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Vitiello, Autilia. "Memetic algorithms for ontology alignment." Doctoral thesis, Universita degli studi di Salerno, 2013. http://hdl.handle.net/10556/1156.
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2011 - 2012Semantic interoperability represents the capability of two or more systems to meaningfully and accurately interpret the exchanged data so as to produce useful results. It is an essential feature of all distributed and open knowledge based systems designed for both e-government and private businesses, since it enables machine interpretation, inferencing and computable logic. Unfortunately, the task of achieving semantic interoperability is very difficult because it requires that the meanings of any data must be specified in an appropriate detail in order to resolve any potential ambiguity. Currently, the best technology recognized for achieving such level of precision in specification of meaning is represented by ontologies. According to the most frequently referenced definition [1], an ontology is an explicit specification of a conceptualization, i.e., the formal specification of the objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold them [2]. However, different tasks or different points of view lead ontology designers to produce different conceptualizations of the same domain of interest. This means that the subjectivity of the ontology modeling results in the creation of heterogeneous ontologies characterized by terminological and conceptual discrepancies. Examples of these discrepancies are the use of different words to name the same concept, the use of the same word to name different concepts, the creation of hierarchies for a specific domain region with different levels of detail and so on. The arising so-called semantic heterogeneity problem represents, in turn, an obstacle for achieving semantic interoperability... [edited by author]
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Maciel, Cristiano Baptista Faria. "A memetic algorithm for logistics network design problems." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/8601.
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Mestrado em Decisão Económica e EmpresarialNeste trabalho, um algoritmo memético é desenvolvido com o intuito de ser aplicado a uma rede logística, com três níveis, múltiplos períodos, seleção do meio de transporte e com recurso a outsourcing. O algoritmo memético pode ser aplicado a uma rede logística existente, no sentido de otimizar a sua configuração ou, se necessário, pode ser utilizado para criar uma rede logística de raiz. A produção pode ser internalizada e é permitido o envio direto de produtos para os clientes. Neste problema, as capacidades das diferentes infraestruturas podem ser expandidas ao longo do período temporal. Caso se trate uma infraestrutura já existente, após uma expansão, já não pode ser encerrada. Sempre que se abre uma nova infraestrutura, a mesma também não pode ser encerrada. A heurística é capaz de determinar o número e localizações das infraestrutura a operar, as capacidades e o fluxo de mercadoria na rede logística.
This thesis describes a memetic algorithm applied to the design of a three-echelon logistics network over multiple periods with transportation mode selection and outsourcing. The memetic algorithm can be applied to an existing supply chain in order to obtain an optimized configuration or, if required, it can be used to define a new logistics network. In addition, production can be outsourced and direct shipments of products to customer zones are possible. In this problem, the capacity of an existing or new facility can be expanded over the time horizon. In this case, the facility cannot be closed. Existing facilities, once closed, cannot be reopened. New facilities cannot be closed, once opened. The heuristic is able to determine the number and locations of facilities (i.e. plants and warehouses), capacity levels as well as the flow of products throughout the supply chain.
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Filák, Jakub. "Evoluční optimalizace turnusů jízdních řádů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236673.
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This thesis deals with the problem of vehicle scheduling in public transport. It contains a theoretical introduction to vehicles scheduling and evolutionary algorithms. Vehicle scheduling is analyzed with respect to the bus timetables. Analysis of evolutionary algorithms is done with emphasis on the genetic algorithms and tabu-search method After the theoretical introduction, a memetic algorithm for the given problem is analyzed. Finally, the thesis contains a description of the optimization system implementation and discusses the experiments with the system.
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Bonfim, Tatiane Regina. "Escalonamento memetico e neuro-memetico de tarefas." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260503.
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Orientador: Akebo YamakamiTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Este trabalho apresenta uma nova abordagem de resolução, por algoritmo memético e pela coevolução de algoritmo memético com redes neurais, para o problema de escalonamento de tarefas em máquinas paralelas idênticas e para o problema de job shop com parâmetros precisos. Para os problemas de escalonamento com parâmetros com incertezas, onde os parâmetros não são precisamente conhecidos, toma-se dificil classificar um determinado escalonamento ótimo. A noção de ótimo também torna-se imprecisa e o grau de otimalidade de um dado escalonamento ("o quanto um escalonamento é ótimo") pode ser caracterizada por um número fuzzy. Foi aplicado também o conceito de otimalidade possível para medir a possibilidade de um determinado escalonamento ser ótimo. O algoritmo memético foi aplicado para encontrar soluções para o problema, a rede neural foi aplicada para encontrar a função de fitness das soluções encontradas pelo algoritmo memético, e o conceito de possibilidade foi aplicado para avaliar as melhores soluções. Foram utilizadas as redes neurais backpropagation e com aprendizado por reforço para encontrar o valor da função de fitness. As simulações mostraram que as redes neurais apresentaram uma boa performance na coevolução com o algoritmo memético e na resolução dos problemas, e mostraram que o conceito de possibilidade teve uma boa perfomance na avaliação da otimalidade das soluções
Abstract: This work presents a new approach for the resolution of the problem of identical parallel machine scheduling and job shop scheduling with precise parameters, with memetic algorithm and memetic algorithm coevolving with neural networks. For problems with parameters with uncertainties, where the parameters of the problem are not precisely known, it is difficult to say in prior which schedule will be optimal. The notion of optimal also becomes imprecise and the degree of optimality of a given schedule ("how much a schedule is optimal") can be characterized by a fuzzy number. We was used also the concepts of possibility to measure the possibility of a given schedule be optimal. Memetic algorithm has been used to find the solutions of the problem, the neural network has been used to find the fitness function of these solutions, and the concept of possibility has been used to evaluate the best solutions. We was used neural networks with backpropagation and reinforcement learning to find the fitness function. Simulations showed that the neural networks presents a good performance in the coevolution of the memetic algorithm and in the resolution of the problems, and showed that the concept of possibility present a good performance in the evaluation of solutions optimality
Doutorado
Telecomunicações e Telemática
Doutor em Engenharia Elétrica
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Procházka, Vít. "Pokročilé optimalizační modely v odpadovém hospodářství." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231395.
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This thesis deals with an optimization of waste collection in a mid-sized town. The model is formulated based on requirements from a real process. To deal with this problem, the original memetic algorithm was developed and implemented in C++.
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Buriol, Luciana Salete. "Algoritmo memetico para o problema do caixeiro viajante assimetrico como parte de um framework para algoritmos evolutivos." [s.n.], 2000. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261832.
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Orientador: Paulo Morelato FrançaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Dentre a gama de técnicas heurísticas e exatas existentes para a resolução de problemas combinatórios, os algoritmos populacionais genéticos e meméticos têm se destacado devido a sua boa performance. Em especial, os algoritmos meméticos podem ser considerados atualmente como uma das técnicas melhores sucedidas para a resolução de vários problemas combinatórios, dentre eles, o problema do caixeiro viajante. Nesta dissertação será apresentado um algoritmo memético aplicado ao problema do caixeiro viajante assimétrico, com a proposta de uma nova busca local: Recursive Arc Insertion. Os resultados computacionais considerando as 27 instâncias assimétricas da TSPLIB são apresentados, analisados e comparados com resultados obtidos por outros métodos propostos para o problema. O mesmo algoritmo é também aplicado a 32 outras instâncias assimétricas e a 30 instâncias reduzidas do problema de ciclos hamiltonianos não direcionados. Um framework para algoritmos evolutivos é apresentado, já incluindo o algoritmo memético implementado e a redução de instâncias do problema de ciclos hamiltonianos não direcionados para o problema do caixeiro viajante simétrico. Além disso, dois geradores portáveis de instâncias com solução ótima conhecida são descritos: um para o problema do caixeiro viajante assimétrico e outro para o problema de ciclos hamiltonianos
Abstract: Among the range of heuristic and exact techniques for solving combinatorial problems, the genetic and memetic populational algorithms play an important role due to their good performance. In special, the memetic algorithms can be considered current1y as one of the best techniques to solve several combinatorial problems, especially, the traveling salesman problem. In this dissertation a memetic algorithm applied to the asymmetric traveling salesman problem is developed, and a new local search is proposed: Recursive Are Insertion. The computational results considering the 27 asymmetric instances from TSPLIB are presented, analyzed and compared with results attained by other methods recent1y published. The same algorithm is also applied to 32 other asymmetric instances and to 30 reduced instances from undirect hamiltonian cycle problem. A framework for evolutionary algorithms is also presented, including the memetic algorithm implemented and the codes which performs a reduction from the undirect hamiltonian cycle problem to the symmetric traveling salesman problem. Besides, two portable instances generators with a known optimal solution are described: one for asymmetric traveling salesman problem and other for hamiltonian cycle problem
Mestrado
Automação
Mestre em Engenharia Elétrica
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Dang, Hieu. "Adaptive multiobjective memetic optimization: algorithms and applications." Journal of Cognitive Informatics and Natural Intelligence, 2012. http://hdl.handle.net/1993/30856.
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The thesis presents research on multiobjective optimization based on memetic computing and its applications in engineering. We have introduced a framework for adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the selection, clustering, and local refinements. A robust stopping criterion for AMMOA has also been introduced to solve non-linear and large-scale optimization problems. The framework has been implemented for different benchmark test problems with remarkable results.
This thesis also presents two applications of these algorithms. First, an optimal image data hiding technique has been formulated as a multiobjective optimization problem with conflicting objectives. In particular, trade-off factors in designing an optimal image data hiding are investigated to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNN) and the adaptive multiobjective memetic optimization algorithm (AMMOA) to solve this challenging problem. This novel image data hiding approach has been implemented for many different test natural images with remarkable robustness and transparency of the embedded logo watermark. We also introduce a perceptual measure based on the relative Rényi information spectrum to evaluate the quality of watermarked images.
The second application is the problem of joint spectrum sensing and power control optimization for a multichannel, multiple-user cognitive radio network. We investigated trade-off factors in designing efficient spectrum sensing techniques to maximize the throughput and minimize the interference. To maximize the throughput of secondary users and minimize the interference to primary users, we propose a joint determination of the sensing and transmission parameters of the secondary users, such as sensing times, decision threshold vectors, and power allocation vectors. There is a conflict between these two objectives, thus a multiobjective optimization problem is used again in the form of AMMOA. This algorithm learns to find optimal spectrum sensing times, decision threshold vectors, and power allocation vectors to maximize the averaged opportunistic throughput and minimize the averaged interference to the cognitive radio network.February 2016
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Aldogan, Deniz. "Memetic Algorithms For Timetabling Problems In Private Schools." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606218/index.pdf.
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The aim of this study is to introduce a real-world timetabling problem that exists in some private schools in Turkey and to solve such problem instances utilizing memetic algorithms.
Being a new type of problem and for privacy reasons, there is no real data available. Hence for benchmarking purposes, a random data generator has been implemented. Memetic algorithms (MAs) combining genetic algorithms and hill-climbing are applied to solve synthetic problem instances produced by this generator.
Different types of recombination and mutation operators based on the hierarchical structure of the timetabling problem are proposed. A modified version of the violation directed hierarchical hill-climbing method (VDHC), introduced by A. Alkan and E. Ozcan, coordinates the process of 12 different low-level hill-climbing operators grouped in two distinct arrangements that attempt to resolve corresponding constraint violations. VDHC is an adaptive method advocating cooperation of hill-climbing operators. In addition, MAs with VDHC are compared with different versions of multimeme algorithms and pure genetic algorithms.
Experimental results on synthetic benchmark data set indicate the success of the proposed MA.
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Caraffini, Fabio. "Novel memetic computing structures for continuous optimisation." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10629.
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This thesis studies a class of optimisation algorithms, namely Memetic Computing Structures, and proposes a novel set of promising algorithms that move the first step towards an implementation for the automatic generation of optimisation algorithms for continuous domains. This thesis after a thorough review of local search algorithms and popular meta-heuristics, focuses on Memetic Computing in terms of algorithm structures and design philosophy. In particular, most of the design carried out during my doctoral studies is inspired by the lex parsimoniae, aka Ockham’s Razor. It has been shown how simple algorithms, when well implemented can outperform complex implementations. In order to achieve this aim, the design is always carried out by attempting to identify the role of each algorithmic component/operator. In this thesis, on the basis of this logic, a set of variants of a recently proposed algorithms are presented. Subsequently a novel memetic structure, namely Parallel Memetic Structure is proposed and tested against modern algorithms representing the state of the art in optimisation. Furthermore, an initial prototype of an automatic design platform is also included. This prototype performs an analysis on separability of the optimisation problem and, on the basis of the analysis results, designs some parts of the parallel structure. Promising results are included. Finally, an investigation of the correlation among the variables and problem dimensionality has been performed. An extremely interesting finding of this thesis work is that the degree of correlation among the variables decreases when the dimensionality increases. As a direct consequence of this fact, large scale problems are to some extent easier to handle than problems in low dimensionality since, due to the lack of correlation among the variables, they can effectively be tackled by an algorithm that performs moves along the axes.
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Fischer, Thomas. "Distributed memetic algorithms for graph theoretical combinatorial optimization problems." Berlin Logos-Verl, 2008. http://d-nb.info/994066945/04.
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Correa, Leonardo de Lima. "Uma proposta de algoritmo memético baseado em conhecimento para o problema de predição de estruturas 3-D de proteínas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/156640.
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Algoritmos meméticos são meta-heurísticas evolutivas voltadas intrinsecamente à exploração e incorporação de conhecimentos relacionados ao problema em estudo. Nesta dissertação, foi proposto um algoritmo memético multi populacional baseado em conhecimento para lidar com o problema de predição de estruturas tridimensionais de proteínas voltado à modelagem de estruturas livres de similaridades conformacionais com estruturas de proteínas determinadas experimentalmente. O algoritmo em questão, foi estruturado em duas etapas principais de processamento: (i) amostragem e inicialização de soluções; e (ii) otimização dos modelos estruturais provenientes da etapa anterior. A etapa I objetiva a geração e classificação de diversas soluções, a partir da estratégia Lista de Probabilidades Angulares, buscando a definição de diferentes grupos estruturais e a criação de melhores estruturas a serem incorporadas à meta-heurística como soluções iniciais das multi populações. A segunda etapa consiste no processo de otimização das estruturas oriundas da etapa I, realizado por meio da aplicação do algoritmo memético de otimização, o qual é fundamentado na organização da população de indivíduos em uma estrutura em árvore, onde cada nodo pode ser interpretado como uma subpopulação independente, que ao longo do processo interage com outros nodos por meio de operações de busca global voltadas a características do problema, visando o compartilhamento de informações, a diversificação da população de indivíduos, e a exploração mais eficaz do espaço de busca multimodal do problema O algoritmo engloba ainda uma implementação do algoritmo colônia artificial de abelhas, com o propósito de ser utilizado como uma técnica de busca local a ser aplicada em cada nodo da árvore. O algoritmo proposto foi testado em um conjunto de 24 sequências de aminoácidos, assim como comparado a dois métodos de referência na área de predição de estruturas tridimensionais de proteínas, Rosetta e QUARK. Os resultados obtidos mostraram a capacidade do método em predizer estruturas tridimensionais de proteínas com conformações similares a estruturas determinadas experimentalmente, em termos das métricas de avaliação estrutural Root-Mean-Square Deviation e Global Distance Total Score Test. Verificou-se que o algoritmo desenvolvido também foi capaz de atingir resultados comparáveis ao Rosetta e ao QUARK, sendo que em alguns casos, os superou. Corroborando assim, a eficácia do método.Memetic algorithms are evolutionary metaheuristics intrinsically concerned with the exploiting and incorporation of all available knowledge about the problem under study. In this dissertation, we present a knowledge-based memetic algorithm to tackle the threedimensional protein structure prediction problem without the explicit use of template experimentally determined structures. The algorithm was divided into two main steps of processing: (i) sampling and initialization of the algorithm solutions; and (ii) optimization of the structural models from the previous stage. The first step aims to generate and classify several structural models for a determined target protein, by the use of the strategy Angle Probability List, aiming the definition of different structural groups and the creation of better structures to initialize the initial individuals of the memetic algorithm. The Angle Probability List takes advantage of structural knowledge stored in the Protein Data Bank in order to reduce the complexity of the conformational search space. The second step of the method consists in the optimization process of the structures generated in the first stage, through the applying of the proposed memetic algorithm, which uses a tree-structured population, where each node can be seen as an independent subpopulation that interacts with others, over global search operations, aiming at information sharing, population diversity, and better exploration of the multimodal search space of the problem The method also encompasses ad-hoc global search operators, whose objective is to increase the exploration capacity of the method turning to the characteristics of the protein structure prediction problem, combined with the Artificial Bee Colony algorithm to be used as a local search technique applied to each node of the tree. The proposed algorithm was tested on a set of 24 amino acid sequences, as well as compared with two reference methods in the protein structure prediction area, Rosetta and QUARK. The results show the ability of the method to predict three-dimensional protein structures with similar foldings to the experimentally determined protein structures, regarding the structural metrics Root-Mean-Square Deviation and Global Distance Total Score Test. We also show that our method was able to reach comparable results to Rosetta and QUARK, and in some cases, it outperformed them, corroborating the effectiveness of our proposal.
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Krasnogor, Natalio. "Studies on the theory and design space of memetic algorithms." Thesis, University of the West of England, Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249135.
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Arshad, Shakeel. "Sequence based memetic algorithms for static and dynamic travelling salesman problems." Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/10809.
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Hybridization of genetic algorithms (GAs) with local search techniques has received significant attention in recent years and is being widely used to solve real-world problems. These hybrid GAs, also called memetic algorithms (MAs), are able to incorporate other powerful techniques within the framework of GAs, working as a single unit and counterbalancing each other’s disadvantages. In this thesis, we propose a hybrid GA, called Sequence Based Memetic Algorithm (SBMA) with Inver Over (IO), for solving the travelling salesman problem (TSP). This is a 2-phase MA. The first phase (SBMA) consists of traditional binary operators, and the second phase is based on a unary operator. In SBMA, a tour is split into equal sub-tours. Further, the shortest tour is selected among the sub-tours and then optimized locally. The sub-tours are stored in the memory and then used to guide the evolutionary process via a kind of embedded local search. Additionally, we apply some techniques to adapt the key parameters based on whether the best individual within the population improves or not while also maintaining the diversity. After the first phase, the hybrid algorithm enters the second phase which is the Inver Over with elite population. Here, the IO is directed to get a clue from the elite population by adding and preserving good edges. We have also shown that the above approach can be extended to handle the dynamic TSP. The framework of our hybrid approach, along with the integration of the nearest neighbour list, applying 2-Opt local search on sub-tours and adaptive parameter control in the first phase, and the elite population with the rotating gene pool strategies in the second phase, works well for the DTSP. In order to test the performance of the proposed approach for the DTSP, experiments were carried out based on different DTSP test beds. From the study, it has been observed that the integrated heuristics or meta-heuristics are able to produce good-quality solutions for the DTSP. We also analysed the effect of the gene pool and immigrants generated with the nearest neighbour algorithm, which works well with all DTSP test instances, under different dynamics.
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Mendes, Alexandre de Sousa. "Algoritmos memeticos aplicados aos problemas de sequenciamento em maquinas." [s.n.], 1999. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259722.
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Orientador: Paulo Morelato FrançaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: O problema de Sequenciamento em Máquina Simples (SMS) é um dos mais tradicionais na área de sequenciamento. Neste trabalho é explorado inicialmente o problema de SMS com restrições de tempo (datas de entrega de produtos e tempos de preparação). O objetivo é a minimização do atraso total, que se caracteriza pela soma dos atrasos na entrega de todos os produtos. O método escolhido é baseado em Algoritmos Meméticos (AM). AM constituem uma classe de metaheurística do tipo populacional que engloba outras já conhecidas, como Algoritmos Genéticos híbridos, Busca por Espalhamento, entre outras. Nesta tese, o AM utilizado é um Algoritmo Genético (AG) acrescido de uma rotina de busca local aplicada a cada elemento novo da população. Na parte evolutiva são estudadas e testadas várias possibilidades para os operadores de recombinação, mutação, estruturas populacionais, etc. São também pesquisadas estruturas de busca local que melhor se adequam para a formação de um AM. As comparações de desempenho são feitas com três diferentes abordagens encontradas na literatura. Como complementação ao trabalho são ainda analisadas a robustez do AM e o fitness landscape. Ambos são de extrema importância para validar o método e para caracterizar a dificuldade em resolver diferentes instâncias do problema. ...Observação: O resumo, na íntegra, poderá ser visualizado no texto completo da tese digital
Abstract: The Single Machine Scheduling Problem (SMS) is one of the most representative in the scheduling area. In this work we initially explore the SMS problem with time constraints (due-dates and setup times). The goal is to minimize the total tardiness, which is characterized by the sum of the delays in the production of all products. The method chosen is based on Memetic Algorithms (MA). MA constitute a class of population metaheuristics that comprise many others, such as Hybrid Genetic Algorithms, Scatter Search, etc. In this thesis the MA implemented is a Genetic Algorithm (GA) with a local search routine that is applied to each new element of the population. Concerning the evolutionary part we study and test several possibilities of recombination operators, mutation, population structures, etc. We also test local search structures that are better fitted to the design of an MA. Performance comparisons are carried out with three different approaches found in the literature. As an addition to this work we also analyze the robustness of the MA and the Fitness Landscape. 80th are extremely important to validate the method and to characterize the difficulty to solve different instances of the problem. As an extension we show some results for the Parallel Machine Scheduling (PMS) problem with sequence-dependent setup times. ...Note: The complete abstract is available with the full electronic digital thesis or dissertations
Mestrado
Automação
Mestre em Engenharia Elétrica
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Tin, Junior Gilberto Jorge. "Algoritmos memeticos aplicados ao problema de no-wait flowshop." [s.n.], 2001. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259672.
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Orientador: Paulo Morelato FrançaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Garcia, Vinicius Jacques. "Algoritmos memeticos paralelos aplicados a problemas de otimização combinatoria." [s.n.], 2002. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260167.
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Orientador : Paulo Morelato FrançaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Merz, Peter. "Memetic algorithms for combinatorial optimization problems fitness landscapes and effective search strategies /." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=960860029.
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Barkat, Ullah Abu Saleh Shah Muhammad Engineering & Information Technology Australian Defence Force Academy UNSW. "An integrated evolutionary system for solving optimization problems." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43764.
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Many real-world decision processes require solving optimization problems which may involve different types of constraints such as inequality and equality constraints. The hurdles in solving these Constrained Optimization Problems (COPs) arise from the challenge of searching a huge variable space in order to locate feasible points with acceptable solution quality. Over the last decades Evolutionary Algorithms (EAs) have brought a tremendous advancement in the area of computer science and optimization with their ability to solve various problems. However, EAs have inherent difficulty in dealing with constraints when solving COPs. This thesis presents a new Agent-based Memetic Algorithm (AMA) for solving COPs, where the agents have the ability to independently select a suitable Life Span Learning Process (LSLP) from a set of LSLPs. Each agent represents a candidate solution of the optimization problem and tries to improve its solution through cooperation with other agents. Evolutionary operators consist of only crossover and one of the self-adaptively selected LSLPs. The performance of the proposed algorithm is tested on benchmark problems, and the experimental results show convincing performance. The quality of individuals in the initial population influences the performance of evolutionary algorithms, especially when the feasible region of the constrained optimization problems is very tiny in comparison to the entire search space. This thesis proposes a method that improves the quality of randomly generated initial solutions by sacrificing very little in diversity of the population. The proposed Search Space Reduction Technique (SSRT) is tested using five different existing EAs, including AMA, by solving a number of state-of-the-art test problems and a real world case problem. The experimental results show SSRT improves the solution quality, and speeds up the performance of the algorithms. The handling of equality constraints has long been a difficult issue for evolutionary optimization methods, although several methods are available in the literature for handling functional constraints. In any optimization problems with equality constraints, to satisfy the condition of feasibility and optimality the solution points must lie on each and every equality constraint. This reduces the size of the feasible space and makes it difficult for EAs to locate feasible and optimal solutions. A new Equality Constraint Handling Technique (ECHT) is presented in this thesis, to enhance the performance of AMA in solving constrained optimization problems with equality constraints. The basic concept is to reach a point on the equality constraint from its current position by the selected individual solution and then explore on the constraint landscape. The technique is used as an agent learning process in AMA. The experimental results confirm the improved performance of the proposed algorithm. This thesis also proposes a Modified Genetic Algorithm (MGA) for solving COPs with equality constraints. After achieving inspiring performance in AMA when dealing with equality constraints, the new technique is used in the design of MGA. The experimental results show that the proposed algorithm overcomes the limitations of GA in solving COPs with equality constraints, and provides good quality solutions.
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Caleiro, Diego. "Simulando Dennett: ferramentas e construções de um naturalista." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/8/8133/tde-17102014-145500/.
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A dissertação pretende permitir ao leitor simular a forma de pensar de Daniel Dennett, e perpassa toda sua filosofia, com ênfase em seu tratamento de o que são padrões, o algoritmo evolutivo, intuition pumps, consciência, e seu uso dos conceitos de illata, abstracta, semântica e sintaxe para compreender a natureza, a biologia e a mente humana. O trabalho reapresenta, sob nova luz, grande parte das ideias mais importantes de Dennett, e procura fazer a engenharia reversa de o que o levou a pensar de determinadas maneiras, guiando o leitor através de caminhos similares, procurando fomentar um aprendizado ativo de uma forma de pensar, acima e além de uma exposição dos resultados obtidos ao longo de décadas desse pensamento no próprio DennettThis dissertation intends to provide the reader with an inner simulation of Daniel Dennetts form of reasoning, spreading over his whole philosophy, emphasizing his treatment of patterns, the evolutionary algorithm, consciousness, and his use of illata, abstracta, semantic, and synthax, to carve nature at its joints, especially biology and the human mind. It recasts, in a new light, great part of his most important ideas, and reverse engineers what made him think in particular ways, walking the reader through similar pathways, fostering an active learning of a thinking style, above and beyond a mere exposition of the results obtained by this thinking style over the years
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Teodoro, Felipe Gustavo Silva. "Seleção de características para reconhecimento biométrico baseado em sinais de eletrocardiograma." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-19122016-003653/.
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O campo da Biometria abarca uma grande variedade de tecnologias usadas para identificar e verificar a identidade de uma pessoa por meio da mensuração e análise de vários aspectos físicos e/ou comportamentais do ser humano. Diversas modalidades biométricas têm sido propostas para reconhecimento de pessoas, como impressões digitais, íris, face e voz. Estas modalidades biométricas possuem características distintas em termos de desempenho, mensurabilidade e aceitabilidade. Uma questão a ser considerada com a aplicação de sistemas biométricos em mundo real é sua robustez a ataques por circunvenção, repetição e ofuscação. Esses ataques estão se tornando cada vez mais frequentes e questionamentos estão sendo levantados a respeito dos níveis de segurança que esta tecnologia pode oferecer. Recentemente, sinais biomédicos, como eletrocardiograma (ECG), eletroencefalograma (EEG) e eletromiograma (EMG) têm sido estudados para uso em problemas envolvendo reconhecimento biométrico. A formação do sinal do ECG é uma função da anatomia estrutural e funcional do coração e dos seus tecidos circundantes. Portanto, o ECG de um indivíduo exibe padrão cardíaco único e não pode ser facilmente forjado ou duplicado, o que tem motivado a sua utilização em sistemas de identificação. Entretanto, a quantidade de características que podem ser extraídas destes sinais é muito grande. A seleção de característica tem se tornado o foco de muitas pesquisas em áreas em que bases de dados formadas por dezenas ou centenas de milhares de características estão disponíveis. Seleção de característica ajuda na compreensão dos dados, reduzindo o custo computacional, reduzindo o efeito da maldição da dimensionalidade e melhorando o desempenho do preditor. O foco da seleção de característica é selecionar um subconjunto de característica a partir dos dados de entrada, que pode descrever de forma eficiente os dados de entrada ao mesmo tempo reduzir os efeitos de ruídos ou características irrelevantes e ainda proporcionar bons resultados de predição. O objetivo desta dissertação é analisar o impacto de algumas técnicas de seleção de característica tais como, Busca Gulosa, Seleção \\textit, Algoritmo Genético, Algoritmo Memético, Otimização por Enxame de Partículas sobre o desempenho alcançado pelos sistemas biométricos baseado em ECG. Os classificadores utilizados foram $k$-Vizinhos mais Próximos, Máquinas de Vetores Suporte, Floresta de Caminhos Ótimos e classificador baseado em distância mínima. Os resultados demonstram que existe um subconjunto de características extraídas do sinal de ECG capaz de fornecer altas taxas de reconhecimentoThe field of biometrics includes a variety of technologies used to identify and verify the identity of a person by measuring and analyzing various physical and/or behavioral aspects of the human being. Several biometric modalities have been proposed for recognition of people, such as fingerprints, iris, face and speech. These biometric modalities have distinct characteristics in terms of performance, measurability and acceptability. One issue to be considered with the application of biometric systems in real world is its robustness to attacks by circumvention, spoof and obfuscation. These attacks are becoming more frequent and more questions are being raised about the levels of security that this technology can offer. Recently, biomedical signals, as electrocardiogram (ECG), electroencephalogram (EEG) and electromyogram (EMG) have been studied for use in problems involving biometric recognition. The ECG signal formation is a function of structural and functional anatomy of the heart and its surrounding tissues. Therefore, the ECG of an individual exhibits unique cardiac pattern and cannot be easily forged or duplicated, that have motivated its use in various identification systems. However, the amount of features that can be extracted from this signal is very large. The feature selection has become the focus of much research in areas where databases formed by tens or hundreds of thousands of features are available. Feature Selection helps in understanding data, reducing computation requirement, reducing the effect of curse of dimensionality and improving the predictor performance. The focus of feature selection is to select a subset of features from the input which can efficiently describe the input data while reducing effects from noise or irrelevant features and still provide good prediction results. The aim of this dissertation is to analyze the impact of some feature selection techniques, such as, greedy search, Backward Selection, Genetic Algorithm, Memetic Algorithm, Particle Swarm Optimization on the performance achieved by biometric systems based on ECG. The classifiers used were $k$-Nearest Neighbors, Support Vector Machines, Optimum-Path Forest and minimum distance classifier. The results demonstrate that there is a subset of features extracted from the ECG signal capable of providing high recognition rates
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Silva, Jose, Noel Varela, Jesus Varas, Omar Lezama, José Maco, and Martín Villón. "Comparison of bioinspired algorithms applied to the timetabling problem." Springer Science and Business Media Deutschland GmbH, 2021. http://hdl.handle.net/10757/654075.
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The problem of timetabling events is present in various organizations such as schools, hospitals, transportation centers. The purpose of timetabling activities at a university is to ensure that all students attend their required subjects in accordance with the available resources. The set of constraints that must be considered in the design of timetables involves students, teachers and infrastructure. This study shows that acceptable solutions are generated through the application of genetic, memetic and immune system algorithms for the problem of timetabling. The algorithms are applied to real instances of the University of Mumbai in India and their results are comparable with those of a human expert.Revisión por pares
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Schönberger, Jörn. "Operational freight carrier planning : basic concepts, optimization models and advanced memetic algorithms ; with 24 tables /." Berlin [u.a.] : Springer, 2005. http://www.loc.gov/catdir/enhancements/fy0663/2005922933-d.html.
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Coral, Daniel Bustos. "A cartographic approach to the dynamic vehicle routing problem with time windows and stochastic customers." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-29102018-160027/.
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This dissertation presents a cartographic approach to the dynamic vehicle routing problem with time windows and stochastic customers (DVRPTWSC). The objectives are to minimize the total travel time and maximize the number of new requests served. Addressing the DVRPTWSC requires solving the vehicle routing problem with time windows (VRPTW). A memetic algorithm (MA) for the VRPTW is proposed. The MA prunes the search space using the information gathered by a clustering procedure, which is applied to customers spatial data. The cartographic approach to the DVRPTWSC is incorporated into a multiagent system where a dispatcher agent plans the routes for vehicle agents. Before creating the initial routing plan, a cartographic processing is applied. This procedure uses hierarchical clustering to divide the region where customers are located into a hierarchy of nested regions. The initial routing plan considers known requests and potential requests sampled from known probability distributions. It is created using the search operators of the MA, which in turn use the information obtained from the hierarchical clustering to perform the search. Over the planning horizon, the dispatcher updates the routing plan: Potential requests that were included in the initial routing plan and do not materialize are removed and new requests are processed using the assignation of requests based on nested regions (ARNR). The ARNR procedure is aimed at reducing the number of vehicles considered for serving new requests. It tries to assign the requests among the vehicles that can serve them at low detour costs. The nested regions created in the cartographic processing are used to identify such vehicles. Experimental results show that the proposed MA performs competitively with state-of-the-art heuristics for the VRPTW. The proposed approach to the DVRPTWSC outperforms approaches that do not include potential requests in the initial routing plan. The use of the ARNR procedure significantly reduces the number of vehicles considered for serving new requests, and it yields solutions similar to those obtained when considering all vehicles in operation. The proposed approach performs consistently under three levels of dynamism: low, medium, and high.Esta dissertação apresenta uma abordagem cartográfica para o problema de roteamento de veículos dinâmico com janelas de tempo e clientes estocásticos (DVRPTWSC, por sua sigla em inglês). Os objetivos considerados são minimizar o tempo total de viagem e maximizar o número de pedidos novos atendidos. Para abordar o DVRPTWSC é necessário resolver o problema de roteamento de veículos com janelas de tempo (VRPTW, por sua sigla em inglês). Assim, para tratar o VRPTW propõe-se um algoritmo memético (MA, por sua sigla em inglês). O MA reduz o espaço de busca usando informação obtida por meio de um procedimento de clusterização, o qual é aplicado aos dados espaciais dos clientes. Para o DVRPTWSC, a abordagem cartográfica é incorporada em um sistema multiagente, no qual um agente roteirizador planeja as rotas para os agentes veículos. O processamento cartográfico é aplicado antes de criar o plano de rotas inicial para o DVRPTWSC. Este procedimento usa clusterização hierárquica para dividir a região onde estão os clientes em uma hierarquia de regiões encaixadas. O plano de rotas inicial considera pedidos conhecidos e pedidos potenciais amostrados de distribuições de probabilidade conhecidas. Para obter o plano de rotas inicial, usam-se os operadores de busca do MA, os quais utilizam a informação obtida da clusterização hierárquica para fazer a busca. Ao longo do horizonte de planejamento, o roteirizador atualiza o plano de rotas: Pedidos potenciais que foram considerados no plano de rotas inicial e que não foram consolidados são removidos e novos pedidos são incluídos usando o procedimento assignation of requests based on nested regions (ARNR). O procedimento ARNR visa reduzir o número de veículos considerados para atender novos pedidos. Para isso, tenta designar os novos pedidos aos veículos disponíveis para o atendimento que possuem os menores custos de desvio da rota pré-determinada. As regiões encaixadas criadas no processamento cartográfico são utilizadas para identificar esses veículos. Para o VRPTW, resultados experimentais mostram que o MA proposto é competitivo com métodos do estado da arte. A abordagem proposta para o DVRPTWSC supera abordagens que não incluem pedidos potenciais no plano de rotas inicial. O uso do procedimento ARNR reduz significativamente o número de veículos considerados para atender novos pedidos, e produz soluções similares às produzidas quando se consideram todos os veículos em operação. A abordagem desenvolvida para o DVRPTWSC tem um desempenho consistente para três níveis de dinamismo: baixo, médio e alto.
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Esseghir, Mohamed Amir. "Metaheuristics for the feature selection problem : adaptive, memetic and swarm approaches." Thesis, Artois, 2011. http://www.theses.fr/2011ARTO0206/document.
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Afin d’améliorer la qualité de prédiction des techniques de classification automatique et de fouilles de données, plusieurs modèles ont été proposés dans la littérature en vue d’extraire des connaissances à partir des données. Toutefois, avec l’expansion des systèmes d’information et des technologies associées, ces techniques d’apprentissage s’avèrent de moins en moins adaptées aux nouvelles tailles et dimensions des données. On s’intéresse dans cette étude aux problèmes de grande dimensionnalité et à l’amélioration du processus d’apprentissage des méthodes de classification à travers les techniques de filtrage et de sélection d’attributs. Le problème « d’identification d’attributs pertinents » (Feature Selection Problem), tel qu’il est défini dans la littérature, relève d’une nature combinatoire. Dans le cadre de cette thèse, on s’est intéressé au développement de nouvelles techniques d’optimisation approchées et spécifiques au problème traité ainsi qu’à l’amélioration d’algorithmes existants. La conception, l’implémentation et l’étude empirique ont montré l’efficacité et la pertinence des métaheuristiques proposéesAlthough the expansion of storage technologies, networking systems, and information system methodologies, the capabilities of conventional data processing techniques remain limited. The need to knowledge extraction, compact representation and data analysis are highly motivated by data expansion. Nevertheless, learning from data might be a complex task, particularly when it includes noisy, redundant and information-less attributes. Feature Selection (FS) tries to select the most relevant attributes from raw data, and hence guides the construction of final classification models or decision support systems. Selected features should be representative of the underlying data and provide effective usefulness to the targeted learning paradigm (i.e. classification). In this thesis, we investigate different optimization paradigms as well as its adaptation to the requirements of the feature selection challenges, namely the problem combinatorial nature. Both theoritical and empirical aspects were studied, and confirm the effectiveness of the adopted methodology as well as the proposed metaheuristic based approaches
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Younes, Abdunnaser. "Adapting Evolutionary Approaches for Optimization in Dynamic Environments." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2835.
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Many important applications in the real world that can be modelled as combinatorial optimization problems are actually dynamic in nature. However, research on dynamic optimization focuses on continuous optimization problems, and rarely targets combinatorial problems. Moreover, dynamic combinatorial problems, when addressed, are typically tackled within an application context. In this thesis, dynamic combinatorial problems are addressed collectively by adopting an evolutionary based algorithmic approach. On the plus side, their ability to manipulate several solutions at a time, their robustness and their potential for adaptability make evolutionary algorithms a good choice for solving dynamic problems. However, their tendency to converge prematurely, the difficulty in fine-tuning their search and their lack of diversity in tracking optima that shift in dynamic environments are drawbacks in this regard.
Developing general methodologies to tackle these conflicting issues constitutes the main theme of this thesis. First, definitions and measures of algorithm performance are reviewed. Second, methods of benchmark generation are developed under a generalized framework. Finally, methods to improve the ability of evolutionary algorithms to efficiently track optima shifting due to environmental changes are investigated. These methods include adapting genetic parameters to population diversity and environmental changes, the use of multi-populations as an additional means to control diversity, and the incorporation of local search heuristics to fine-tune the search process efficiently.
The methodologies developed for algorithm enhancement and benchmark generation are used to build and test evolutionary models for dynamic versions of the travelling salesman problem and the flexible manufacturing system. Results of experimentation demonstrate that the methods are effective on both problems and hence have a great potential for other dynamic combinatorial problems as well.
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Chae, Junjae. "Concurrent design of facility layout and flow-based department formation." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969.1/1606.
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The design of facility layout takes into account a number of issues including the formation of departments, the layout of these, the determination of the material handling methods to be used, etc. To achieve an efficient layout, these issues should be examined simultaneously. However, in practice, these problems are generally formulated and solved sequentially due to the complicated nature of the integrated problem. Specifically, there is close interaction between the formation of departments and layout of these departments. These problems are treated as separate problems that are solved sequentially. This procedure is mainly due to the complexity of each problem and the interrelationships between them. In this research, we take a first step toward integrating the flow-based department formation and departmental layout into comprehensive mathematical models and develop appropriate solution procedures. It is expected that these mathematical models and the solution procedures developed will generate more efficient manufacturing system designs, insights into the nature of the concurrent facility layout problem, and new research directions.
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Oliveira, Dayvid Victor Rodrigues de. "Algoritmos de Geração de Protótipos Para Bases Desbalanceadas." Universidade Federal de Pernambuco, 2014. https://repositorio.ufpe.br/handle/123456789/11330.
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license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)Made available in DSpace on 2015-03-06T19:37:29Z (GMT). No. of bitstreams: 2 DISSERTAÇÃO Dayvid Victor Rodrigues de Oliveira.pdf: 798881 bytes, checksum: 3b4ac40fda11573b025340c2b03e8903 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2014-02-25
Técnicas de redução de instâncias são técnicas usadas para reduzir a quantidade de instâncias em um conjunto de dados. Estas técnicas podem atuar removendo dados redundantes ou gerando novos dados. As instâncias resultantes são chamadas de protótipos. Técnicas de seleção de protótipos, são técnicas de redução de instâncias que realizam esta tarefa selecionando um subconjunto do conjunto de dados original. Já as técnicas de geração de protótipos, são técnicas de redução de instâncias que criam instâncias que não necessariamente pertencem ao conjunto de dados original. Algoritmos evolucionários têm sido frequentemente utilizados em seleção de protótipos, tal abordagem é chamada de evolutionary prototype selection. Algumas bases de dados do mundo real possuem muitas instâncias de uma classe, a classe majoritária, e poucas de outra, classe minoritária, estas bases são chamadas de bases desbalanceadas. Em tais bases, muitos algoritmos de redução de instâncias se tornam inviáveis, retornando muitas instâncias da classe majoritária e poucas, ou até nenhuma, da classe minoritária. Este efeito é ainda mais acentuado em técnicas de remoção de ruídos. Neste trabalho, são propostas duas técnicas de geração de protótipos que minimizam o efeito de desbalanceamento entre classes. A primeira proposta é o Creative Steady-State Memetic Algorithm (CSSMA), um algoritmo de geração de protótipos que utiliza um algoritmo evolucionário, incorporando uma busca local, para encontrar o conjunto de protótipos artificiais que maximiza a função de aptidão. Esta técnica é inspirada no Steady-State Memetic Algorithm, uma das melhores técnicas de seleção de protótipos na literatura, tanto em redução quanto em classificação. A segunda proposta é o Adaptive Self- Generating Prototypes (ASGP), esta técnica gera instâncias levando em consideração o tamanho do maior agrupamento de cada classe. O ASGP é uma derivação do Self-Generating Prototypes (SGP), considerada uma das técnicas de geração de protótipos de maior poder de generalização, sendo, porém, ineficiente em bases desbalanceadas. As bases de dados usadas nos experimentos são do módulo imbalanced datasets do KEEL software, dicotômicas, e com diferentes níveis de desbalanceamento. Cada base é dividida em 5 partições para aplicação do k-fold cross validation (k=5). As métricas usadas para avaliar a performance dos algoritmos foram a area under the ROC curve (AUC) e a taxa de redução. Para comparar os resultados, foi utilizado o teste estatístico de Wilcoxon. Os resultados mostram que o CSSMA foi superior em taxa de acerto, AUC, a outros algoritmos evolucionários de redução de instâncias recentemente propostos. O ASGP também obteve uma AUC superior ao Self-Generating Prototypes 2, versão mais atual do SGP.
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Silva, Ana Cristina Girao e. "Busca heur?stica atrav?s de algoritmo gen?tico e mem?tico com constru??o de voc?bulos para o problema de atribui??o de localidades a an?is Sonet." Universidade Federal do Rio Grande do Norte, 2008. http://repositorio.ufrn.br:8080/jspui/handle/123456789/14914.
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Previous issue date: 2008-12-23Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
Telecommunications play a key role in contemporary society. However, as new technologies are put into the market, it also grows the demanding for new products and services that depend on the offered infrastructure, making the problems of planning telecommunications networks, despite the advances in technology, increasingly larger and complex. However, many of these problems can be formulated as models of combinatorial optimization, and the use of heuristic algorithms can help solving these issues in the planning phase. In this project it was developed two pure metaheuristic implementations Genetic algorithm (GA) and Memetic Algorithm (MA) plus a third hybrid implementation Memetic Algorithm with Vocabulary Building (MA+VB) for a problem in telecommunications that is known in the literature as Problem SONET Ring Assignment Problem or SRAP. The SRAP arises during the planning stage of the physical network and it consists in the selection of connections between a number of locations (customers) in order to meet a series of restrictions on the lowest possible cost. This problem is NP-hard, so efficient exact algorithms (in polynomial complexity ) are not known and may, indeed, even exist
As telecomunica??es desempenham um papel fundamental na sociedade contempor?nea. Mas ? medida que novas tecnologias s?o introduzidas ao mercado, cresce tamb?m a demanda por novos produtos e servi?os que dependem da infra-estrutura oferecida, tornando os problemas de planejamento de redes de telecomunica??es, apesar da evolu??o tecnol?gica, cada vez maiores e complexos. No entanto, muitos desses problemas podem ser formulados como modelos de otimiza??o combinat?ria, e o uso de algoritmos heur?sticos podem ajudar a solucionar essas quest?es da fase de planejamento. Neste trabalho, foram desenvolvidas duas implementa??es metaheur?sticas puras Algoritmo Gen?tico (AG) e Algoritmo Mem?tico (AM) al?m de uma terceira implementa??o h?brida Algoritmo Mem?tico com Vocabulary Building (AM+VB) para um problema de telecomunica??es que ? conhecido na literatura por Problema de Atribui??o de Localidades a An?is SONET ou SRAP (do ingl?s, SONET Ring Assignment Problem). O SRAP surge durante a etapa do planejamento f?sico da rede e consiste na determina??o das conex?es entre um conjunto de localidades (clientes), de modo a satisfazer uma s?rie de restri??es ao menor custo poss?vel. Esse problema ? NP-dif?cil e portanto algoritmos exatos eficientes (de complexidade polinomial) n?o s?o conhecidos, podendo, inclusive, nem existir
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Bitar, Abdoul. "Ordonnancement sur machines parallèles appliqué à la fabrication de semi-conducteurs : ateliers de photolithographie." Thesis, Saint-Etienne, EMSE, 2015. http://www.theses.fr/2015EMSE0808/document.
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Le secteur des semi-conducteurs a connu un développement considérable ces dernières décennies, du fait des nouvelles applications de la microélectronique dans l'industrie. Le processus de fabrication est réputé pour sa complexité. L'un des ateliers les plus critiques de la production, l'atelier de photolithographie, est régi par un ensemble conséquent de contraintes de production. La multiplicité des ressources utilisées, le nombre important de produits traités, en font une zone importante à optimiser. Les objectifs de la thèse ont été de modéliser cet atelier sous la forme d'un problème d'ordonnancement sur machines parallèles et d'optimiser plusieurs critères jugés pertinents pour évaluer la qualité des solutions. Des résultats en termes de complexité, et d'algorithmes de résolution, ont permis une application industrielle, dans la mesure où un logiciel d'optimisation destiné à l'ordonnancement des lots en photolithographie a été développéSemiconductor manufacturing has grown considerably in recent decades, due to new industrial applications of microelectronic devices. The related manufacturing process is known to be complex. A bottleneck process step, the photolithography workshop, gathers various types of constraints, related to the number of auxiliary resources and the tools characteristics. The aims of the thesis were to model this workstation as a parallel machine scheduling problem and to optimize various criteria, determined by industrial needs. Some complexity results are provided and optimization algorithms led to an industrial application, i.e. a software providing optimized schedules in a specific fab
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Carneiro, Milena Bueno Pereira. "Reconhecimento de íris utilizando algoritmos genéticos e amostragem não uniforme." Universidade Federal de Uberlândia, 2010. https://repositorio.ufu.br/handle/123456789/14276.
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The automatic recognition of individuals through the iris characteristics
is an e±cient biometric technique that is widely studied and applied around
the world. Many image processing stages are necessary to make possible
the representation and the interpretation of the iris information. This work
presents the state of the art in iris recognition systems where the most re-
markable works and the di®erent techniques applied to perform each process-
ing stage are quoted. The implementations of each processing stage using
traditional techniques are presented and, afterwards, two innovator methods
are proposed with the common objective of bringing bene¯t to the system.
The ¯rst processing stage should be the localization of the iris region in an
eye image. The ¯rst method proposed in this work presents an algorithm
to achieve the iris localization through the utilization of the called Memetic
Algorithms. The new method is compared to a classical method and the
obtained results show advantages concerning e±ciency and processing time.
In another processing stage there must be a pixels sampling from the iris
region, from where the information used to di®erentiate the individuals is
extracted. Traditionally, this sampling process is accomplished in an uni-
form way along the whole iris region. It is proposed a pre-processing method
which suggests a non uniform pixels sampling from the iris region with the
objective of selecting the group of pixels which carry more information about
the iris structure. The search for this group of pixels is done through Ge-
netic Algorithms. The application of the new method improves the e±ciency
of the system and also, allows the generation of smaller templates. In this
work, a study on the called Active Shape Models is also accomplished and its
application to perform the iris region segmentation is evaluated. To execute
the simulations and the evaluation of the methods, it was used two public
and free iris images database: UBIRIS database and MMU database.O reconhecimento automático de pessoas utilizando-se características da íris é uma eficiente técnica biométrica que está sendo largamente estudada e aplicada em todo o mundo. Diversas etapas de processamento são necessárias para tornar possível a representação e a interpretação da informação contida na íris. Neste trabalho é apresentado o estado da arte de sistemas de reconhecimento de íris onde são citados os trabalhos de maior destaque e as diferentes técnicas empregadas em cada etapa de processamento. São apresentadas implementações de cada etapa de processamento utilizando técnicas tradicionais e, posteriormente, são propostos dois métodos inovadores que têm o objetivo comum de trazer benefícios ao sistema. A primeira etapa de processamento é a localização da região da íris na imagem. O primeiro método proposto neste trabalho apresenta um algoritmo para realizar a localização da íris utilizando os chamados Algoritmos Meméticos. O novo método é comparado a um método clássico e os resultadosnobtidos demonstram vantagens no que diz respeito à eficiência e ao tempo de processamento. Em uma outra etapa de processamento deve haver uma amostragem de pixels na região da íris, de onde são retiradas as informações utilizadas para diferenciar os indivíduos. Tradicionalmente, esta amostragem é realizada de maneira uniforme ao longo de toda a região da íris. É proposto um método de pré-processamento que sugere uma amostragem não uniforme de pixels na região da íris com o objetivo de selecionar o conjunto de pixels que carregam mais informações da estrutura da íris. A busca por esse conjunto de pixels é realizada utilizando-se Algoritmos Genéticos. A aplicação deste novo método aumenta a eficiência do sistema e ainda possibilita a geração de templates binários menores. Neste trabalho é realizado, ainda, um estudos dos chamados Active Shape Models e a sua aplicação para segmentar a região da íris é avaliada. Para a simulação e avaliação dos métodos, foram utilizados dois bancos de imagens de íris públicos e gratuitos: o banco de imagens UBIRIS e o banco de imagens MMU.
Doutor em Ciências
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Zakaria, Rabih. "Optimization of the car relocation operations in one-way carsharing systems." Thesis, Belfort-Montbéliard, 2015. http://www.theses.fr/2015BELF0281/document.
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L'autopartage est un service de mobilité qui offre les mêmes avantages que les voitures particulières mais sansnotion de propriété. Les clients du système peuvent accéder aux véhicules sans ou avec réservation préalable. Laflotte de voitures est distribuée entre les stations et les clients peuvent prendre une voiture d'une station et ladéposer dans n'importe quelle autre station (one-way), chaque station disposant d'un nombre maximum de placesde stationnement. La demande pour la prise ou le retour des voitures dans chaque station est souvent asymétriqueentre les stations et varie au cours de la journée. Par conséquent, certaines stations accumulent des voitures etatteignent leur capacité maximale prévenant alors de nouvelles voitures de trouver une place de stationnement.Dans le même temps, des stations se vident et conduisent au rejet de la demande de retrait de clients. Notre travailporte sur l'optimisation des opérations de redéploiement de voitures afin de redistribuer efficacement les voitures surles stations suivant la demande qui varie en fonction du temps et de l'espace. Dans les systèmes d'autopartage àsens unique, le problème du redéploiement de voitures sur les stations est techniquement plus difficile que leproblème de la redistribution des vélos dans les systèmes de vélopartage. Dans ce dernier, on peut utiliser uncamion pour déplacer plusieurs vélos en même temps, alors que nous ne pouvons pas le faire dans le systèmeautopartage en raison de la taille des voitures et de la difficulté de chargement et de déchargement. Ces opérationsaugmentent le coût de fonctionnement du système d'autopartage sur l'opérateur. De ce fait, l'optimisation de cesopérations est essentielle afin de réduire leur coût. Dans cette thèse, nous développons un modèle deprogrammation linéaire en nombre entier pour ce problème. Ensuite, nous présentons trois politiques différentes deredéploiement de voitures que nous mettons en oeuvre dans des algorithmes de recherche gloutonne et nousmontrons que les opérations de redéploiement qui ne considèrent pas les futures demandes ne sont pas efficacesdans la réduction du nombre de demandes rejetées. Les solutions fournies par notre algorithme glouton sontperformantes en temps d'exécution (moins d'une seconde) et en qualité en comparaison avec les solutions fourniespar CPLEX. L'évaluation de la robustesse des deux approches présentées par l'ajout d'un bruit stochastique sur lesdonnées d'entrée montre qu'elles sont très dépendantes des données même avec l'adoption de valeur de seuil deredéploiement. En parallèle à ce travail algorithmique, l'analyse de variance (ANOVA) et des méthodes derégression multilinéaires ont été appliqués sur l'ensemble de données utilisées pour construire un modèle global afind'estimer le nombre de demandes rejetées. Enfin, nous avons développé et comparé deux algorithmesévolutionnaires multicritères pour prendre en compte l'indécision sur les objectifs de l'optimisation, NSGA-II et unalgorithme mémétique qui a montré une bonne performance pour résoudre ce problèmeTo buy it. Users can have access to vehicles on the go with or without reservation. Each station has a maximumnumber of parking places. In one-way carsharing system, users can pick up a car from a station and drop it in anyother station. The number of available cars in each station will vary based on the departure and the arrival of cars oneach station at each time of the day. The demand for taking or returning cars in each station is often asymmetric andis fluctuating during the day. Therefore, some stations will accumulate cars and will reach their maximum capacitypreventing new arriving cars from finding a parking place, while other stations will become empty which lead to therejection of new users demand to take a car. Users expect that cars are always available in stations when they needit, and they expect to find a free parking place at the destination station when they want to return the rented car aswell. However, maintaining this level of service is not an easy task. For this sake, carsharing operators recruitemployees to relocate cars between the stations in order to satisfy the users' demands.Our work concerns the optimization of the car relocation operations in order to efficiently redistribute the cars overthe stations with regard to user demands, which are time and space dependent. In one-way carsharing systems, therelocation problem is technically more difficult than the relocation problem in bikesharing systems. In the latter, wecan use trucks to move several bikes at the same time, while we cannot do this in carsharing system because of thesize of cars and the difficulty of loading and unloading cars. These operations increase the cost of operating thecarsharing system.As a result, optimizing these operations is crucial in order to reduce the cost of the operator. In this thesis, we modelthis problem as an Integer Linear Programming model. Then we present three different car relocation policies thatwe implement in a greedy search algorithm. The comparison between the three policies shows that car relocationoperations that do not consider future demands are not effective in reducing the number of rejected demands.Results prove that solutions provided by our greedy algorithm when using a good policy, are competitive withCPLEX solutions. Furthermore, adding stochastic modification on the input data proves that the robustness of thetwo presented approaches to solve the relocation problem is highly dependent on the input demand even afteradding threshold values constraints. After that, the analysis of variance (ANOVA) and the multi-linear regressionmethods were applied on the used dataset in order to build a global model to estimate the number of rejecteddemands. Finally, we developed and compared two multi-objectives evolutionary algorithms to deal with thedecisional aspect of the car relocation problem using NSGA-II and memetic algorithms
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Póvoa, Caio José Fernandes. "Estratégias de otimização de trajetos e alocação de torres em projetos de linhas de transmissão aéreas." Universidade Federal de Goiás, 2018. http://repositorio.bc.ufg.br/tede/handle/tede/8294.
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This dissertation of master degree describes methods of optimizing routes and allocating towers of overhead power lines, with the objective of meeting technical, structural and constructive constraints, and reducing financial costs. The generated solutions are graphically presented through the transmission line profile and its 3-dimension representation upon the elevation map of the area. For the projects evaluation, elements of structural analysis are used, highlighting the Matrix Structural Analysis for the study of efforts and deformations in the towers and their components. Three methods are proposed, each one using different approaches. First, it will be shown an optimization algorithm based on Evolutionary Computation, characterized by the application of natural selection on individuals generated from mutations and genetic crossover. The second algorithm was inspired by the well-known Nelder-Mead optimization method. The triangular transformations addressed in the original method were adapted and physically implemented to transmission lines. The last optimization algorithm presented is a hybridization of the two previous methods. Finally, a performance comparison of the algorithms, in which each one of them will be applied to three different cases, will be carried out in order to validate them.
Esta dissertação de mestrado descreve métodos de otimização de trajetos e alocação de torres de linhas aéreas de transmissão de energia elétrica, com o objetivo de obedecer a restrições técnicas, estruturais e construtivas, e de reduzir custos financeiros. As soluções encontradas são apresentadas graficamente a partir da plotagem do perfil da linha de transmissão, e da sua representação em três dimensões sobre o mapa de relevo da região. Para a avaliação dos projetos, utilizam-se elementos de análise estrutural, destacando-se a Análise Estrutural Matricial para o estudo dos esforços e deformações nas torres e seus componentes. São propostos três métodos que utilizam abordagens diferentes. Primeiramente, será considerado um algoritmo de otimização baseado na Computação Evolucionária, caracterizando-se pela aplicação da seleção natural ao longo de gerações, em indivíduos gerados a partir de mutações e recombinações. O segundo algoritmo apresentado é inspirado no consagrado método de Nelder-Mead, sendo as transformações triangulares, por ele apresentadas, adaptadas e implementadas fisicamente a linhas de transmissão. O último método de otimização é uma hibridação dos dois métodos anteriores. Por fim, será feita uma comparação de desempenho dos algoritmos apresentados, a partir da aplicação de cada um deles a três estudos de caso distintos para validá-los.
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Hasan, S. M. Kamrul Engineering & Information Technology Australian Defence Force Academy UNSW. "Evolutionary algorithms for solving job-shop scheduling problems in the presence of process interruptions." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43768.
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In this thesis, the Job Shop Scheduling Problem (JSSP) is the problem of interest. The classical JSSP is well-known as an NP-hard problem. Although with current computational capabilities, the small problems are solvable using deterministic methods, it is out of reach when they are larger in size. The complexity of JSSP is further increased when process interruptions, such as machine breakdown and/or machine unavailability, are introduced. Over the last few decades, several stochastic algorithms have been proposed to solve JSSPs. However, none of them are suitable for all kinds of problems. Genetic and Memetic algorithms have proved their effectiveness in these regards, because of their diverse searching behavior. In this thesis, we have developed one genetic algorithm and three different Memetic Algorithms (MAs) for solving JSSPs. Three priority rules are designed, namely partial re-ordering, gap reduction and restricted swapping, and these have been used as local search techniques in designing our MAs. We have solved 40 well-known benchmark problems and compared the results obtained with some of the established algorithms available in the literature. Our algorithm clearly outperforms those established algorithms. For better justification of the superiority of MAs over GA, we have performed statistical significance testing (Student's t-test). The experimental results show that MA, as compared to GA, not only significantly improves the quality of solutions, but also reduces the overall computation. We have extended our work by proposing an improved local search technique, shifted gap-reduction (SGR), which improves the performance of MAs when tested with the relatively difficult test problems. We have also modified the new algorithm to accommodate JSSPs with machine unavailability and also developed a new reactive scheduling technique to re-optimize the schedule after machine breakdowns. We have considered two scenarios of machine unavailability. Firstly, where the unavailability information is available in advance (predictive), and secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules. We also confirm that the effect of a single continuous breakdown has more impact compared to short multiple breakdowns, even if the total durations of the breakdowns are the same. Finally, for convenience of implementation, we have developed a decision support system (DSS). In the DSS, we have built a graphical user interface (GUI) for user friendly data inputs, model choices, and output generation. This DSS tool will help users in solving JSSPs without understanding the complexity of the problem and solution approaches, as well as will contribute in reducing the computational and operational costs.
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Costa, Vinícius Oliveira. "Alocação de antenas para rede celular de 4G utilizando algoritmos meméticos." Universidade Federal do Tocantins, 2016. http://hdl.handle.net/11612/973.
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Este trabalho trata do problema de alocação de estações rádio base (ERBs) para o sistema
de telefonia celular de 4G, que no Brasil utiliza o protocolo LTE (Long Term Evolution).
Tal problema consiste em dada uma determinada região geográfica, onde se encontram
os possíveis clientes, dispor antenas de modo a cobrir a maior área possível da região
em estudo, levando em consideração a capacidade de cada antena em atender os clientes
com qualidade de serviço. O algoritmo apresentado calcula o raio de alcance da ERB, a
quantidade mínima de ERBs necessárias para cobrir a região em estudo e a localização
de cada ERB. Para que o algoritmo pudesse ser desenvolvido foi investigado o sistema
de comunicação LTE, modelos de propagação de sinal além do algoritmo memético, visto
que a alocação de ERBs é um problema NP-difícil. Para o raio de ação da célula foi
considerado, além do modelo de propagação, o calculo de link budget, throughput e relação
sinal ruído. Por fim, uma comparação entre o LTE operando nas faixas de frequências de
700 MHz e 2,5 GHz foi realizado. O algoritmo de alocação de ERBs se mostrou eficiente
cobrindo mais de 80% da área de estudo em 29 dos 30 casos analisados. Com relação a
frequência, o LTE se mostrou mais adequado operando em 700 MHz pois a quantidade de
ERBs para cobertura da área de estudo é menor se comparado a frequências de 2,5 GHz.This work deals with the issue of radio base stations (RBSs) allocation for the 4G cell phone system, which in Brazil uses the LTE (Long Term Evolution) protocol. Such problem consists in a certain geographical region, where potential customers might be found, having antennas to cover the largest possible area of the region under study, taking into account the capacity of each antenna to serve customers with quality of service. The presented algorithm calculates the range of the RBS station, the minimum amount of necessary RBS to cover the area under study and the location of each RBS. In order to the algorithm to be developed the LTE communication system was investigated, signal propagation models beyond memetic algorithm, since the RBS allocation is a NP-hard problem. For the cell’s range of action it was considered, besides the model of propagation, the link budget calculation, throughput and noise signal relation. Therefore, a comparison between LTE operating on 700 MHz and 2,5 GHz frequencies was made. The RBS allocation algorithm was efficient covering more than 80% of the study area in 29 from the 30 analyzed cases. In relation to the frequency, LTE was considered more adequate operating on 700 MHz, for the quantity of RBS to cover the study area is smaller, if compared to 2,5 GHz frequencies.
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Nguyen, Thi Nguyet Que. "Nouveaux développements en histologie spectrale IR : application au tissu colique." Thesis, Reims, 2016. http://www.theses.fr/2016REIMS040/document.
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Les développements continus en micro-spectroscopie vibrationnelle IR et en analyse numérique de données multidimensionnelles ont permis récemment l'émergence de l'histologie spectrale. A l'échelle tissulaire et sur une base biomoléculaire, cette nouvelle approche représente un outil prometteur pour une meilleure analyse et caractérisation de différents états physiopathologiques, et potentiellement une aide au diagnostic clinique. Dans ce travail, en utilisant un modèle tissulaire de côlon normal chez la Souris et chez l’Homme, nous avons apporté des améliorations à la chaîne de traitements des données afin d'automatiser et d'optimiser cette histologie spectrale.En effet, dans un premier temps, le développement d’une double application hiérarchique d'indices de validité a permis de déterminer le nombre optimal de classes nécessaire à une caractérisation complète des structures histologiques. Dans un second temps, cette méthode a été généralisée à l'échelle interindividuelle par couplage d'un prétraitement par EMSC (Extended Multiplicative Signal Correction) et d'une classification non-supervisée k-Means; ce couplage étant appliqué conjointement à toutes les images spectrales IR. Enfin, compte tenu de l'essor des métaheuristiques et de leur capacité à résoudre des problèmes complexes d'optimisation numérique, nous avons transposé un algorithme mémétique aux données spectrales IR. Ce nouvel algorithme se compose d'un algorithme génétique et d'un raffinement par classification non-supervisée k-Means. Comparé aux méthodes classiques de clustering, cet algorithme mémétique appliqué aux images spectrales IR, a permis de réaliser une classification non-supervisée optimale et indépendante de l'initialisationRecent developments in IR vibrational microspectroscopy and numerical multidimensional analysis have led to the emergence of spectral histology. At the tissue level, this new approach represents an attractive tool for a better analysis and characterization of pathophysiological states and for diagnostic challenges. Here, using normal murine and human colon tissues, data processing steps have been improved for automating and optimizing this spectral histology. First, the development of a hierarchical double application of validity indices permitted to determine the optimal number of clusters that correctly identified the different colon histological components. Second, this method has been improved to perform spectral histology at the inter-individual level. For this, EMSC (Extended Multiplicative Signal Correction) preprocessing has been successfully combined to k-Means clustering. Finally, given the ability of metaheuristics to solve complex optimization problems, a memetic algorithm has been developed for IR spectral data clustering. This algorithm is composed of a genetic algorithm and a k-Means clustering refinement. Compared with conventional clustering methods, our memetic algorithm allowed to generate an optimal and initialization-independent clustering
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Brum, James Gladstone Fagundes. "Desenvolvimento de um protótipo de software para geração de grade de programação de comerciais aplicável à TV Digital/IPTV utilizando Metaheurísticas." Universidade do Vale do Rio dos Sinos, 2014. http://www.repositorio.jesuita.org.br/handle/UNISINOS/4658.
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PROCERGS – Cia de Processamento Dados do Estado Rio Grande Sul
Este trabalho apresenta o desenvolvimento de um protótipo de software, utilizando metaheurísticas por meio de um Algoritmo Memético, para a Geração de Grade de Programação de intervenções comerciais aplicada à TV Digital e a IPTV. O problema apresenta-se como uma linha de tempo na grade televisiva com sua programação onde estão definidos horários de intervenção em que grupos de comerciais devem ser exibidos. A organização destes comerciais nas intervenções obedecem a um conjunto de requisitos que devem ser otimizados como: a taxa de retorno, adequação ao público alvo, e utilização da largura de banda do servidor e também de restrições como: a classificação indicativa, número de exibições do comercial e adequação à programação. Neste contexto são considerados os problemas de Seleção de Partes e de Timetabling para a elaboração do protótipo, abordando sua solução com a utilização de um Algoritmo Memético, desenvolvido aplicando as metaheurísticas de Algoritmos Genéticos e de Busca Tabu. O resultado obtido foi a geração de uma ferramenta computacional que viabilizou o gerenciamento da inserção de comerciais nas grades de programação, através da obtenção de soluções de boa qualidade.
This paper shows the development of a software prototype using metaheuristics via a memetic algorithm to generation of the Grid Programming of ads interventions applied to Digital TV and IPTV. This problem is presented as a timeline in a TV programing with intervals of interventions where ads groups should be displayed. The organization of these interventions ads groups follow a set of requirements that must be optimized as: the rate of return, appropriateness to the target audience, and use of the bandwidth of the server, and also restrictions like: parental rating, number of views of each ad, time box of the intervention and fitness programming. In this context are considered the problems of Selection Parties and Timetabling for build the prototype and approach the solution using a memetic algorithm developed by applying the metaheuristic Genetic Algorithms and Tabu Search. The resulted was the generation of a computational tool that allows the insertion of ads management in grids programming, by obtaining good quality solutins.
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Mansouri, Abdelkhalek. "Generic heuristics on GPU to superpixel segmentation and application to optical flow estimation." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA012.
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Déterminer des clusters dans des nuages de points et apparier des graphes sont des tâches primordiales en informatique, analyse de donnée, traitement d’image, généralement modélisées par des problèmes d’optimisation de classe NP-difficile. Avec l’avènement des multiprocesseurs à bas coût, l’accélération des procédures heuristiques pour ces tâches devient possible et nécessaire. Nous proposons des implantations parallèles sur système GPU (graphics processing unit) pour des algorithmes génériques appliqués ici à la segmentation d’image en superpixels et au problème du flot optique. Le but est de fournir des algorithmes génériques basés sur des structures de données décentralisées et aisément adaptables à différents problèmes d’optimisation sur des graphes et plateformes parallèles.Les algorithmes parallèles proposés sur GPU incluent le classique k-means et le calcul de forêt couvrante minimum pour la segmentation en superpixels. Ils incluent également un algorithme de recherche locale parallèle et un algorithme mémétique à base de population de solutions appliqués à l’estimation du flot optique via des appariements de superpixels. Tandis que les opérations sur les données exploitent le GPU, l’algorithme mémétique opère en tant que coalition de processus exécutés en parallèle sur le CPU multi-cœur et requérant des ressources GPU. Les images sont des nuages de points de l’espace euclidien 3D (domaine espace-intensité), et aussi des graphes auxquels sont associés des grilles de processeurs. Les kernels GPU exécutent des transformations en parallèle sous contrôle du CPU qui a un rôle réduit de détection des conditions d’arrêt et de séquencement des transformations.La contribution présentée est composée de deux grandes parties. Dans une première partie, nous présentons des outils pour la segmentation en superpixels. Une implémentation parallèle de l’algorithme des k-means est présentée et appliquée aux données 3D. Elle est basée sur une subdivision cellulaire de l’espace 3D qui permet des recherches de plus proche voisin en parallèle en temps optimal constant pour des distributions bornées. Nous présentons également une application de l’algorithme parallèle de calcul de forêt couvrante de Boruvka à la segmentation superpixel de type ligne de partage-des-eaux (watershed). Dans une deuxième partie, en se basant sur les superpixels générés, des procédures parallèles de mise en correspondance sont dérivées pour l’estimation du flot optique avec prise en compte des discontinuités. Ces méthodes incluent des heuristiques de construction et d’amélioration, telles que le winner-take-all et la recherche locale parallèle, et leur intégration dans une métaheuristique à base de population. Diverses combinaisons d’exécution sont présentées et évaluées en comparaison avec des algorithmes de l’état de l’art performantsFinding clusters in point clouds and matching graphs to graphs are recurrent tasks in computer science domain, data analysis, image processing, that are most often modeled as NP-hard optimization problems. With the development and accessibility of cheap multiprocessors, acceleration of the heuristic procedures for these tasks becomes possible and necessary. We propose parallel implantation on GPU (graphics processing unit) system for some generic algorithms applied here to image superpixel segmentation and image optical flow problem. The aim is to provide generic algorithms based on standard decentralized data structures to be easy to improve and customized on many optimization problems and parallel platforms.The proposed parallel algorithm implementations include classical k-means algorithm and application of minimum spanning forest computation for super-pixel segmentation. They include also a parallel local search procedure, and a population-based memetic algorithm applied to optical flow estimation based on superpixel matching. While data operations fully exploit GPU, the memetic algorithm operates like a coalition of processes executed in parallel on the multi-core CPU and requesting GPU resources. Images are point clouds in 3D Euclidean space (space-gray value domain), and are also graphs to which are assigned processor grids. GPU kernels execute parallel transformations under CPU control whose limited role only consists in stopping criteria evaluation or sequencing transformations.The presented contribution contains two main parts. Firstly, we present tools for superpixel segmentation. A parallel implementation of the k-means algorithm is presented with application to 3D data. It is based on a cellular grid subdivision of 3D space that allows closest point findings in constant optimal time for bounded distributions. We present an application of the parallel Boruvka minimum spanning tree algorithm to compute watershed minimum spanning forest. Secondly, based on the generated superpixels and segmentation, we derive parallel optimization procedures for optical flow estimation with edge aware filtering. The method includes construction and improvement heuristics, as winner-take-all and parallel local search, and their embedding into a population-based metaheuristic framework. The algorithms are presented and evaluated in comparison to state-of-the-art algorithms
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Vigneron, Vincent. "Programmation par contraintes et découverte de motifs sur données séquentielles." Thesis, Angers, 2017. http://www.theses.fr/2017ANGE0028/document.
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Des travaux récents ont montré l’intérêt de la programmation par contraintes pour la fouille de données. Dans cette thèse, nous nous intéressons à la recherche de motifs sur séquences, et en particulier à la caractérisation, à l’aide de motifs, de classes de séquences pré-établies. Nous proposons à cet effet un langage de modélisation à base de contraintes qui suppose une représentation matricielle du jeu de séquences. Un motif s’y définit comme un ensemble de caractères (ou de patrons) et pour chacun une localisation dans différentes séquences. Diverses contraintes peuvent alors s’appliquer : validité des localisations, couverture d’une classe de séquences, ordre sur les localisations des caractères commun aux séquences, etc. Nous formulons deux problèmes de caractérisation NP-complets : la caractérisation par motif totalement ordonné (e.g. sous-séquence exclusive à une classe) ou partiellement ordonné. Nous en donnons deux modélisations CSP qui intègrent des contraintes globales pour la preuve d’exclusivité. Nous introduisons ensuite un algorithme mémétique pour l’extraction de motifs partiellement ordonnés qui s’appuie sur la résolution CSP lors des phases d’initialisation et d’intensification. Cette approche hybride se révèle plus performante que l’approche CSP pure sur des séquences biologiques. La mise en forme matricielle de jeux de séquences basée sur une localisation des caractères peut être de taille rédhibitoire. Nous proposons donc de localiser des patrons plutôt que des caractères. Nous présentons deux méthodes ad-hoc, l’une basée sur un parcours de treillis et l’autre sur la programmation dynamiqueRecent works have shown the relevance of constraint programming to tackle data mining tasks. This thesis follows this approach and addresses motif discovery in sequential data. We focus in particular, in the case of classified sequences, on the search for motifs that best fit each individual class. We propose a language of constraints over matrix domains to model such problems. The language assumes a preprocessing of the data set (e.g., by pre-computing the locations of each character in each sequence) and views a motif as the choice of a sub-matrix (i.e., characters, sequences, and locations). We introduce different matrix constraints (compatibility of locations with the database, class covering, location-based character ordering common to sequences, etc.) and address two NP-complete problems: the search for class-specific totally ordered motifs (e.g., exclusive subsequences) or partially ordered motifs. We provide two CSP models that rely on global constraints to prove exclusivity. We then present a memetic algorithm that uses this CSP model during initialisation and intensification. This hybrid approach proves competitive compared to the pure CSP approach as shown by experiments carried out on protein sequences. Lastly, we investigate data set preprocessing based on patterns rather than characters, in order to reduce the size of the resulting matrix domain. To this end, we present and compare two alternative methods, one based on lattice search, the other on dynamic programming
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Gach, Olivier. "Algorithmes mémétiques de détection de communautés dans les réseaux complexes : techniques palliatives de la limite de résolution." Phd thesis, Université du Maine, 2013. http://tel.archives-ouvertes.fr/tel-01037937.
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Les réseaux complexes, issus de relevés de terrain d'origines trèsvariées, en biologie, science de l'information ou sociologie,présentent une caractéristique remarquable dénommée structurecommunautaire. Des groupes, ou communautés, à l'intérieur duréseau, ont une cohésion interne forte et des liens entre eux plusfaibles. Sans connaissance a priori du nombre de communautés, ladifficulté réside dans la caractérisation d'un bon partitionnement encommunautés. La modularité est une mesure globale de qualité departitionnement très utilisée qui capture les contraintes de cohésioninterne forte et de liens externes faibles. Elle transforme le problèmede détection de communautés en problème d'optimisationNP-difficile. Elle souffre d'un défaut, la limite de résolution, qui tendà rendre indétectables les très petites communautés d'autant plusque le réseau est grand. L'algorithme le plus efficace pour optimiserla modularité, dit de Louvain, procède par fusion de communautés.Cette thèse s'attache à modifier cet algorithme pour qu'il réalisemajoritairement des fusions pertinentes, qui n'aggravent pas lalimite de résolution, en utilisant une condition de fusion. De plus, enl'associant à un algorithme mémétique, les partitions proposéessont très proches des partitions attendues pour des graphesgénérés par un modèle qui reproduit les caractéristiques desréseaux complexes. Enfin, cet algorithme mémétique réduitfortement l'inconsistance de solution, défaut de la modularité selonlequel deux partitions trouvées à partir d'un examen des noeudsdans un ordre aléatoire, pour le même graphe, peuvent êtrestructurellement très différentes, rendant leur interprétation délicate.
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Nesi, Luan Carlos. "Modelo hipermídia para geração de layouts de interfaces de aplicações." Universidade do Vale do Rio dos Sinos, 2014. http://www.repositorio.jesuita.org.br/handle/UNISINOS/3102.
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Milton Valente
Nesse trabalho foi desenvolvido um modelo computacional de Hipermídia Adaptativa para geração de layouts de interface de aplicações. A pesquisa partiu de uma revisão sobre Hipermídia Adaptativa, com um apanhado sobre os conceitos e características dos métodos e técnicas de adaptação a fim de embasar seu desenvolvimento. Após, avaliou-se o uso das metaheurísticas Algoritmo Genético, Busca Tabu e Algoritmo Memético como as ferramentas de apoio no desenvolvimento do modelo. Na sequência, as Redes de Autômatos Estocásticos nortearam a modelagem do formalismo utilizado para a retenção de conhecimento. Dessas bases, foi desenvolvida a prova de conceito. Conseguinte, apresentam-se os experimentos realizados para validação. Os resultados obtidos pelo modelo foram de boa qualidade, indo ao encontro dos objetivos da pesquisa. Como decorrência deste trabalho, obteve-se um sistema capaz de gerar layouts, contemplando as características dos usuários e seus dispositivos, sendo capaz de acompanhar uma tendência de consumo de conteúdos não só mercadológica, mas também, social.
In this paper was developed a computational model of Adaptive Hypermedia for generation of interface layouts of applications. The research began with a review of Adaptive Hypermedia, with an overview of the concepts and characteristics of the methods and adaptation techniques in order to base its development. After, we evaluated the use of metaheuristic Genetic Algorithm, Tabu Search, and Memetic Algorithm as support tools in the development of the model. Following, the Stochastic Automata Networks guided the modeling of the formalism used for knowledge retention. These bases, the proof of concept were developed. Therefore, we present the experiments to validate. The obtained results by the model were of good quality, meeting the research objectives. As results of this work, we obtained a system capable to generate layouts, considering the characteristics of the users and their devices, being able to follow a trend of content consumption not only marketing, but also social.
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Starke, Sebastian [Verfasser]. "Bio IK: A Memetic Evolutionary Algorithm for Generic Multi-Objective Inverse Kinematics : Bio IK: Ein memetischer evolutionärer Algorithmus für generische inverse Kinematik mit mehreren Zielen / Sebastian Starke." Hamburg : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2020. http://d-nb.info/1221720910/34.
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Silva, Neto Jo?o Saturnino da. "Aplica?a? das t?cnicas Path-relinking e Vocabulary buiding na melhoria de performance do algoritmo mem?tico para o problema do caixeiro viajante assim?trico." Universidade Federal do Rio Grande do Norte, 2009. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17005.
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Previous issue date: 2009-07-10The present essay shows strategies of improvement in a well succeded evolutionary metaheuristic to solve the Asymmetric Traveling Salesman Problem. Such steps consist in a Memetic Algorithm projected mainly to this problem. Basically this improvement applied optimizing techniques known as Path-Relinking and Vocabulary Building. Furthermore, this last one has being used in two different ways, in order to evaluate the effects of the improvement on the evolutionary metaheuristic. These methods were implemented in C++ code and the experiments were done under instances at TSPLIB library, being possible to observe that the procedures purposed reached success on the tests done
O presente trabalho prop?e estrat?gias de melhoria em uma bem sucedida metaheur ?stica evolucionaria para a resolu??o do Problema do Caixeiro Viajante Assim?trico. Tal procedimento consiste em um algoritmo mem?tico projetado especificamente para esse problema. Essas melhorias t?m por base a aplica??o de t?cnicas de otimiza??o conhecidas como Path-Relinking e Vocabulary Building, sendo essa ?ltima t?cnica utilizada de dois modos distintos, com o intuito de avaliar os efeitos de melhoria sobre a metaheur?stica evolucion?ria empregada. Os m?todos propostos foram implementados na linguagem de programa??o C++ e os experimentos computacionais foram realizados sobre inst?ncias disponibilizadas na biblioteca TSPLIB, tornando poss?vel observar que os procedimentos propostos alcan?aram ?xito nos testes realizados
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Silva, Leandro Mengue da. "Um modelo de otimização baseado em algoritmo memético para o escalonamento de ordens de produção utilizando divisão de lotes de tamanho variável." Universidade do Vale do Rio dos Sinos, 2017. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6353.
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Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5)Made available in DSpace on 2017-06-16T12:13:47Z (GMT). No. of bitstreams: 2 Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Leandro Mengue da Silva_.pdf: 1918963 bytes, checksum: 8d329d578b6f3672b670f65fd2f7ea08 (MD5) Previous issue date: 2017-03-23
CNPQ – Conselho Nacional de Desenvolvimento Científico e Tecnológico
A contribuição de metaheurísticas, em especial a dos algoritmos evolutivos, na área de otimização combinatória é de extrema relevância, pois auxiliam na busca de soluções próximas ao ótimo para problemas complexos da vida real cuja resolução em tempo aceitável é inviável devido a sua complexidade computacional, oferecendo uma flexibilidade importante na modelagem do problema. Este trabalho se propõe a apresentar e implementar um modelo computacional a ser utilizado na otimização do escalonamento de ordens de produção utilizando um Algoritmo Memético (AM), que permite a busca tanto da melhor sequência das ordens de produção quanto dos lotes de tamanho variável em que a quantidade de cada operação pode ser subdividida. A possibilidade de utilização de máquinas alternativas, de recursos secundários, de intervalos de indisponibilidade e de lotes de transferência, é apresentada no modelo, o que lhe proporciona grande robustez e aplicabilidade em ambientes de manufatura flexível, permitindo uma modelagem do Flexible Job Shop Scheduling Problem (FJSSP) que reflete com maior fidedignidade a realidade do ambiente fabril, gerando como resultado um escalonamento otimizado e aderente às necessidades da fábrica. Várias instâncias do FJSSP são utilizadas nos testes e os resultados obtidos comprovam que o algoritmo proposto consegue otimizar o escalonamento das ordens de produção de cada instância de maneira eficiente.
The contribution of meta-heuristics, especially evolutionary algorithms, in combinatorial optimization area is extremely important, as they help in finding near optimal solutions to complex real-life problems whose resolution is infeasible in acceptable time due to its computational complexity, offering an important flexibility in the modeling of problem. This study propose to present and implement a computational model to be used in optimizing the production scheduling of manufacturing orders using a Memetic Algorithm that allows to search both the best sequence of jobs as of variable size batches that the quantity of each operation can be subdivided. The possibility of using alternative resources, operations with secondary resources, unavailability intervals and batch transfer lots are features presented in the model, which lends it great robustness and applicability to flexible manufacturing environments, allowing the modeling of Flexible Job Shop Scheduling Problem (FJSSP) that reflects with higher accuracy the real manufacturing environment, generating optimized scheduling results that are adhering to the plant needs. Multiple instances of FJSSP are used in the tests and the results show that the proposed algorithm succeeds in optimizing the scheduling of production orders for each instance so efficient.
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Ferreira, Vanessa Danielle Santos. "Um algoritmo h?brido para o problema de roteamento de ve?culos com frotas heterog?neas." Universidade Federal do Rio Grande do Norte, 2011. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15007.
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Previous issue date: 2011-07-13Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico
This paper aims to propose a hybrid meta-heuristics for the Heterogeneous Fleet Vehicle Routing Problem (HVRP), which is a combinatorial optimization problem NP-hard, and is characterized by the use of a limited fleet consists of different vehicles with different capacities. The hybrid method developed makes use of a memetic algorithm associated with the component optimizer Vocabulary Building. The resulting hybrid meta-heuristic was implemented in the programming language C + + and computational experiments generated good results in relation to meta-heuristic applied in isolation, proving the efficiency of the proposed method.
O presente trabalho visa propor uma meta-heur?stica h?brida para o Problema de Roteamento de Ve?culos com Frotas Heterog?neas (PRVFH), que ? um problema de otimiza??o combinat?ria NP-dif?cil, e que se caracteriza pelo uso de uma frota limitada composta por ve?culos distintos com capacidades distintas. O m?todo h?brido desenvolvido utiliza-se de um algoritmo mem?tico associado ao componente otimizador Vocabulary Building. A meta-heur?stica h?brida resultante foi implementada na linguagem de programa??o C++ e os experimentos computacionais geraram bons resultados em rela??o ? meta-heur?stica aplicada isoladamente, comprovando a efici?ncia do m?todo proposto.
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Fontes, F?bio Francisco da Costa. "Algoritmo mem?tico com infec??o viral: uma aplica??o ao problema do caixeiro viajante assim?trico." Universidade Federal do Rio Grande do Norte, 2006. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15107.
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Previous issue date: 2006-05-19The Combinatorial Optimization is a basic area to companies who look for competitive advantages in the diverse productive sectors and the Assimetric Travelling Salesman Problem, which one classifies as one of the most important problems of this area, for being a problem of the NP-hard class and for possessing diverse practical applications, has increased interest of researchers in the development of metaheuristics each more efficient to assist in its resolution, as it is the case of Memetic Algorithms, which is a evolutionary algorithms that it is used of the genetic operation in combination with a local search procedure. This work explores the technique of Viral Infection in one Memetic Algorithms where the infection substitutes the mutation operator for obtaining a fast evolution or extinguishing of species (KANOH et al, 1996) providing a form of acceleration and improvement of the solution . For this it developed four variants of Viral Infection applied in the Memetic Algorithms for resolution of the Assimetric Travelling Salesman Problem where the agent and the virus pass for a symbiosis process which favored the attainment of a hybrid evolutionary algorithms and computational viable
A Otimiza??o Combinat?ria ? uma ?rea fundamental para empresas que buscam vantagens competitivas nos diversos setores produtivos, e o Problema do Caixeiro Viajante Assim?trico, o qual se classifica como um dos mais importantes problemas desta ?rea, devido a ser um problema da classe NP-dif?cil e tamb?m por possuir diversas aplica??es pr?ticas, tem despertado interesse de pesquisadores no desenvolvimento de Metaheur?sticas cada vez mais eficientes para auxiliar na sua resolu??o, como ? o caso do Algoritmo Mem?tico, o qual ? um algoritmo evolutivo que se utiliza dos operadores gen?ticos em combina??o com um procedimento de busca local. Este trabalho explora a t?cnica de Infec??o Viral em um Algoritmo Mem?tico, onde a infec??o substitui o operador de muta??o por conseguir uma r?pida evolu??o ou extin??o de esp?cies (KANOH et al., 1996), proporcionando uma forma de acelera??o e melhoria da solu??o. Para isto se desenvolveu quatro variantes de Infec??o Viral aplicadas no Algoritmo Mem?tico para resolu??o do Problema do Caixeiro Viajante Assim?trico, onde o agente e o v?rus passam por um processo de Simbiose, as quais favoreceram a obten??o de um algoritmo evolutivo h?brido e computacionalmente vi?vel
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Dang, Vinh Q. "Evolutionary approaches for feature selection in biological data." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2014. https://ro.ecu.edu.au/theses/1276.
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Data mining techniques have been used widely in many areas such as business, science, engineering and medicine. The techniques allow a vast amount of data to be explored in order to extract useful information from the data. One of the foci in the health area is finding interesting biomarkers from biomedical data. Mass throughput data generated from microarrays and mass spectrometry from biological samples are high dimensional and is small in sample size. Examples include DNA microarray datasets with up to 500,000 genes and mass spectrometry data with 300,000 m/z values. While the availability of such datasets can aid in the development of techniques/drugs to improve diagnosis and treatment of diseases, a major challenge involves its analysis to extract useful and meaningful information. The aims of this project are: 1) to investigate and develop feature selection algorithms that incorporate various evolutionary strategies, 2) using the developed algorithms to find the “most relevant” biomarkers contained in biological datasets and 3) and evaluate the goodness of extracted feature subsets for relevance (examined in terms of existing biomedical domain knowledge and from classification accuracy obtained using different classifiers). The project aims to generate good predictive models for classifying diseased samples from control.
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Ahmad, Maqsood. "Mathematical models and methods based on metaheuristic approach for timetabling problem." Thesis, Clermont-Ferrand 2, 2013. http://www.theses.fr/2013CLF22393/document.
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Résumé indisponibleIn this thesis we have concerned ourselves with university timetabling problems both course timetabling and examination timetabling problems. Most of the timetabling problems are computationally NP-complete problems, which means that the amount of computation required to find solutions increases exponentially with problem size. These are idiosyncratic nature problems, for example different universities have their own set of constraints, their own definition of good timetable, feasible timetable and their own choice about the use of constraint type (as a soft or hard constraint). Unfortunately, it is often the case that a problem solving approach which is successfully applied for one specific problem may not become suitable for others. This is a motivation, we propose a generalized problem which covers many constraints used in different universities or never used in literature. Many university timetabling problems are sub problems of this generalized problem. Our proposed algorithms can solve these sub problems easily, moreover constraints can be used according to the desire of user easily because these constraints can be used as reference to penalty attached with them as well. It means that give more penalty value to hard constraints than soft constraint. Thus more penalty value constraints are dealt as a hard constraint by algorithm. Our algorithms can also solve a problem in two phases with little modification, where in first phase hard constraints are solved. In this work we have preferred and used two phase technique to solve timetabling problems because by using this approach algorithms have broader search space in first phase to satisfy hard constraints while not considering soft constraints at all. Two types of algorithms are used in literature to solve university timetabling problem, exact algorithms and approximation algorithms. Exact algorithms are able to find optimal solution, however in university timetabling problems exact algorithms constitute brute-force style procedures. And because these problems have the exponential growth rates of the search spaces, thus these kinds of algorithms can be applied for small size problems. On the other side, approximation algorithms may construct optimal solution or not but they can produce good practically useable solutions. Thus due to these factors we have proposed approximation algorithms to solve university timetabling problem. We have proposed metaheuristic based techniques to solve timetabling problem, thus we have mostly discussed metaheuristic based algorithms such as evolutionary algorithms, simulated annealing, tabu search, ant colony optimization and honey bee algorithms. These algorithms have been used to solve many other combinatorial optimization problems other than timetabling problem by modifying a general purpose algorithmic framework. We also have presented a bibliography of linear integer programming techniques used to solve timetabling problem because we have formulated linear integer programming formulations for our course and examination timetabling problems. We have proposed two stage algorithms where hard constraints are satisfied in first phase and soft constraints in second phase. The main purpose to use this two stage technique is that in first phase hard constraints satisfaction can use more relax search space because in first phase it does not consider soft constraints. In second phase it tries to satisfy soft constraints when maintaining hard constraints satisfaction which are already done in first phase. (...)
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Moalic, Laurent. "Modélisation dynamique de la densité de population via les réseaux cellulaires et optimisation multiobjectif de l'auto-partage." Thesis, Besançon, 2013. http://www.theses.fr/2013BESA2051/document.
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De nombreux problèmes de décision issus du monde réel sont de nature NP-difficile. Il est également fréquent que de tels problèmes rassemblent plusieurs objectifs à optimiser simultanément, généralement contradictoires entre eux. Pour aborder cette classe de problèmes, les métaheuristiques multiobjectifs fournissent des outils particulièrement efficaces. Par ailleurs, pour traiter des problèmes de transport, l'élaboration de modèles permettant de caractériser l’évolution spatio-temporelle d’une population est un élément essentiel. Dans le cadre de ces travaux, nous nous intéressons à la chaine complète qui permet de guider une décision dans le domaine de l'aménagement du territoire et du transport. Nous considérons ainsi les deux principales phases impliquées dans le processus de décision : la modélisation des déplacements de la population d'une part, et l'élaboration d'une métaheuristique hybride pour résoudre des problèmes d'optimisation multiobjectif d'autre part. Afin de modéliser l’évolution de la présence de personnes sur un territoire, nous proposons dans cette thèse un nouveau modèle de mobilité. L'originalité de ce travail réside dans l'utilisation de données nouvelles issues de la téléphonie mobile, ainsi que dans l'exploitation d'informations géographiques et socio-économiques pour caractériser le pouvoir d'attraction du territoire. Nous proposons par ailleurs une heuristique pour résoudre des problèmes multiobjectifs. L’étude de l'influence de différents opérateurs sur la construction de l'ensemble Pareto, nous a amené à concevoir une heuristique hybride de type mémétique, qui se révèle être significativement plus efficace que des approches de référence. Les deux principales phases, modélisation et optimisation, ont été expérimentées et validées dans un contexte réel. Elles ont donné lieu au développement d’une plate-forme logicielle d’aide à la décision utilisée notamment pour proposer des emplacements de stations pour un service d'auto-partage électriqueMany decision-making problems in the real world are NP-hard. These problems commonly feature several mutually-contradictory objectives to be optimized simultaneously. Multiobjective metaheuristics provide particularly effective means of addressing this class of problems. Moreover, for transportation problems, the development of models able to evaluate the spatiotemporal evolution of a population is essential. In our research, we are interested in the complete chain guiding a decision in the fields of transportation and territory planning. We consider the two main phases involved in the decision-making process: building a population mobility model and developing a hybrid metaheuristic to solve multiobjective optimization problems. In order to compute the evolution of population presence on a territory, in this thesis we propose a new mobility model; its originality lies in employing new data from mobile phone networks as well as geographic and socio-economic information to indicate the attractiveness of the territory. We have also developed a heuristic to solve multiobjective problems: following the study of the influence of several operators on the Pareto front, we have designed a hybrid memetic heuristic that is significantly more effective than reference approaches. The two main phases of modelling and optimizing have been tested and validated in a real context, allowing us to develop a decision-making software platform that can be used to provide station locations for an electric car-sharing service
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Oliveira, Camila Nascimento de. "Uma investiga??o de algoritmos exatos e metaheur?sticos aplicados ao nonograma." Universidade Federal do Rio Grande do Norte, 2013. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18081.
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Previous issue date: 2013-02-01Nonogram is a logical puzzle whose associated decision problem is NP-complete. It has applications in pattern recognition problems and data compression, among others. The puzzle consists in determining an assignment of colors to pixels distributed in a N M matrix that satisfies line and column constraints. A Nonogram is encoded by a vector whose elements specify the number of pixels in each row and column of a figure without specifying their coordinates. This work presents exact and heuristic approaches to solve Nonograms. The depth first search was one of the chosen exact approaches because it is a typical example of brute search algorithm that is easy to implement. Another implemented exact approach was based on the Las Vegas algorithm, so that we intend to investigate whether the randomness introduce by the Las Vegas-based algorithm would be an advantage over the depth first search. The Nonogram is also transformed into a Constraint Satisfaction Problem. Three heuristics approaches are proposed: a Tabu Search and two memetic algorithms. A new function to calculate the objective function is proposed. The approaches are applied on 234 instances, the size of the instances ranging from 5 x 5 to 100 x 100 size, and including logical and random Nonograms
O Nonograma ? um jogo l?gico cujo problema de decis?o associado ? NP-completo. Ele possui aplica??o em problemas de identifica??o de padr?es e de compacta??o de dados, dentre outros. O jogo consiste em determinar uma aloca??o de cores em pixels distribu?dos em uma matriz N M atendendo restri??es em linhas e colunas. Um Nonograma ? codificado atrav?s de vetores cujos elementos especificam o n?mero de pixels existentes em cada coluna e linha de uma figura, sem especificar suas coordenadas. Este trabalho apresenta abordagens exatas e heur?sticas para solucionar o Nonograma. A Busca em Profundidade foi uma das abordagens exatas escolhida, por ser um exemplo t?pico de algoritmo de for?a bruta de f?cil implementa??o. Outra abordagem exata implementada foi baseada no algoritmo Las Vegas, atrav?s do qual se pretende investigar se a aleatoriedade introduzida pelo algoritmo Las Vegas traria algum benef?cio em rela??o ? Busca em Profundidade. O Nonograma tamb?m ? transformado em um Problema de Satisfa??o de Restri??es. Tr?s abordagens heur?sticas s?o propostas: uma Busca Tabu e dois algoritmos Mem?tico. Uma nova abordagem para o c?lculo da fun??o objetivo ? proposta neste trabalho. As abordagens s?o testadas em 234 casos de teste de tamanho entre 5 x 5 e 100 x 100, incluindo Nonogramas l?gicos e aleat?rios
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Larabi, Mohand. "Le problème de job-shop avec transport : modélisation et optimisation." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2010. http://tel.archives-ouvertes.fr/tel-00625528.
Full textAbstract:
Dans cette thèse nous nous sommes intéressés à l'extension du problème job-shop en ajoutant la contrainte du transport des jobs entre les différentes machines. Dans cette étude nous avons retenu l'existence de deux types de robots, les robots de capacité de chargement unitaire (capacité=1 veut dire qu'un robot ne peut transporter qu'un seul job à la fois) et les robots de capacité de chargement non unitaire (capacité>1 veut dire qu'un robot peut transporter plusieurs job à la fois). Nous avons traité cette extension en deux étapes. Ainsi, la première étape est consacrée au problème du job-shop avec plusieurs robots de capacité de chargement unitaire et en seconde étape en ajoutant la capacité de chargement non unitaire aux robots. Pour les deux problèmes étudiés nous avons proposé :* Une modélisation linéaire ;* Une modélisation sous forme de graphe disjonctif ;* Plusieurs heuristiques de construction de solutions ;* Plusieurs recherches locales qui améliorent les solutions obtenues ;* Utilisation des algorithmes génétiques / mémétiques comme schéma global d'optimisation ;* De nouveaux benchmarks, des résultats de test de nos approches sur nos benchmarks et ceux de la littérature et ces résultats sont commentés et comparés à ceux de la littérature. Les résultats obtenus montrent la pertinence de notre modélisation ainsi que sa qualité.