Le Benchmarking des systèmes client-serveur implique des infrastructures techniques réparties complexes, dont la gestion nécessite une approche autonomique. Cette gestion s'appuie sur une suite d'étapes, observation, analyse et rétroaction, qui correspond au principe d'une boucle de contrôle autonome. Des travaux antérieurs dans le domaine du test de performances ont montré comment introduire des fonctionnalités de test autonome par le biais d'une injection de charge auto-régulée. L'objectif de cette thèse est de suivre cette démarche de calcul autonome (autonomic computing) en y introduisant des fonctionnalités d'optimisation autonome. On peut ainsi obtenir automatiquement des résultats de benchmarks fiables et comparables, mettant en oeuvre l'ensemble des étapes de self-benchmarking. Notre contribution est double. D'une part, nous proposons un algorithme original pour l'optimisation dans un contexte de test de performance, qui vise à diminuer le nombre de solutions potentielles à tester, moyennant une hypothèse sur la forme de la fonction qui lie la valeur des paramètres à la performance mesurée. Cet algorithme est indépendant du système à optimiser. Il manipule des paramètres entiers, dont les valeurs sont comprises dans un intervalle donné, avec une granularité de valeur donnée. D'autre part, nous montrons une approche architecturale à composants et une organisation du benchmark automatique en plusieurs boucles de contrôle autonomes (détection de saturation, injection de charge, calcul d'optimisation), coordonnées de manière faiblement couplée via un mode de communication asynchrone de type publication-souscription. Complétant un canevas logiciel à composants pour l'injection de charge auto-régulée, nous y ajoutons des composants pour reparamétrer et redémarrer automatiquement le système à optimiser.Deux séries d'expérimentations ont été menées pour valider notre dispositif d'auto-optimisation. La première série concerne une application web de type achat en ligne, déployée sur un serveur d'application JavaEE. La seconde série concerne une application à trois tiers effectifs (WEB, métier (EJB JOnAS) et base de données) clusterSample. Les trois tiers sont sur des machines physiques distinctes. / Benchmarking client-server systems involves complex, distributed technical infrastructures, whose management deserves an autonomic approach. It also relies on observation, analysis and feedback steps that closely matches the autonomic control loop principle. While previous works in performance testing have shown how to introduce autonomic load testing features through self-regulated load injection, the goal of this thesis is to follow this approach of autonomic computing to introduce self-optimization features in this architecture to obtain reliable and comparable benchmark results, and to achieve the fully principle of Self-benchmarking.Our contribution is twofold. From the algorithmic point of view, we propose an original optimization algorithm in the context of performance testing. This algorithm is divided into two parts. The first one concerns the overall level, i.e. the control of the performance index evolution, based on global parameters setting of the system. The second part concerns the search for the optimum when only one parameter is modified. From the software architecture point of view, we complete the Fractal component-based architecture, containing several autonomic control loops (saturation, injection, optimization computing) and we implement the coordination principle between these loops by asynchronous messages according to the publish-subscribe communication paradigm. To apply a given parameters setting on the system under test, we introduced new components Configurators to support the setting of parameters before starting the test process. It may also be necessary to restart all or part of the system to optimize to ensure that the new setting is effectively taken into account. We introduced components Starters to cover this need in a specific way for each system.To validate our self-optimization framework, two types of campaigns have been conducted onto the servers of Orange Labs in Meylan and the servers of the LISTIC Laboratory of the University of Savoie in Polytech Annecy-Chambéry (Annecy le Vieux). The first one is a WEB online shopping application deployed on a Java EE application server JonAS. The second one is a three-tiers application (WEB, business (EJB JOnAS) and data base) clusterSample. The three tiers are in three separate machines.

The aim of this thesis is establishment of an overall framework of a novel control parameter optimization of automotive engine. Today, control parameters of an automotive engine have to be adjusted adequately and simultaneously to achieve plural criteria such as environmental emissions, fuel-consumption and engine torque. This process is called 'engine calibration'. Because many electronic control devices have been adopted for engine to satisfy these objectives, the complexity of engine calibration is increasing year to year. Recent progress in automatic control and instrumentation provides a smart environment called Hardware In the Loop Simulation (HILS) for engine calibration. In addition, Response Surface Methodology (RSM) based on statistical model is currently employed as the optimization method. Nevertheless, this approach is complicated by adequate model selection, precise model construction, and close model validation to confirm the precision of the model output. To cope with these problems, we noticed experiment-based optimization via HILS environment based on Multi-objective Evolutionary Algorithms (MOEAs), that is expected to be a powerful optimization framework for real world problems such as engineering design, as another automatic calibration approach. In experiment-based optimization, the parameters of a real system are optimized directly by optimization techniques in real time through experimentation. In this thesis, this approach is called Experiment-Based Evolutionary Multi-objective Optimization (EBEMO) and it is proposed as a novel automatic engine calibration technique. This approach can release us from burdens of model selection, construction, and validation. When using this technique, calibration can be done immediately after specifications have been changed after optimization. Hence, EBEMO promises to be an effective approach to automatic engine calibration. However, since conventional MOEAs face several difficulties, it is not easy to apply it to real engines. On the one hand, deterioration factors of the search performance of MOEAs in real environments have to be considered. For example, the observation noise of sensors included in output interferes with convergence of MOEAs. In addition, transient response by parameter switching also has similar harmful effects. Moreover, the periodicity of control inputs increase the complexity of the problems. On the other hand, the search time of MOEAs in real environments has to reduce because MOEAs require a tremendous number of evaluations. While we can obtain many measurements with HILS, severe limitations in the number of fitness evaluations still exist because the real experiments need real-time evaluations. Therefore, it is difficult to obtain a set of Pareto optimal solutions in practical time with conventional MOEAs. Additionally, plural MOPs defined by plural operating conditions of map-based controllers has to be optimized. In this thesis, to overcome the difficulties and to make EBEMO using the HILS environment feasible, five techniques are proposed. Each technique is developed through problem formulation, and their effectiveness are confirmed via numerical and real engine experiments. First, observation noise handling technique for MOEAs is considered. Because observation noise deteriorates the search ability of MOEAs, a memorybased fitness estimation method to exclude observation noise is introduced. Then, a crossover operator for periodic functions is proposed. Periodicity exists in engineering problems and leads to harmful effects on the performance of evolutionary algorithms. Moreover, the influence of transient response caused by parameter switching for dynamical systems is considered. In order to solve this problem, a solver of traveling salesman problems is used to determine the evaluation order of individuals. In addition, Pre-selection as acceleration method of MOEAs is proposed. In this technique, the generated offspring are pre-evaluated in the approximation model made by the search history, and then the promising offspring are evaluated in a real environment. Finally, parameterization of multi-objective optimization problems is considered. In engine calibration for maps, optimal control parameters have to be obtained at each operating condition such as engine speed and torque. This problem can be formulated in a form that needs to solve all of the plural multi-objective optimization problems defined by plural conditional variables. To solve this problem effectively, an interpolative initialization method is proposed. Through the real engine experiments, it was confirmed that EBEMO can achieve a practical search accuracy and time by using proposed techniques. In conclusion, the contribution of EBEMO for engine calibration is discussed. Additionally, the directions for future work are outlined. / Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(情報学) / 甲第14187号 / 情博第320号 / 新制||情||61(附属図書館) / 26493 / UT51-2008-N504 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 喜多 一, 教授 酒井 徹朗, 教授 片井 修 / 学位規則第4条第1項該当

Peridynamics (PD), a reformulation of the Classical Continuum Mechanics (CCM), is a new and promising meshless and nonlocal computational method in solid mechanics. To permit discontinuities, the PD integro-differential equation contains spatial integrals and time derivatives. PD can be considered as the continuum version of molecular dynamics. This feature of PD makes it a good candidate for multi-scale analysis of materials. Concurrently, the topology optimization has also been rapidly growing in view of the need to design lightweight and high performance structures. Therefore, this thesis presents the potential for a peridynamics-based topology optimization approach. To avoid the gradient calculations, a heuristic topology optimization method is employed. The minimization of the PD strain energy density is set as the objective function. The structure is optimized based on a modified solid isotropic material with a penalization approach and a projection scheme is utilized to obtain distinct results. Several test cases have been studied to analyze the suitability of the proposed method in topology optimization. / Graduate

Many scientific, engineering and economic problems involve the optimisation of a set of parameters. These problems include examples like minimising the losses in a power grid by finding the optimal configuration of the components, or training a neural network to recognise images of people's faces. Numerous optimisation algorithms have been proposed to solve these problems, with varying degrees of success. The Particle Swarm Optimiser (PSO) is a relatively new technique that has been empirically shown to perform well on many of these optimisation problems. This thesis presents a theoretical model that can be used to describe the long-term behaviour of the algorithm. An enhanced version of the Particle Swarm Optimiser is constructed and shown to have guaranteed convergence on local minima. This algorithm is extended further, resulting in an algorithm with guaranteed convergence on global minima. A model for constructing cooperative PSO algorithms is developed, resulting in the introduction of two new PSO-based algorithms. Empirical results are presented to support the theoretical properties predicted by the various models, using synthetic benchmark functions to investigate specific properties. The various PSO-based algorithms are then applied to the task of training neural networks, corroborating the results obtained on the synthetic benchmark functions. / Thesis (PhD)--University of Pretoria, 2007. / Computer Science / Unrestricted

The performance of optimization algorithms is sensitive to both the optimization problem's numerical characteristics and the termination criteria of the algorithm. Given these considerations two tuning algorithms named tMOPSO and MOTA are proposed to assist optimization practitioners to nd algorithm settings which are approximate for the problem at hand. For a speci ed problem tMOPSO aims to determine multiple groups of control parameter values, each of which results in optimal performance at a di erent objective function evaluation budget. To achieve this, the control parameter tuning problem is formulated as a multi-objective optimization problem. Furthermore, tMOPSO uses a noise-handling strategy and control parameter value assessment procedure, which are specialized for tuning stochastic optimization algorithms. The principles upon which tMOPSO were designed are expanded into the context of many objective optimization, to create the MOTA tuning algorithm. MOTA tunes an optimization algorithm to multiple problems over a range of objective function evaluation budgets. To optimize the resulting many objective tuning problem, MOTA makes use of bi-objective decomposition. The last section of work entails an application of the tMOPSO and MOTA algorithms to benchmark optimization algorithms according to their tunability. Benchmarking via tunability is shown to be an effective approach for comparing optimization algorithms, where the various control parameter choices available to an optimization practitioner are included into the benchmarking process. / Thesis (PhD)--University of Pretoria, 2014 / gm2015 / Mechanical and Aeronautical Engineering / PhD / Unrestricted

The purpose of this thesis was to identify the optimal design parameters for a jet nozzle which obtains a local maximum shear stress while maximizing the average shear stress on the floor of a fluid filled system. This research examined how geometric parameters of a jet nozzle, such as the nozzle's angle, height, and orifice, influence the shear stress created on the bottom surface of a tank. Simulations were run using a Computational Fluid Dynamics (CFD) software package to determine shear stress values for a parameterized geometric domain including the jet nozzle. A response surface was created based on the shear stress values obtained from 112 simulated designs. A multi-objective optimization software utilized the response surface to generate designs with the best combination of parameters to achieve maximum shear stress and maximum average shear stress. The optimal configuration of parameters achieved larger shear stress values over a commercially available design.

Orientador: Renato Pavanello / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-27T18:05:36Z (GMT). No. of bitstreams: 1 QuispeRodriguez_Sergio_M.pdf: 51003475 bytes, checksum: 7e557fe0fe0448fd7cae415ebca527f8 (MD5) Previous issue date: 2015 / Resumo: A otimização estrutural topológica é uma ferramenta aplicada atualmente em muitos campos da engenharia tendo se consolidado no meio acadêmico e industrial. Em muitos casos práticos os carregamentos mecânicos e térmicos ocorrem simultaneamente nas estruturas. Nestas situações, a aplicação do método de otimização estrutural topológica deve contemplar tanto os requisitos mecânicos, como os requisitos térmicos. Assim, uma abordagem multi-física e multi-objetivo precisa ser desenvolvida para a solução desta classe de problemas. O presente trabalho é dedicado ao estudo da aplicação do método BESO (BESO - Bi-directional Evolutionary Structural Optimization) à sistemas multi-físicos considerando inicialmente os carregamentos termo-mecânicos como forças de corpo ou seja, forças dependentes do projeto. As funções objetivo consideradas são a flexibilidade média da estrutura e a capacidade térmica do sistema. A análise termo-mecânica é realizada usando o método de acoplamento sequencial, onde obtêm-se inicialmente a resposta do campo térmico, ou aplica-se um campo previamente conhecido do ponto da estrutura e na sequência calculam-se as forças térmicas geradas e a dilatação da estrutura. Explora-se também a otimização termo-mecânica multiobjetivo, em que duas funções objetivo são consideradas simultaneamente. Considera-se como o objetivo do problema de otimização, a minimização da flexibilidade média e a minimização da capacidade térmica, usando o método de soma ponderada. Para a validação dos procedimentos de otimização implementados neste trabalho, são apresentados exemplos de otimização para sistemas termo-mecânicos bidimensionais. A viabilidade do método para aplicação em problemas de engenharia e a comparação de resultados com outros métodos de otimização, permite afirmar que as técnicas propostas podem ser usadas na solução de problemas de otimização topológica de sistemas termo-mecânicos / Abstract: The structural topology optimization is an usefull tool applied in many engineering fields, having been established in the academic and industrial environments. In many practical cases, the mechanical and thermal loads occur simultaneously in a structure. In these cases, the aplication of structural topology optimization should consider the thermal and mechanical requirements. For this reason, a multi-physic and multi-objective approach needs to be developed for the solution of these types of problems. The present work is dedicated to the study of the BESO method (BESO - Bi-directional Evolutionary Structural Optimization) applied to multi-physic systems taking in consideration thermo-mechanical loads as design dependent body loads. The objective functions considered are the compliance and heat capacity of the system. The thermo-mechanical analysis is carried out using a sequential coupling method, where the thermal field response is obtained initially, and in the sequence, the thermal loads or dilation loads are calculated. The bi-objective thermo-mechanical optimization problem is also analysed, where two objective functions are considered simultaneously. To validate the procedures implemented in this work, some 2-D examples of thermo-mechancial systems optimization are presented. The feasibility of the method for the aplication in engineering problems and the comparison of the results obtained using other methods, alows to state that the proposed techniques can be used in the solution of optimization problems of thermo-mechanical systems / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

An unmanned aerial vehicle (UAV) swarm allows for a more time-efficient method of searching a specified area than a single UAV or piloted plane. There are a variety of factors that affect how well an area is surveyed. We specifically analyzed the effect both vehicle properties and communication had on the swarm search performance. We used non-dimensionalization so the results can be applied to any domain size with any type of vehicle. We found that endurance was the most important factor. Vehicles with good endurance sensed approximately 90% to 100% of the grid, even when other properties were lacking. If the vehicles lacked endurance, the amount of area the vehicles could sense at a given time step became more important and 10% more of the grid was sensed with the increase in sensed area. The maneuverability of the vehicles was measured as the vehicles' radii of turn compared to the search domain size. The maneuverability mattered the most in the middle-range endurance cases. In some cases 30% more of the grid was searched with improving vehicle maneuverability. In addition, we also examined four communication cases with different amounts of information regarding vehicle location. We found communication increased search performance by at least 6.3%. However, increasing the amount of information only changed the performance by 2.3%. We also studied the impact the range of vehicle communication had on search performance. We found that simulations benefited most from increasing the communication range when the amount of area sensed at a given time step was small and the vehicles had good maneuverability. We also extended the optimization to a multi-objective process with the inclusion of target tracking. We analyzed how the different weightings of the objectives affected the performance outcomes. We found that target tracking performance dramatically changes based on the given weighting of each objective and saw an increase of approximately 52%. However, the amount of the grid that was sensed only dropped by approximately 10%.

Wind farm layout optimization can greatly improve wind farm performance. However, past wind farm design has been limited in several ways. Wind farm design usually assumes that all the turbines throughout the farm should be exactly the same. Oftentimes, the location of every turbine is optimized individually, which is computationally expensive. Furthermore, designers fail to consider turbine loads during layout optimization. This dissertation presents four studies which provide partial solutions to these limitations and greatly improve wind farm layout optimization. Two studies explore differing turbine designs in wind farms. In these studies, Wind farm layouts are optimized simultaneously with turbine design. We found that for small rotor diameters and closely spaced wind turbines, wind farms with different heights have a 5–10% reduction in cost of energy compared to farms with all the same turbine height. Coupled optimization of turbine layout and full turbine design results in an 2–5% reduction in cost of energy compared to optimizing sequentially for wind farms with turbine spacings of 8.5–11 rotor diameters. Wind farms with tighter spacing benefit even more from coupled optimization. Furthermore, we found that heterogeneous turbine design can produce up to an additional 10% cost of energy reduction compared to wind farms with identical turbines throughout the farm, especially when the wind turbines are closely spaced. The third study presents the boundary-grid parameterization method to reduce the computational expense of optimizing wind farms. This parameterization uses only five variables to define the layout of a wind farm with any number of turbines. For a 100 turbine wind farm, we show that optimizing the five variables of the boundary-grid method produces wind farms that perform just as well as farms where the location of each turbine is optimized individually, which requires 200 design variables. The presented method facilitates the study for both gradient-free and gradient-based optimization of large wind farms. The final study presents a model to calculate fatigue damage caused by partial waking on a wind turbine which is computationally efficient and can be included in wind farm layout optimization. Compared to high fidelity simulation data, the model accurately predicts the damage trends of various waking conditions. We also perform a wind farm layout optimization with the presented model in which we maximize the annual energy production of a wind farm while constraining the damage of each turbine. The results of the optimization show that the turbine damage can be constrained with only a very small sacrifice of less than 1% to the annual energy production.

An airfoil shape optimization problem with conflicting objectives is handled using two different multi-objective approaches. These are an a priori scalarization approach where the conflicting objectives are assigned weights and summed together to form a single objective, and the Pareto-optimal multi-objective approach. The optimization formulations for both approaches contain challenging numerical characteristics which include noise, multi-modality and undefined regions. Gradient-, surrogate- and population-based single objective optimization methods are applied to the `a priori' formulations. The gradient methods are modified to improve their performance on noisy problems as well as to handle undefined regions in the design space. The modifications are successful but the modified methods are outperformed by the surrogate methods and population based methods. Population-based techniques are used for the Pareto-optimal multi-objective approach. Two established optimization algorithms and two custom algorithms are implemented. The custom algorithms use fitted unrotated hyper ellipses and linear aggregating functions to search the design space for non-dominated designs. Various multi-objective formulations are posed to investigate different aspects of the airfoil design problem. The non-dominated designs found by the Pareto-optimal multi-objective optimization algorithms are then presented. / Dissertation (MEng)--University of Pretoria, 2011. / Mechanical and Aeronautical Engineering / unrestricted