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Actuator effect of a piezoelectric anisotropic plate model

Costa, Lino; Oliveira, Pedro; Figueiredo, Isabel N.; Leal, Rogério
Fonte: Centro de Matemática da Universidade de Coimbra Publicador: Centro de Matemática da Universidade de Coimbra
Tipo: Pré-impressão
Português
Relevância na Pesquisa
45.98%
This paper addresses the actuator e ect of a piezoelectric anisotropic plate model, depending on the location of the applied electric potentials, and for di erent clamped boundary conditions. It corresponds to integer optimization problems, whose objective functions involve the displacement of the plate. We adopt the two-dimensional piezoelectric anisotropic nonhomogeneous plate model derived in Figueiredo and Leal [1]. This model is rst discretised by the nite element method. Then, we describe the associated integer optimization problems, which aim to nd the maximum displacement of the plate, as a function of the location of the applied electric potentials. In this sense, we also introduce a related multi-objective optimization problem, that is solved through genetic algorithms. Several numerical examples are reported. For all the tests, the sti ness matrices and force vectors have been evaluated with the subroutines planre and platre, of the CALFEM toolbox of MATLAB [2], and, the genetic algorithms have been implemented in C++.

Distribuição de pressão em rede de irrigação localizada otimizada por algoritmos genéticos; Pressure distribution in a low-pressure optimized irrigation network using genetic algorithms

MARCUZZO, Francisco F. N.; WENDLAND, Edson
Fonte: Associação Brasileira de Engenharia Agrícola Publicador: Associação Brasileira de Engenharia Agrícola
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
45.98%
Modelos matemáticos computacionais de otimização de redes de irrigação, sob vazão em marcha, capazes de fornecer dados hidráulicos, são importantes para a verificação do comportamento do sistema quanto à distribuição da carga hidráulica (energia) e da pressão nas tubulações da rede. Este trabalho teve como objetivo estudar a distribuição da carga efetiva e hidráulica da unidade operacional de uma rede de irrigação localizada otimizada por algoritmos genéticos. As variáveis de decisão para otimização, com auxílio de algoritmos genéticos, foram os diâmetros de cada trecho da rede: dois para linhas laterais, quatro para linhas de derivação, quatro para linhas secundárias e um para a linha principal. Foi desenvolvido um código em linguagem MatLab, considerando todas as perdas de energia distribuídas e localizadas entre o início da rede e o conjunto motobomba. A análise de sensibilidade realizada foi baseada na variação, na declividade do terreno (0; 2,5 e 5%). Os resultados mostram que, para as tubulações com vazão em marcha, quando se aumenta a declividade do terreno, ocorre ganho de energia no início da tubulação, que vai perdendo-se de maneira gradual, e diminuição da pressão no início da tubulação...

Análise de desempenho de algoritmos evolutivos no domínio do futebol de robôs; Performance analysis of evolutionary algorithms in the robot soccer domain

Fraccaroli, Eduardo Sacogne
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 01/09/2010 Português
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Muitos problemas de otimização em ambientes multiagentes utilizam os algoritmos evolutivos para encontrar as melhores soluções. Uma das abordagens mais utilizadas consiste na aplicação de um algoritmo genético, como alternativa aos métodos tradicionais, para definir as ações dos jogadores em um time de futebol de robôs. Entretanto, conforme relatado na literatura, há inúmeras possibilidades e formas de se aplicar um algoritmo genético no domínio do futebol de robôs. Assim sendo, neste trabalho buscou-se realizar uma análise comparativa dos algoritmos genéticos mono-objetivo e multi-objetivo aplicados no domínio do futebol de robôs. O problema padrão escolhido para realizar essa análise foi de desenvolver uma estratégia de controle autônomo, a fim de capacitar que os robôs tomem decisões sem interferência externa, pois, além de sua solução se encontrar ainda em aberto, o mesmo é também de suma relevância para a área de robótica.; Many optimization problems in multiagent environments adapt evolutionary algorithms to find the best solutions. A widely used approach consists of applying a genetic algorithm as an alternative to traditional methods, in order to define the actions of the players on a soccer team of simulated robots. However...

Desenvolvimento de algoritmos genéticos para consultas por similaridade em domínios métricos; Genetic algorithms for similarity queries in metric spaces

Bueno, Renato
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 30/05/2005 Português
Relevância na Pesquisa
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O custo do acesso exato a dados complexos tende a ser muito alto, do ponto de vista da carga de processamento computacional. Além disso, a operação de busca em dados multimídia não é efetuada realmente sobre os dados originais, mas sobre características extraídas desses dados, as quais os descrevem. Por exemplo, na busca por imagens similares utilizando-se histogramas de cor, realizando uma consulta exata, o que se obtém são as imagens cujos histogramas são exatamente os mais similares aos da imagem referenciada 11a consulta, mas isso não implica necessariamente que se obtenha as imagens que atendam exatamente a consulta efetuada, pois as imagens recuperadas podem ser muito diferentes quanto a forma, por exemplo. Portanto, em muitas aplicações que acessam dados complexos, a recuperação exata deixa de um requisito fundamental, podendo a exatidão das respostas ser trocada por um melhor desempenho Neste trabalho foram desenvolvidos algoritmos para recuperação aproximada do conjunto-resposta de consultas por similaridade em domínios métricos utilizando algoritmos genéticos. Neste trabalho, com a utilização de algoritmos genéticos, foram desenvolvidas técnicas de recuperação aproximada de dados cm domínio métrico...

Determinação de um cronograma de manutenção preventiva utilizando algoritmos genéticos em equipamento de beneficiamento de aços planos

Flores, Daniel Ferreira
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Trabalho de Conclusão de Curso Formato: application/pdf
Português
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Em estudos de manutenção centrada na confiabilidade o uso de pesquisa operacional via algoritmos genéticos em problemas de otimização é relatado em abundância na literatura. O presente artigo traz adaptações no método proposto por Tsai et al. (2001) e sua integração a algoritmos genéticos na otimização de agenda de manutenções preventivas em uma máquina de beneficiamento de aços planos. Os resultados numéricos mostram a eficiência do método com base em algoritmos genéticos por conta de sua rápida convergência na resolução de problemas de manutenção. O cronograma de manutenção gerado pelo método encontrou sustentação na avaliação de especialistas de processo da empresa.; In reliability centered maintenance studies the use of optimization relying on Genetic Algorithms is widely available in current literature. This paper proposes modifications on the method suggested Tsai et al. (2001) and integrates it to genetic algorithms aimed at optimizing the preventive maintenance schedule in a steel processing mill. The Genetic Algorithm performed efficiently in term of its fast convergence. The suggested maintenance schedule was approved after experts’ assessment.

Short-term planning of electric power distribution networks using multiobjective genetic algorithim

Pereira Jr., Benvindo; Cossi, Antonio; Mantovani, José Roberto
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 28-35
Português
Relevância na Pesquisa
46%
The high active and reactive power level demanded by the distribution systems, the growth of consuming centers, and the long lines of the distribution systems result in voltage variations in the busses compromising the quality of energy supplied. To ensure the energy quality supplied in the distribution system short-term planning, some devices and actions are used to implement an effective control of voltage, reactive power, and power factor of the network. Among these devices and actions are the voltage regulators (VRs) and capacitor banks (CBs), as well as exchanging the conductors sizes of distribution lines. This paper presents a methodology based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II) for optimized allocation of VRs, CBs, and exchange of conductors in radial distribution systems. The Multiobjective Genetic Algorithm (MGA) is aided by an inference process developed using fuzzy logic, which applies specialized knowledge to achieve the reduction of the search space for the allocation of CBs and VRs.

Simulação e otimização de reator de formaldeido, processo prata, usando tecnicas de inteligencia artificial; Simulation and optimization of silver formaldehyde reactor, using artificial intelligence techniques

Antonio Carlos Papes Filho
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 27/07/2007 Português
Relevância na Pesquisa
45.99%
Essa tese encontra-se focada na simulação e otimização de um reator de leito fixo com catalisador de prata para oxidação do metanol a formaldeído, utilizando técnicas de inteligência artificial (algoritmo genético e redes neurais artificiais). O formaldeído é um importante intermediário químico utilizado principalmente na produção de adesivos ou resinas empregados em vários segmentos de mercado. A sua produção pelo processo ?Prata? encontra-se em desvantagem frente às novas plantas construídas com o moderno processo ?Formox?, catalisado por óxido de ferro-molibdênio, por apresentar menor desempenho e possuir poucas ferramentas que permitam melhorar o processo. Poucos modelos cinéticos são encontrados na literatura para o processo Prata, inadequados para a simulação do reator sob condições industriais. Há falta de dados cinéticos de qualidade para o desenvolvimento de equações da taxa e existem dificuldades experimentais em obtê-los sob condições industrialmente relevantes. Um simulador híbrido foi desenvolvido para o reator de formaldeído, empregando-se um modelo determinístico baseado na equação diferencial de balanço de massa aplicado sobre o leito fixo e uma rede neural artificial para modelar a cinética da reação...

Genetic Algorithms for the bus driver scheduling problem : a case study

T. G. Dias; J. P. de Sousa; J. F. Cunha
Fonte: Universidade do Porto Publicador: Universidade do Porto
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.02%
This paper describes an application of genetic algorithms to the bus driver scheduling problem. The application of genetic algorithms extends the traditional approach of Set Covering / Set Partitioning formulations, allowing the simultaneous consideration of several complex criteria. The genetic algorithm is integrated in a DSS but it can be used as very interactive tool or a stand-alone application. It incorporates the user knowledge in a quite natural way and produces solutions that are almost directly implemented by the transport companies, in their operational planning processes. Computational results with airline and bus crew scheduling problems from real world companies are presented and discussed.

Time-domain optimization of amplifiers based on distributed genetic algorithms

Tavares, Rui Manuel Leitão Santos
Fonte: Faculdade de Ciências e Tecnologia Publicador: Faculdade de Ciências e Tecnologia
Tipo: Tese de Doutorado
Publicado em //2010 Português
Relevância na Pesquisa
45.99%
Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer Engineering; The work presented in this thesis addresses the task of circuit optimization, helping the designer facing the high performance and high efficiency circuits demands of the market and technology evolution. A novel framework is introduced, based on time-domain analysis, genetic algorithm optimization, and distributed processing. The time-domain optimization methodology is based on the step response of the amplifier. The main advantage of this new time-domain methodology is that, when a given settling-error is reached within the desired settling-time, it is automatically guaranteed that the amplifier has enough open-loop gain, AOL, output-swing (OS), slew-rate (SR), closed loop bandwidth and closed loop stability. Thus, this simplification of the circuit‟s evaluation helps the optimization process to converge faster. The method used to calculate the step response expression of the circuit is based on the inverse Laplace transform applied to the transfer function, symbolically, multiplied by 1/s (which represents the unity input step). Furthermore, may be applied to transfer functions of circuits with unlimited number of zeros/poles...

Genetic Algorithms for Finite Mixture Model Based Voxel Classification in Neuroimaging

Tohka, Jussi; Krestyannikov, Evgeny; Dinov, Ivo D.; Graham, Allan MacKenzie; Shattuck, David W.; Ruotsalainen, Ulla; Toga, Arthur W.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /05/2007 Português
Relevância na Pesquisa
46.02%
Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectation-maximization (EM) algorithm, if a principled initialization is not available. In this paper, we propose a new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms. Our specific contributions are two-fold: 1) we propose to use blended crossover in order to reduce the premature convergence problem to its minimum and 2) we introduce a completely new permutation operator specifically meant for the FMM parameter estimation. In addition to improving the optimization results, the permutation operator allows for imposing biologically meaningful constraints to the FMM parameter values. We also introduce a hybrid of the genetic algorithm and the EM algorithm for efficient solution of multidimensional FMM fitting problems. We compare our algorithm to the self-annealing EM-algorithm and a standard real coded genetic algorithm with the voxel classification tasks within the brain imaging. The algorithms are tested on synthetic data as well as real three-dimensional image data from human magnetic resonance imaging...

Application of Genetic Algorithms to the Discovery of Complex Models for Simulation Studies in Human Genetics

Moore, Jason H.; Hahn, Lance W.; Ritchie, Marylyn D.; Thornton, Tricia A.; White, Bill C.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46%
Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes. Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. In this paper, we present a strategy for identifying complex genetic models for simulation studies that utilizes genetic algorithms. The genetic models used in this study are penetrance functions that define the probability of disease given a specific DNA sequence variation has been inherited. We demonstrate that the genetic algorithm approach routinely identifies interesting and useful penetrance functions in a human-competitve manner.

Leak detection and calibration of water distribution systems using transients and genetic algorithms

Vitkovsky, J.; Simpson, A.; Lambert, M.
Fonte: American Society of Civil Engineers Publicador: American Society of Civil Engineers
Tipo: Conference paper
Publicado em //1999 Português
Relevância na Pesquisa
46%
The use of genetic algorithm optimisation applied to solving engineering problems has gained popularity over the last 10 years. Applications to the design of water distribution systems based on genetic algorithm optimisation first appeared in the early 1990s. This paper starts out with a brief review of the past use of genetic algorithms applied to aspects of water distribution systems. Leak detection and calibration of pipe internal roughnesses in a network are important issues for water authorities around the world. Computer simulation of water distribution systems has become a routine task of water authorities and consultants. One of the big unknowns in developing these models is the condition of the pipes, especially if they are old. It is very difficult to obtain reliable estimates of the roughness height for each pipe in the system using steady state calibration techniques. Liggett and Chen at Cornell University in 1994 developed an innovative technique called the inverse transient technique. The technique is able to determine, from unsteady pressure traces at a number of nodes in the network, the locations and magnitudes of any leaks that are occurring and the friction factor for each pipe in the network. An alternative approach to solving the minimization problem is presented in this paper. Genetic algorithm optimisation is used. A population of solutions is generated with each string representing values of the decision variables that are to be found. These include the magnitudes of leaks at nodes in the network and friction factors for each pipe. A forward transient analysis is performed for each string in the population that represents different combinations of leak magnitudes and friction factors. The sum of the absolute deviations between the measured transient pressures and the pressures predicted by the numerical model are determined and are used to determine the fitness of the string. The smaller the sum of the deviations then the larger the fitness that is assigned to the string. The genetic algorithm operators that are used include tournament selection...

A Modified Genetic Algorithm and Switch-Based Neural Network Model Applied to Misuse-Based Intrusion Detection

Stewart, IAN
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 1260857 bytes; application/pdf
Português
Relevância na Pesquisa
45.99%
As our reliance on the Internet continues to grow, the need for secure, reliable networks also increases. Using a modified genetic algorithm and a switch-based neural network model, this thesis outlines the creation of a powerful intrusion detection system (IDS) capable of detecting network attacks. The new genetic algorithm is tested against traditional and other modified genetic algorithms using common benchmark functions, and is found to produce better results in less time, and with less human interaction. The IDS is tested using the standard benchmark data collection for intrusion detection: the DARPA 98 KDD99 set. Results are found to be comparable to those achieved using ant colony optimization, and superior to those obtained with support vector machines and other genetic algorithms.; Thesis (Master, Computing) -- Queen's University, 2009-03-03 13:28:23.787

Between theory and practice: guidelines for an optimization scheme with genetic algorithms - Part I: single-objective continuous global optimization

Serafino, Loris
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.03%
The rapid advances in the field of optimization methods in many pure and applied science pose the difficulty of keeping track of the developments as well as selecting an appropriate technique that best suits the problem in-hand. From a practitioner point of view is rightful to wander "which optimization method is the best for my problem?". Looking at the optimization process as a "system" of intercon- nected parts, in this paper are collected some ideas about how to tackle an optimization problem using a class of tools from evolutionary computations called Genetic Algorithms. Despite the number of optimization techniques available nowadays the author of this paper thinks that Genetic Algorithms still play a central role for their versatility, robustness, theoretical framework and simplicity of use. The paper can be considered a "collection of tips" (from literature and personal experience) for the non-computer-scientist that has to deal with optimization problems both in the science and engineering practice. No original methods or algorithms are proposed.; Comment: 21 pages, 1 figure. Rearranged section 2. Other minor changes throughout the paper and in references

Higher-Order Quantum-Inspired Genetic Algorithms

Nowotniak, Robert; Kucharski, Jacek
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/07/2014 Português
Relevância na Pesquisa
46.02%
This paper presents a theory and an empirical evaluation of Higher-Order Quantum-Inspired Genetic Algorithms. Fundamental notions of the theory have been introduced, and a novel Order-2 Quantum-Inspired Genetic Algorithm (QIGA2) has been presented. Contrary to all QIGA algorithms which represent quantum genes as independent qubits, in higher-order QIGAs quantum registers are used to represent genes strings which allows modelling of genes relations using quantum phenomena. Performance comparison has been conducted on a benchmark of 20 deceptive combinatorial optimization problems. It has been presented that using higher quantum orders is beneficial for genetic algorithm efficiency, and the new QIGA2 algorithm outperforms the old QIGA algorithm which was tuned in highly compute intensive metaoptimization process.

Generating extrema approximation of analytically incomputable functions through usage of parallel computer aided genetic algorithms

Swierczewski, Lukasz
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 18/03/2013 Português
Relevância na Pesquisa
46.02%
This paper presents capabilities of using genetic algorithms to find approximations of function extrema, which cannot be found using analytic ways. To enhance effectiveness of calculations, algorithm has been parallelized using OpenMP library. We gained much increase in speed on platforms using multithreaded processors with shared memory free access. During analysis we used different modifications of genetic operator, using them we obtained varied evolution process of potential solutions. Results allow to choose best methods among many applied in genetic algorithms and observation of acceleration on Yorkfield, Bloomfield, Westmere-EX and most recent Sandy Bridge cores.; Comment: 16 pages, 13 figures

The new classes of the genetic algorithms are defined by nonassociative groupoids

Sverchkov, S.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 21/09/2012 Português
Relevância na Pesquisa
45.99%
The genetic product of the groupoids, originating in the theory of DNA recombination, is introduced. It permits a natural generalization of the classical genetic algorithm. The full characterization of all three-element genetic groupoids gives an approach to construct the new classes of genetic algorithms. In the conclusion, we formulate some open problems in the theory of the genetic groupoids.

Implementing Genetic Algorithms on Arduino Micro-Controllers

Alves, Nuno
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/02/2010 Português
Relevância na Pesquisa
46.02%
Since their conception in 1975, Genetic Algorithms have been an extremely popular approach to find exact or approximate solutions to optimization and search problems. Over the last years there has been an enhanced interest in the field with related techniques, such as grammatical evolution, being developed. Unfortunately, work on developing genetic optimizations for low-end embedded architectures hasn't embraced the same enthusiasm. This short paper tackles that situation by demonstrating how genetic algorithms can be implemented in Arduino Duemilanove, a 16 MHz open-source micro-controller, with limited computation power and storage resources. As part of this short paper, the libraries used in this implementation are released into the public domain under a GPL license.

Structure Learning of Bayesian Networks by Estimation of Distribution Algorithms with Transpose Mutation

Kim,D.W.; Ko,S.; Kang,B.Y.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/08/2013 Português
Relevância na Pesquisa
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Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms that were developed as a natural alternative to genetic algorithms (GAs). Several studies have demonstrated that the heuristic scheme of EDAs is effective and efficient for many optimization problems. Recently, it has been reported that the incorporation of mutation into EDAs increases the diversity of genetic information in the population, thereby avoiding premature convergence into a suboptimal solution. In this study, we propose a new mutation operator, a transpose mutation, designed for Bayesian structure learning. It enhances the diversity of the offspring and it increases the possibility of inferring the correct arc direction by considering the arc directions in candidate solutions as bi-directional, using the matrix transpose operator. As compared to the conventional EDAs, the transpose mutation-adopted EDAs are superior and effective algorithms for learning Bayesian networks.

Application of Bio-inspired Metaheuristics in the Data Clustering Problem

Colanzi,Thelma Elita; Guez Assunção,Wesley Klewerton; Ramirez Pozo,Aurora Trinidad; B,Ana Cristina; Vendramin,Kochem; Barros Pereira,Diogo Augusto; Zorzo,Carlos Alberto; de Paula Filho,Pedro Luiz
Fonte: CLEI Electronic Journal Publicador: CLEI Electronic Journal
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2011 Português
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Abstract Clustering analysis includes a number of different algorithms and methods for grouping objects by their similar characteristics into categories. In recent years, considerable effort has been made to improve such algorithms performance. In this sense, this paper explores three different bio-inspired metaheuristics in the clustering problem: Genetic Algorithms (GAs), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). This paper proposes some refinements to be applied to these metaheuristics in order to improve their performance in the data clustering problem. The performance of the proposed algorithms is compared on five different numeric UCI databases. The results show that GA, ACO and AIS based algorithms are able to efficiently and automatically forming natural groups from a pre-defined number of clusters.