Métodos híbridos de pontos interiores/exteriores e de aproximantes de funções em problemas multiobjetivo de despacho econômico e ambiental

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Design equations for reinforced concrete members strengthened in shear with external FRP reinforcement formulated in an evolutionary multi-objective framework

Methods for predicting the shear capacity of FRP shear strengthened RC beams assume the traditional approach of superimposing the contribution of the FRP reinforcing to the contributions from the reinforcing steel and the concrete. These methods become the basis for most guides for the design of externally bonded FRP systems for strengthening concrete structures. The variations among them come from the way they account for the effect of basic shear design parameters on shear capacity. This paper presents a simple method for defining improved equations to calculate the shear capacity of reinforced concrete beams externally shear strengthened with FRP. For the first time, the equations are obtained in a multiobjective optimization framework solved by using genetic algorithms, resulting from considering simultaneously the experimental results of beams with and without FRP external reinforcement. The performance of the new proposed equations is compared to the predictions with some of the current shear design guidelines for strengthening concrete structures using FRPs. The proposed procedure is also reformulated as a constrained optimization problem to provide more conservative shear predictions.

Influencia de las restricciones medioambientales en el sector eléctrico

El objetivo de esta tesis es la caracterización de la generación térmica representativa de la existente en la realidad, para posteriormente proceder a su modelización y simulación integrándolas en una red eléctrica tipo y llevar a cabo estudios de optimización multiobjetivo económico medioambiental. Para ello, en primera instancia se analiza el contexto energético y eléctrico actual, y más concretamente el peninsular, en el que habiendo desaparecido las centrales de fuelóleo, sólo quedan ciclos combinados y centrales de carbón de distinto rango. Seguidamente se lleva a cabo un análisis de los principales impactos medioambientales de las centrales eléctricas basadas en combustión, representados sobre todo por sus emisiones de CO2, SO2 y NOx, de las medidas de control y mitigación de las mismas y de la normativa que les aplica. A continuación, a partir de las características de los combustibles y de la información de los consumos específicos, se caracterizan los grupos térmicos frente a las funciones relevantes que definen su comportamiento energético, económico y medioambiental, en términos de funciones de salida horarias dependiendo de la carga. Se tiene en cuenta la posibilidad de desnitrificación y desulfuración. Dado que las funciones objetivo son múltiples, y que están en conflicto unas con otras, se ha optado por usar métodos multiobjetivo que son capaces de identificar el contorno de puntos óptimos o frente de Pareto, en los que tomando una solución no existe otra que lo mejore en alguna de las funciones objetivo sin empeorarlo en otra. Se analizaron varios métodos de optimización multiobjetivo y se seleccionó el de las ε constraint, capaz de encontrar frentes no convexos y cuya optimalidad estricta se puede comprobar. Se integró una representación equilibrada de centrales de antracita, hulla nacional e importada, lignito y ciclos combinados en la red tipo IEEE-57, en la que se puede trabajar con siete centrales sin distorsionar demasiado las potencias nominales reales de los grupos, y se programó en Matlab la resolución de flujos óptimos de carga en alterna con el método multiobjetivo integrado. Se identifican los frentes de Pareto de las combinaciones de coste y cada uno de los tres tipos de emisión, y también el de los cuatro objetivos juntos, obteniendo los resultados de costes óptimos del sistema para todo el rango de emisiones. Se valora cuánto le cuesta al sistema reducir una tonelada adicional de cualquier tipo de emisión a base de desplazarse a combinaciones de generación más limpias. Los puntos encontrados aseguran que bajo unas determinadas emisiones no pueden ser mejorados económicamente, o que atendiendo a ese coste no se puede reducir más allá el sistema en lo relativo a emisiones. También se indica cómo usar los frentes de Pareto para trazar estrategias óptimas de producción ante cambios horarios de carga. ABSTRACT The aim of this thesis is the characterization of electrical generation based on combustion processes representative of the actual power plants, for the latter modelling and simulation of an electrical grid and the development of economic- environmental multiobjective optimization studies. In this line, the first step taken is the analysis of the current energetic and electrical framework, focused on the peninsular one, where the fuel power plants have been shut down, and the only ones remaining are coal units of different types and combined cycle. Then it is carried out an analysis of the main environmental impacts of the thermal power plants, represented basically by the emissions of CO2, SO2 y NOx, their control and reduction measures and the applicable regulations. Next, based on the combustibles properties and the information about the units heat rates, the different power plants are characterized in relation to the outstanding functions that define their energy, economic and environmental behaviour, in terms of hourly output functions depending on their load. Optional denitrification and desulfurization is considered. Given that there are multiple objectives, and that they go in conflictive directions, it has been decided the use of multiobjective techniques, that have the ability of identifying the optimal points set, which is called the Pareto front, where taken a solution there will be no other point that can beat the former in an objective without worsening it in another objective. Several multiobjective optimization methods were analysed and pondered, selecting the ε constraint technique, which is able to find no convex fronts and it is opened to be tested to prove the strict Pareto optimality of the obtained solutions. A balanced representation of the thermal power plants, formed by anthracite, lignite, bituminous national and imported coals and combined cycle, was integrated in the IEEE-57 network case. This system was selected because it deals with a total power that will admit seven units without distorting significantly the actual size of the power plants. Next, an AC optimal power flow with the multiobjective method implemented in the routines was programmed. The Pareto fronts of the combination of operative costs with each of the three emissions functions were found, and also the front of all of them together. The optimal production costs of the system for all the emissions range were obtained. It is also evaluated the cost of reducing an additional emission ton of any of the emissions when the optimal production mix is displaced towards cleaner points. The obtained solutions assure that under a determined level of emissions they cannot be improved economically or, in the other way, at a determined cost it cannot be found points of lesser emissions. The Pareto fronts are also applied for the search of optimal strategic paths to follow the hourly load changes.

Desenvolvimento de estratégias de otimização contínua e discreta para problemas de fluxo de potência ótimo

O objetivo do presente trabalho é a investigação e o desenvolvimento de estratégias de otimização contínua e discreta para problemas de Fluxo de Potência Ótimo (FPO), onde existe a necessidade de se considerar as variáveis de controle associadas aos taps de transformadores em-fase e chaveamentos de bancos de capacitores e reatores shunt como variáveis discretas e existe a necessidade da limitação, e/ou até mesmo a minimização do número de ações de controle. Neste trabalho, o problema de FPO será abordado por meio de três estratégias. Na primeira proposta, o problema de FPO é modelado como um problema de Programação Não Linear com Variáveis Contínuas e Discretas (PNLCD) para a minimização de perdas ativas na transmissão; são propostas três abordagens utilizando funções de discretização para o tratamento das variáveis discretas. Na segunda proposta, considera-se que o problema de FPO, com os taps de transformadores discretos e bancos de capacitores e reatores shunts fixos, possui uma limitação no número de ações de controles; variáveis binárias associadas ao número de ações de controles são tratadas por uma função quadrática. Na terceira proposta, o problema de FPO é modelado como um problema de Otimização Multiobjetivo. O método da soma ponderada e o método ε-restrito são utilizados para modificar os problemas multiobjetivos propostos em problemas mono-objetivos. As variáveis binárias associadas às ações de controles são tratadas por duas funções, uma sigmoidal e uma polinomial. Para verificar a eficácia e a robustez dos modelos e algoritmos desenvolvidos serão realizados testes com os sistemas elétricos IEEE de 14, 30, 57, 118 e 300 barras. Todos os algoritmos e modelos foram implementados em General Algebraic Modeling System (GAMS) e os solvers CONOPT, IPOPT, KNITRO e DICOPT foram utilizados na resolução dos problemas. Os resultados obtidos confirmam que as estratégias de discretização são eficientes e as propostas de modelagem para variáveis binárias permitem encontrar soluções factíveis para os problemas envolvendo as ações de controles enquanto os solvers DICOPT e KNITRO utilizados para modelar variáveis binárias não encontram soluções.

Simultaneous environmental and economic process synthesis of Isobutane Alkylation

This multidisciplinary study concerns the optimal design of processes with a view to both maximizing profit and minimizing environmental impacts. This can be achieved by a combination of traditional chemical process design methods, measurements of environmental impacts and advanced mathematical optimization techniques. More to the point, this paper presents a hybrid simulation-multiobjective optimization approach that at once optimizes the production cost and minimizes the associated environmental impacts of isobutane alkylation. This approach has also made it possible to obtain the flowsheet configurations and process variables that are needed to manufacture isooctane in a way that satisfies the above-stated double aim. The problem is formulated as a Generalized Disjunctive Programming problem and solved using state-of-the-art logic-based algorithms. It is shown, starting from existing alternatives for the process, that it is possible to systematically generate a superstructure that includes alternatives not previously considered. The optimal solution, in the form a Pareto curve, includes different structural alternatives from which the most suitable design can be selected. To evaluate the environmental impact, Life Cycle Assessment based on two different indicators is employed: Ecoindicator 99 and Global Warming Potential.

Projeto iterativo, simulação, análise e otimização de máquina a relutância variável monofásica

Variable reluctance motors have been increasingly used as an alternative for variable speed and high speed drives in many industrial applications, due to many advantages like the simplicity of construction, robustness, and low cost. The most common applications in recent years are related to aeronautics, electric and hybrid vehicles and wind power generation. This paper explores the theory, operation, design procedures and analysis of a variable reluctance machine. An iterative design methodology is introduced and used to design a 1.25 kW prototype. For the analysis of the machine two methods are used, an analytical method and the finite element simulation. The results obtained by both methods are compared. The results of finite element simulation are used to determine the inductance profiles and torque of the prototype. The magnetic saturation is examined visually and numerically in four critical points of the machine. The data collected in the simulation allow the verification of design and operating limits for the prototype. Moreover, the behavior of the output quantities is analyzed (inductance, torque and magnetic saturation) by variation of physical dimensions of the motor. Finally, a multiobjective optimization using Differential Evolution algorithms and Genetic Algorithms for switched reluctance machine design is proposed. The optimized variables are rotor and stator polar arcs, and the goals are to maximize the average torque, the average torque per copper losses and the average torque per core volume. Finally, the initial design and optimized design are compared.

Predictive Analytics and Decision Support for Heart Failure patients

Thesis (Master's)--University of Washington, 2016-08

Optimisation of digitally coded test vectors for mixed-signal components

International audience

Design for uncertainty of aerospace structures

The aim of this work is to present a general overview of state-of-the-art related to design for uncertainty with a focus on aerospace structures. In particular, a simulation on a FCCZ lattice cell and on the profile shape of a nozzle will be performed. Optimization under uncertainty is characterized by the need to make decisions without complete knowledge of the problem data. When dealing with a complex problem, non-linearity, or optimization, two main issues are raised: the uncertainty of the feasibility of the solution and the uncertainty of the objective value of the function. In the first part, the Design Of Experiments (DOE) methodologies, Uncertainty Quantification (UQ), and then Uncertainty optimization will be deepened. The second part will show an application of the previous theories on through a commercial software. Nowadays multiobjective optimization on high non-linear problem can be a powerful tool to approach new concept solutions or to develop cutting-edge design. In this thesis an effective improvement have been reached on a rocket nozzle. Future work could include the introduction of multi scale modelling, multiphysics approach and every strategy useful to simulate as much possible real operative condition of the studied design.

EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization.

In recent years, protein-ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 A around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 A root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the alphaVbeta3 integrin, starting far away from the binding pocket.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.

Multiobjective Biogeography-Based Optimization Based on Predator-Prey Approach

Biogeography is the science that studies the geographical distribution and the migration of species in an ecosystem. Biogeography-based optimization (BBO) is a recently developed global optimization algorithm as a generalization of biogeography to evolutionary algorithm and has shown its ability to solve complex optimization problems. BBO employs a migration operator to share information between the problem solutions. The problem solutions are identified as habitat, and the sharing of features is called migration. In this paper, a multiobjective BBO, combined with a predator-prey (PPBBO) approach, is proposed and validated in the constrained design of a brushless dc wheel motor. The results demonstrated that the proposed PPBBO approach converged to promising solutions in terms of quality and dominance when compared with the classical BBO in a multiobjective version.

RISK-BASED MULTIOBJECTIVE PATH PLANNING AND DESIGN OPTIMIZATION FOR UNMANNED AERIAL VEHICLES

Safe operation of unmanned aerial vehicles (UAVs) over populated areas requires reducing the risk posed by a UAV if it crashed during its operation. We considered several types of UAV risk-based path planning problems and developed techniques for estimating the risk to third parties on the ground. The path planning problem requires making trade-offs between risk and flight time. Four optimization approaches for solving the problem were tested; a network-based approach that used a greedy algorithm to improve the original solution generated the best solutions with the least computational effort. Additionally, an approach for solving a combined design and path planning problems was developed and tested. This approach was extended to solve robust risk-based path planning problem in which uncertainty about wind conditions would affect the risk posed by a UAV.

Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration

The power loss reduction in distribution systems (DSs) is a nonlinear and multiobjective problem. Service restoration in DSs is even computationally hard since it additionally requires a solution in real-time. Both DS problems are computationally complex. For large-scale networks, the usual problem formulation has thousands of constraint equations. The node-depth encoding (NDE) enables a modeling of DSs problems that eliminates several constraint equations from the usual formulation, making the problem solution simpler. On the other hand, a multiobjective evolutionary algorithm (EA) based on subpopulation tables adequately models several objectives and constraints, enabling a better exploration of the search space. The combination of the multiobjective EA with NDE (MEAN) results in the proposed approach for solving DSs problems for large-scale networks. Simulation results have shown the MEAN is able to find adequate restoration plans for a real DS with 3860 buses and 632 switches in a running time of 0.68 s. Moreover, the MEAN has shown a sublinear running time in function of the system size. Tests with networks ranging from 632 to 5166 switches indicate that the MEAN can find network configurations corresponding to a power loss reduction of 27.64% for very large networks requiring relatively low running time.