Algoritmos genéticos: uso de lógica nebulosa e análise de convergência por cadeia de Markov

In this work, the Markov chain will be the tool used in the modeling and analysis of convergence of the genetic algorithm, both the standard version as for the other versions that allows the genetic algorithm. In addition, we intend to compare the performance of the standard version with the fuzzy version, believing that this version gives the genetic algorithm a great ability to find a global optimum, own the global optimization algorithms. The choice of this algorithm is due to the fact that it has become, over the past thirty yares, one of the more importan tool used to find a solution of de optimization problem. This choice is due to its effectiveness in finding a good quality solution to the problem, considering that the knowledge of a good quality solution becomes acceptable given that there may not be another algorithm able to get the optimal solution for many of these problems. However, this algorithm can be set, taking into account, that it is not only dependent on how the problem is represented as but also some of the operators are defined, to the standard version of this, when the parameters are kept fixed, to their versions with variables parameters. Therefore to achieve good performance with the aforementioned algorithm is necessary that it has an adequate criterion in the choice of its parameters, especially the rate of mutation and crossover rate or even the size of the population. It is important to remember that those implementations in which parameters are kept fixed throughout the execution, the modeling algorithm by Markov chain results in a homogeneous chain and when it allows the variation of parameters during the execution, the Markov chain that models becomes be non - homogeneous. Therefore, in an attempt to improve the algorithm performance, few studies have tried to make the setting of the parameters through strategies that capture the intrinsic characteristics of the problem. These characteristics are extracted from the present state of execution, in order to identify and preserve a pattern related to a solution of good quality and at the same time that standard discarding of low quality. Strategies for feature extraction can either use precise techniques as fuzzy techniques, in the latter case being made through a fuzzy controller. A Markov chain is used for modeling and convergence analysis of the algorithm, both in its standard version as for the other. In order to evaluate the performance of a non-homogeneous algorithm tests will be applied to compare the standard fuzzy algorithm with the genetic algorithm, and the rate of change adjusted by a fuzzy controller. To do so, pick up optimization problems whose number of solutions varies exponentially with the number of variables

Identificação e comparação entre controle preditivo com modelo não linear e PI sintonizados com PSO em sistema de separação gravitacional de águia-óleo

The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

Fundamentos de otimização por inteligência de enxames: uma visão geral

Este artigo apresenta uma breve revisão de alguns dos mais recentes métodos bioinspirados baseados no comportamento de populações para o desenvolvimento de técnicas de solução de problemas. As metaheurísticas tratadas aqui correspondem às estratégias de otimização por colônia de formigas, otimização por enxame de partículas, algoritmo shuffled frog-leaping, coleta de alimentos por bactérias e colônia de abelhas. Os princípios biológicos que motivaram o desenvolvimento de cada uma dessas estratégias, assim como seus respectivos algoritmos computacionais, são introduzidos. Duas aplicações diferentes foram conduzidas para exemplificar o desempenho de tais algoritmos. A finalidade é enfatizar perspectivas de aplicação destas abordagens em diferentes problemas da área de engenharia.

H-2 and H-infinity - norm control of intelligent structures using LMI techniques

Linear Matrix Inequalities (LMIs) is a powerful too] that has been used in many areas ranging from control engineering to system identification and structural design. There are many factors that make LMI appealing. One is the fact that a lot of design specifications and constrains can be formulated as LMIs [1]. Once formulated in terms of LMIs a problem can be solved efficiently by convex optimization algorithms. The basic idea of the LMI method is to formulate a given problem as an optimization problem with linear objective function and linear matrix inequalities constrains. An intelligent structure involves distributed sensors and actuators and a control law to apply localized actions, in order to minimize or reduce the response at selected conditions. The objective of this work is to implement techniques of control based on LMIs applied to smart structures.

Uma metodologia para o controle de seguranca dinamica de sistemas de energia eletrica

This work presents an algorithm for the security control of electric power systems using control actions like generation reallocation, determined by sensitivity analysis (linearized model) and optimization by neural networks. The model is developed taking into account the dynamic network aspects. The preventive control methodology is developed by means of sensitivity analysis of the security margin related with the mechanical power of the system synchronous machines. The reallocation power in each machine is determined using neural networks. The neural network used in this work is of Hopfield type. These networks are dedicated electric circuits which simulate the constraint set and the objective function of an optimization problem. The advantage of using these networks is the higher speed in getting the solutions when compared to conventional optimization algorithms due to the great convergence rate of the process and the facility of the method parallelization. Then, the objectives are: formulate and investigate these networks implementations in determining. The generation reallocation in digital computers. Aiming to illustrate the proposed methodology an application considering a multi-machine system is presented.

Active control in flexible plates with piezoelectric actuators using linear matrix inequalities

The study of algorithms for active vibrations control in flexible structures became an area of enormous interest, mainly due to the countless demands of an optimal performance of mechanical systems as aircraft, aerospace and automotive structures. Smart structures, formed by a structure base, coupled with piezoelectric actuators and sensor are capable to guarantee the conditions demanded through the application of several types of controllers. The actuator/sensor materials are composed by piezoelectric ceramic (PZT - Lead Zirconate Titanate), commonly used as distributed actuators, and piezoelectric plastic films (PVDF-PolyVinyliDeno Floride), highly indicated for distributed sensors. The design process of such system encompasses three main phases: structural design; optimal placement of sensor/actuator (PVDF and PZT); and controller design. Consequently, for optimal design purposes, the structure, the sensor/actuator placement and the controller have to be considered simultaneously. This article addresses the optimal placement of actuators and sensors for design of controller for vibration attenuation in a flexible plate. Techniques involving linear matrix inequalities (LMI) to solve the Riccati's equation are used. The controller's gain is calculated using the linear quadratic regulator (LQR). The major advantage of LMI design is to enable specifications such as stability degree requirements, decay rate, input force limitation in the actuators and output peak bounder. It is also possible to assume that the model parameters involve uncertainties. LMI is a very useful tool for problems with constraints, where the parameters vary in a range of values. Once formulated in terms of LMI a problem can be solved efficiently by convex optimization algorithms.

A hybrid image restoration algorithm based on Projections onto Convex Sets and Harmony Search

Image restoration is a research field that attempts to recover a blurred and noisy image. Since it can be modeled as a linear system, we propose in this paper to use the meta-heuristics optimization algorithm Harmony Search (HS) to find out near-optimal solutions in a Projections Onto Convex Sets-based formulation to solve this problem. The experiments using HS and four of its variants have shown that we can obtain near-optimal and faster restored images than other evolutionary optimization approach. © 2013 IEEE.

Método de detecção interferométrica de fase, com baixa profundidade de modulação, aplicado à medição de deslocamentos nanométricos em atuadores e mini-manipuladores piezoelétricos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Uma investigação da influência do parâmetro de barreira na solução do problema de fluxo de potência ótimo

Pós-graduação em Engenharia Elétrica - FEB

Abordagens de sistemas inteligentes para a solução do problema de despacho econômico de geração

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Heurísticas especializadas aplicadas ao problema de carregamento de contêiner

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Métodos de otimização global aplicados na busca dos parâmetros SRC

O método de empilhamento por Superfície de Reflexão Comum (SRC) produz seções simuladas de afastamento nulo (AN) por meio do somatório de eventos sísmicos dos dados de cobertura múltipla contidos nas superfícies de empilhamento. Este método não depende do modelo de velocidade do meio, apenas requer o conhecimento a priori da velocidade próxima a superfície. A simulação de seções AN por este método de empilhamento utiliza uma aproximação hiperbólica de segunda ordem do tempo de trânsito de raios paraxiais para definir a superfície de empilhamento ou operador de empilhamento SRC. Para meios 2D este operador depende de três atributos cinemáticos de duas ondas hipotéticas (ondas PIN e N), observados no ponto de emergência do raio central com incidência normal, que são: o ângulo de emergência do raio central com fonte-receptor nulo (β₀) , o raio de curvatura da onda ponto de incidência normal (R_PIN) e o raio de curvatura da onda normal (R_N). Portanto, o problema de otimização no método SRC consiste na determinação, a partir dos dados sísmicos, dos três parâmetros (β₀, R_PIN, R_N) ótimos associados a cada ponto de amostragem da seção AN a ser simulada. A determinação simultânea destes parâmetros pode ser realizada por meio de processos de busca global (ou otimização global) multidimensional, utilizando como função objetivo algum critério de coerência. O problema de otimização no método SRC é muito importante para o bom desempenho no que diz respeito a qualidade dos resultados e principalmente ao custo computacional, comparado com os métodos tradicionalmente utilizados na indústria sísmica. Existem várias estratégias de busca para determinar estes parâmetros baseados em buscas sistemáticas e usando algoritmos de otimização, podendo estimar apenas um parâmetro de cada vez, ou dois ou os três parâmetros simultaneamente. Levando em conta a estratégia de busca por meio da aplicação de otimização global, estes três parâmetros podem ser estimados através de dois procedimentos: no primeiro caso os três parâmetros podem ser estimados simultaneamente e no segundo caso inicialmente podem ser determinados simultaneamente dois parâmetros (β₀, R_PIN) e posteriormente o terceiro parâmetro (R_N) usando os valores dos dois parâmetros já conhecidos. Neste trabalho apresenta-se a aplicação e comparação de quatro algoritmos de otimização global para encontrar os parâmetros SRC ótimos, estes são: Simulated Annealing (SA), Very Fast Simulated Annealing (VFSA), Differential Evolution (DE) e Controlled Rando Search - 2 (CRS2). Como resultados importantes são apresentados a aplicação de cada método de otimização e a comparação entre os métodos quanto a eficácia, eficiência e confiabilidade para determinar os melhores parâmetros SRC. Posteriormente, aplicando as estratégias de busca global para a determinação destes parâmetros, por meio do método de otimização VFSA que teve o melhor desempenho foi realizado o empilhamento SRC a partir dos dados Marmousi, isto é, foi realizado um empilhamento SRC usando dois parâmetros (β₀, R_PIN) estimados por busca global e outro empilhamento SRC usando os três parâmetros (β₀, R_PIN, R_N) também estimados por busca global.

Aeroelastic stability analysis using linear matrix inequalities

The present work describes an alternative methodology for identification of aeroelastic stability in a range of varying parameters. Analysis is performed in time domain based on Lyapunov stability and solved by convex optimization algorithms. The theory is outlined and simulations are carried out on a benchmark system to illustrate the method. The classical methodology with the analysis of the system's eigenvalues is presented for comparing the results and validating the approach. The aeroelastic model is represented in state space format and the unsteady aerodynamic forces are written in time domain using rational function approximation. The problem is formulated as a polytopic differential inclusion system and the conceptual idea can be used in two different applications. In the first application the method verifies the aeroelastic stability in a range of air density (or its equivalent altitude range). In the second one, the stability is verified for a rage of velocities. These analyses are in contrast to the classical discrete analysis performed at fixed air density/velocity values. It is shown that this method is efficient to identify stability regions in the flight envelope and it offers promise for robust flutter identification.

Estudo dos problemas de corte e empacotamento

Pós-graduação em Engenharia Elétrica - FEIS

Desenvolvimento de solução para o problema de alocação de navios em berços de terminais portuários: aplicação de algoritmos genéticos e otimização por nuvens de partículas

This work proposes two optimization algorithms for the solution of the Berth Allocation Problem (PAB). Due to the economic development of the country, it became necessary for the improvement of means of transport, which mainly shipping. For this, you need a better system management port, you will receive a lot of ships carrying cargo. In this work the PAB is approached so that the goals are to reduce costs and time handling in ports. For this, we applied two computational techniques, genetic algorithms and optimization for cloud particles, to obtain the best results for this problem. The results obtained with each type of algorithm are compared to conclude which method is more efficient for the port system