Die erhöhte Suchperformance wird bei hybriden Indexstrukturen durch einen höheren Aufwand an Vorberechnungen bei Einfügeoperationen erreicht. Dadurch ergibt sich, im Gegensatz zu Ansätzen, welche mehrere Indexstrukturen miteinander verbinden oder getrennte Suchanfragen ausführen eine Effizienz der Reorganisation hybrider Indexstrukturen, die prohibitiv für den Einsatz in den meisten Anwendungen ist. Diese sollen innerhalb des Promotionsprojekts optimiert werden, um eine Einsatzfähigkeit in realistischen Szenarien gewährleisten zu können.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-03-22T11:33:37Z No. of bitstreams: 1 arquivototal.pdf: 1643585 bytes, checksum: 5ba2336704d1de91b41bbe323ef3781e (MD5) / Made available in DSpace on 2016-03-22T11:33:37Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 1643585 bytes, checksum: 5ba2336704d1de91b41bbe323ef3781e (MD5) Previous issue date: 2015-05-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In this paper we discuss the problem of linear tting to experimental data using a method bio-inspired of optimization, i.e., it imitates the biological concepts attempt to nd optimal or suboptimal results. The method used is the genetic algorithm (GA), AG makes use of the theory of Darwinian evolution to nd the best route for the desired maximum point. Traditionally, the linear tting is made through the method of least squares. The method is e cient, but is di cult to justify the pre-calculus classes. Therefore, the alternative AG comes as a computationally exhaustive procedure, however easy justi cation for these classes. Thus, the purpose of this study is to compare the results of linear tting for some control scenarios using this methods and certify the quality of the adjustments obtained by the approximate method. At the end of the work it was found that the results are solid enough to justify the alternative method and the proposed use of this optimization process has the potential to spark interest in other areas of mathematics. / Neste trabalho abordaremos o problema de ajuste linear para dados experimentais através de um método de otimização bio-inspirado, isto é, que mimetiza conceitos biológicos na tentativa de buscar resultados ótimos ou sub-ótimos. O método utilizado é o algoritmo genético (AG), AG faz uso da teoria da evolução Darwiniana para buscar a melhor rota para o ponto de máximo desejado. Tradicionalmente, o ajuste linear é feito através do método de mínimos quadrados. Tal método é e ciente, porém é de difícil justi cativa para as turmas pré-cálculo. Diante disso, a alternativa do AG vem como um procedimento exaustivo computacionalmente, entretanto de fácil justi cativa para essas turmas. Assim, a proposta do trabalho é comparar os resultados de ajuste linear para alguns cenários de controle através dos dois métodos e certi car a qualidade dos ajustes obtidos pelo método aproximado. No nal do trabalho constatou-se que os resultados encontrados sÿo sólidos o bastante para justi car o método alternativo e que a proposta da utilização desse processo de otimização tem potencial para despertar interesse em outras áreas da matemática.

Im Folgenden wird ein Promotionsprojekt zur Implementierung und Optimierung von hybriden Indexstrukturen beschrieben. Die erhöhte Suchperformance wird bei hybriden Indexstrukturen durch einen höheren Aufwand an Vorberechnungen bei Einfügeoperationen erreicht. Dadurch ergibt sich, im Gegensatz zu Ansätzen, welche mehrere Indexstrukturen miteinander verbinden oder getrennte Suchanfragen ausführen eine Effizienz der Reorganisation hybrider Indexstrukturen, die prohibitiv für den Einsatz in den meisten Anwendungen ist. Diese sollen innerhalb des Promotionsprojekts optimiert werden, um eine Einsatzfähigkeit in realistischen Szenarien gewährleisten zu können.

Genetic algorithms (GA) are stochastic global search methods based on the mechanics of natural biological evolution, proposed by John Holland in 1975. Here in this thesis, we have exploited possible utilities of Genetic Algorithm optimization in Nuclear Magnetic Resonance (NMR) experiments. We have performed (i ) Pulse sequence generation and optimization for NMR Quantum Information Processing, (ii ) efficient creation of NOON states, (iii ) Composite operator design and (iv ) delay optimization for refocused quantitative INEPT. We have generated time optimal as well as robust pulse sequences for popular quantum gates. A Matlab package is developed for basic Target unitary operator to pulse sequence optimization and is explained with an example. Chapter 1 contains a brief introduction to NMR, Quantum computation and Genetic algorithm optimization. Experimental unitary operator decomposition using Genetic Algorithm is explained in Chapter 2. Starting from a two spin homonu- clear system (5-Bromofuroic acid), we have generated hard pulse sequences for performing (i ) single qubit rotation, (ii ) controlled NOT gates and (iii ) pseudo pure state creation, which demonstrates universal quantum computation in such systems. The total length of the pulse sequence for the single qubit rotation of an angle π/2 is less than 500µs, whereas the conventional method (using a selective soft pulse) would need a 2ms shaped pulse. This substantial shortening in time can lead to a significant advantage in quantum circuits. We also demonstrate the creation of Long Lived Singlet State and other Bell states, directly from thermal equilibrium state, with the shortest known pulse sequence. All the pulse sequences generated here are generic i.e., independent of the system and the spectrometer. We further generalized this unitary operator decomposition technique for a variable operators termed as Fidelity Profile Optimization (FPO) (Chapter 3) and performed quantum simulations of Hamiltonian such as Heisenberg XY interaction and Dzyaloshinskii-Moriya interaction. Exact phase (φ) dependent experimental unitary decompositions of Controlled-φ and Controlled Controlled-φ are solved using first order FPO. Unitary operator decomposition for experimental quantum simulation of Dzyaloshinskii-Moriya interaction in the presence of Heisenberg XY interaction is solved using second order FPO for any relative strengths of interactions (γ) and evolution time (τ ). Experimental gate time for this decomposition is invariant under γ or τ , which can be used for relaxation independent studies of the system dynamics. Using these decompositions, we have experimentally verified the entanglement preservation mechanism suggested by Hou et al. [Annals of Physics, 327 292 (2012)]. NOON state or Schrodinger cat state is a maximally entangled N qubit state with superposition of all individual qubits being at |0 and being at |1 . NOON states have received much attention recently for their high precession phase measurements, which enables the design of high sensitivity sensors in optical interfer- ometry and NMR [Jones et al. Science, 324 1166(2009)]. We have used Genetic algorithm optimization for efficient creation of NOON states in NMR (Chapter 4). The decompositions are, (i ) a minimal in terms of required experimental resources – radio frequency pulses and delays – and have (ii ) good experimental fidelity. A composite pulse is a cluster of nearly connected rf pulses which emulate the effect of a simple spin operator with robust response over common experimental imperfections. Composite pulses are mainly used for improving broadband de- coupling, population inversion, coherence transfer and in nuclear overhauser effect experiments. Composite operator is a generalized idea where a basic operator (such as rotation or evolution of zz coupling) is made robust against common experimental errors (such as inhomogeneity / miscalibration of rf power or errror in evaluation of zz coupling strength) by using a sequence of basic operators available for the system. Using Genetic Algorithm optimization, we have designed and experimentally verified following composite operators, (i ) broadband rotation pulses, (ii ) rf inhomogeneity compensated rotation pulses and (iii ) zz evolution operator with robust response over a range of zz coupling strengths (Chapter 5). We also performed rf inhomogeneity compensated Controlled NOT gate. Extending Genetic Algorithm optimization in classical NMR applications, we have improved the quantitative refocused constant-time INEPT experiment (Q-INEPT- CT) of M¨kel¨ et al. [JMR 204(2010) 124-130] with various optimization constraints . The improved ‘average polarization transfer’ and ‘min-max difference’ of new delay sets effectively reduces the experimental time by a factor of two (compared with Q-INEPT-CT, M¨kel¨ et al.) without compromising on accuracy (Chapter 6). We also introduced a quantitative spectral editing technique based on average polarization transfer. These optimized quantitative experiments are also described in Chapter 6. Time optimal pulse sequences for popular quantum gates such as, (i ) Controlled Hadamard (C-H) gate, (ii ) Controlled-Controlled-NOT (CCNOT) Gate and (iii ) Controlled SWAP (C-S) gate are optimized using Genetic Algorithm (Appendix. A). We also generated optimal sequences for Quantum Counter circuits, Quantum Probability Splitter circuits and efficient creation of three spin W state. We have developed a Matlab package based on GA optimization for three spin target operator to pulse sequence generator. The package is named as UOD (Unitary Operator Decomposition) is explained with an example of Controlled SWAP gate in Appendix. B. An algorithm based on quantum phase estimation, which discriminates quantum states non-destructively within a set of arbitrary orthogonal states, is described and experimentally verified by a NMR quantum information processor (Appendix. C). The procedure is scalable and can be applied to any set of orthogonal states. Scalability is demonstrated through Matlab simulation.