446 resultados para Computação evolucionária

em Universidade Federal do Rio Grande do Norte(UFRN)


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The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

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The Car Rental Salesman Problem (CaRS) is a variant of the classical Traveling Salesman Problem which was not described in the literature where a tour of visits can be decomposed into contiguous paths that may be performed in different rental cars. The aim is to determine the Hamiltonian cycle that results in a final minimum cost, considering the cost of the route added to the cost of an expected penalty paid for each exchange of vehicles on the route. This penalty is due to the return of the car dropped to the base. This paper introduces the general problem and illustrates some examples, also featuring some of its associated variants. An overview of the complexity of this combinatorial problem is also outlined, to justify their classification in the NPhard class. A database of instances for the problem is presented, describing the methodology of its constitution. The presented problem is also the subject of a study based on experimental algorithmic implementation of six metaheuristic solutions, representing adaptations of the best of state-of-the-art heuristic programming. New neighborhoods, construction procedures, search operators, evolutionary agents, cooperation by multi-pheromone are created for this problem. Furtermore, computational experiments and comparative performance tests are conducted on a sample of 60 instances of the created database, aiming to offer a algorithm with an efficient solution for this problem. These results will illustrate the best performance reached by the transgenetic algorithm in all instances of the dataset

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The distribution of petroleum products through pipeline networks is an important problem that arises in production planning of refineries. It consists in determining what will be done in each production stage given a time horizon, concerning the distribution of products from source nodes to demand nodes, passing through intermediate nodes. Constraints concerning storage limits, delivering time, sources availability, limits on sending or receiving, among others, have to be satisfied. This problem can be viewed as a biobjective problem that aims at minimizing the time needed to for transporting the set of packages through the network and the successive transmission of different products in the same pipe is called fragmentation. This work are developed three algorithms that are applied to this problem: the first algorithm is discrete and is based on Particle Swarm Optimization (PSO), with local search procedures and path-relinking proposed as velocity operators, the second and the third algorithms deal of two versions based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed algorithms are compared to other approaches for the same problem, in terms of the solution quality and computational time spent, so that the efficiency of the developed methods can be evaluated

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The Multiobjective Spanning Tree is a NP-hard Combinatorial Optimization problem whose application arises in several areas, especially networks design. In this work, we propose a solution to the biobjective version of the problem through a Transgenetic Algorithm named ATIS-NP. The Computational Transgenetic is a metaheuristic technique from Evolutionary Computation whose inspiration relies in the conception of cooperation (and not competition) as the factor of main influence to evolution. The algorithm outlined is the evolution of a work that has already yielded two other transgenetic algorithms. In this sense, the algorithms previously developed are also presented. This research also comprises an experimental analysis with the aim of obtaining information related to the performance of ATIS-NP when compared to other approaches. Thus, ATIS-NP is compared to the algorithms previously implemented and to other transgenetic already presented for the problem under consideration. The computational experiments also address the comparison to two recent approaches from literature that present good results, a GRASP and a genetic algorithms. The efficiency of the method described is evaluated with basis in metrics of solution quality and computational time spent. Considering the problem is within the context of Multiobjective Optimization, quality indicators are adopted to infer the criteria of solution quality. Statistical tests evaluate the significance of results obtained from computational experiments

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The Hiker Dice was a game recently proposed in a software designed by Mara Kuzmich and Leonardo Goldbarg. In the game a dice is responsible for building a trail on an n x m board. As the dice waits upon a cell on the board, it prints the side that touches the surface. The game shows the Hamiltonian Path Problem Simple Maximum Hiker Dice (Hidi-CHS) in trays Compact Nth , this problem is then characterized by looking for a Hamiltonian Path that maximize the sum of marked sides on the board. The research now related, models the problem through Graphs, and proposes two classes of solution algorithms. The first class, belonging to the exact algorithms, is formed by a backtracking algorithm planed with a return through logical rules and limiting the best found solution. The second class of algorithms is composed by metaheuristics type Evolutionary Computing, Local Ramdomized search and GRASP (Greed Randomized Adaptative Search). Three specific operators for the algorithms were created as follows: restructuring, recombination with two solutions and random greedy constructive.The exact algorithm was teste on 4x4 to 8x8 boards exhausting the possibility of higher computational treatment of cases due to the explosion in processing time. The heuristics algorithms were tested on 5x5 to 14x14 boards. According to the applied methodology for evaluation, the results acheived by the heuristics algorithms suggests a better performance for the GRASP algorithm

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The Scientific Algorithms are a new metaheuristics inspired in the scientific research process. The new method introduces the idea of theme to search the solution space of hard problems. The inspiration for this class of algorithms comes from the act of researching that comprises thinking, knowledge sharing and disclosing new ideas. The ideas of the new method are illustrated in the Traveling Salesman Problem. A computational experiment applies the proposed approach to a new variant of the Traveling Salesman Problem named Car Renter Salesman Problem. The results are compared to state-of-the-art algorithms for the latter problem

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This paper introduces a new variant of the Traveling Car Renter Problem, named Prizecollecting Traveling Car Renter Problem. In this problem, a set of vertices, each associated with a bonus, and a set of vehicles are given. The objective is to determine a cycle that visits some vertices collecting, at least, a pre-defined bonus, and minimizing the cost of the tour that can be traveled with different vehicles. A mathematical formulation is presented and implemented in a solver to produce results for sixty-two instances. The proposed problem is also subject of an experimental study based on the algorithmic application of four metaheuristics representing the best adaptations of the state of the art of the heuristic programming.We also provide new local search operators which exploit the neighborhoods of the problem, construction procedures and adjustments, created specifically for the addressed problem. Comparative computational experiments and performance tests are performed on a sample of 80 instances, aiming to offer a competitive algorithm to the problem. We conclude that memetic algorithms, computational transgenetic and a hybrid evolutive algorithm are competitive in tests performed

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The progresses of the Internet and telecommunications have been changing the concepts of Information Technology IT, especially with regard to outsourcing services, where organizations seek cost-cutting and a better focus on the business. Along with the development of that outsourcing, a new model named Cloud Computing (CC) evolved. It proposes to migrate to the Internet both data processing and information storing. Among the key points of Cloud Computing are included cost-cutting, benefits, risks and the IT paradigms changes. Nonetheless, the adoption of that model brings forth some difficulties to decision-making, by IT managers, mainly with regard to which solutions may go to the cloud, and which service providers are more appropriate to the Organization s reality. The research has as its overall aim to apply the AHP Method (Analytic Hierarchic Process) to decision-making in Cloud Computing. There to, the utilized methodology was the exploratory kind and a study of case applied to a nationwide organization (Federation of Industries of RN). The data collection was performed through two structured questionnaires answered electronically by IT technicians, and the company s Board of Directors. The analysis of the data was carried out in a qualitative and comparative way, and we utilized the software to AHP method called Web-Hipre. The results we obtained found the importance of applying the AHP method in decision-making towards the adoption of Cloud Computing, mainly because on the occasion the research was carried out the studied company already showed interest and necessity in adopting CC, considering the internal problems with infrastructure and availability of information that the company faces nowadays. The organization sought to adopt CC, however, it had doubt regarding the cloud model and which service provider would better meet their real necessities. The application of the AHP, then, worked as a guiding tool to the choice of the best alternative, which points out the Hybrid Cloud as the ideal choice to start off in Cloud Computing. Considering the following aspects: the layer of Infrastructure as a Service IaaS (Processing and Storage) must stay partly on the Public Cloud and partly in the Private Cloud; the layer of Platform as a Service PaaS (Software Developing and Testing) had preference for the Private Cloud, and the layer of Software as a Service - SaaS (Emails/Applications) divided into emails to the Public Cloud and applications to the Private Cloud. The research also identified the important factors to hiring a Cloud Computing provider

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth

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Hospital Automation is an area that is constantly growing. The emergency of new technologies and hardware is transforming the processes more efficient. Nevertheless, some of the hospital processes are still being performed manually, such as monitoring of patients that is considered critical because it involves human lives. One of the factors that should be taken into account during a monitoring is the agility to detect any abnormality in vital signs of patients, as well as warning of this anomaly to the medical team involved. So, this master's thesis aims to develop an architecture to automate this process of monitoring and reporting of possible alert to a professional, so that emergency care can be done effectively. The computing mobile was used to improve the communication by distributing messages between a central located into the hospital and the mobile carried by the duty

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One of the current challenges of Ubiquitous Computing is the development of complex applications, those are more than simple alarms triggered by sensors or simple systems to configure the environment according to user preferences. Those applications are hard to develop since they are composed by services provided by different middleware and it is needed to know the peculiarities of each of them, mainly the communication and context models. This thesis presents OpenCOPI, a platform which integrates various services providers, including context provision middleware. It provides an unified ontology-based context model, as well as an environment that enable easy development of ubiquitous applications via the definition of semantic workflows that contains the abstract description of the application. Those semantic workflows are converted into concrete workflows, called execution plans. An execution plan consists of a workflow instance containing activities that are automated by a set of Web services. OpenCOPI supports the automatic Web service selection and composition, enabling the use of services provided by distinct middleware in an independent and transparent way. Moreover, this platform also supports execution adaptation in case of service failures, user mobility and degradation of services quality. The validation of OpenCOPI is performed through the development of case studies, specifically applications of the oil industry. In addition, this work evaluates the overhead introduced by OpenCOPI and compares it with the provided benefits, and the efficiency of OpenCOPI s selection and adaptation mechanism

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In this work will applied the technique of Differential Cryptanalysis, introduced in 1990 by Biham and Shamir, on Papílio s cryptosystem, developed by Karla Ramos, to test and most importantly, to prove its relevance to other block ciphers such as DES, Blowfish and FEAL-N (X). This technique is based on the analysis of differences between plaintext and theirs respective ciphertext, in search of patterns that will assist in the discovery of the subkeys and consequently in the discovery of master key. These differences are obtained by XOR operations. Through this analysis, in addition to obtaining patterns of Pap´ılio, it search to obtain also the main characteristics and behavior of Papilio throughout theirs 16 rounds, identifying and replacing when necessary factors that can be improved in accordance with pre-established definitions of the same, thus providing greater security in the use of his algoritm

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With the advance of the Cloud Computing paradigm, a single service offered by a cloud platform may not be enough to meet all the application requirements. To fulfill such requirements, it may be necessary, instead of a single service, a composition of services that aggregates services provided by different cloud platforms. In order to generate aggregated value for the user, this composition of services provided by several Cloud Computing platforms requires a solution in terms of platforms integration, which encompasses the manipulation of a wide number of noninteroperable APIs and protocols from different platform vendors. In this scenario, this work presents Cloud Integrator, a middleware platform for composing services provided by different Cloud Computing platforms. Besides providing an environment that facilitates the development and execution of applications that use such services, Cloud Integrator works as a mediator by providing mechanisms for building applications through composition and selection of semantic Web services that take into account metadata about the services, such as QoS (Quality of Service), prices, etc. Moreover, the proposed middleware platform provides an adaptation mechanism that can be triggered in case of failure or quality degradation of one or more services used by the running application in order to ensure its quality and availability. In this work, through a case study that consists of an application that use services provided by different cloud platforms, Cloud Integrator is evaluated in terms of the efficiency of the performed service composition, selection and adaptation processes, as well as the potential of using this middleware in heterogeneous computational clouds scenarios

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This work aims to understand how cloud computing contextualizes the IT government and decision agenda, in the light of the multiple streams model, considering the current status of public IT policies, the dynamics of the agenda setting for the area, the interface between the various institutions, and existing initiatives on the use of cloud computing in government. Therefore, a qualitative study was conducted through interviews with a group of policy makers and the other group consists of IT managers. As analysis technique, this work made use of content analysis and analysis of documents, with some results by word cloud. As regards the main results to overregulation to the area, usually scattered in various agencies of the federal government, which hinders the performance of the managers. Identified a lack of knowledge of standards, government programs, regulations and guidelines. Among these he highlighted a lack of understanding of the TI Maior Program, the lack of effectiveness of the National Broadband Plan in view of the respondents, as well as the influence of Internet Landmark as an element that can jam the advances in the use of computing cloud in the Brazilian government. Also noteworthy is the bureaucratization of the acquisition of goods to IT services, limited, in many cases, technological advances. Regarding the influence of the actors, it was not possible to identify the presence of a political entrepreneur, and it was noticed a lack of political force. Political flow was affected only by changes within the government. Fragmentation was a major factor for the theme of weakening the agenda formation. Information security was questioned by the respondents pointed out that the main limitation coupled with the lack of training of public servants. In terms of benefits, resource economy is highlighted, followed by improving efficiency. Finally, the discussion about cloud computing needs to advance within the public sphere, whereas the international experience is already far advanced, framing cloud computing as a responsible element for the improvement of processes, services and economy of public resources