920 resultados para Simulated annealing acoplado. Metaheurística. Eficiência paralela. Escalabilidade paralela
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Em sistemas híbridos de geração de eletricidade (SHGEs) é fundamental avaliar corretamente o dimensionamento, a operação e a gestão do sistema, de forma a evitar seu colapso prematuro e garantir a continuidade do fornecimento de energia elétrica com a menor intervenção possível de usuários ou de empresas geradoras e distribuidoras de eletricidade. O presente trabalho apresenta propostas de otimização para as etapas de dimensionamento, operação e gestão de SHGEs atendendo minirredes de distribuição de eletricidade. É proposta uma estratégia de operação que visa otimizar o despacho de energia do sistema, identificando a melhor relação, sob aspectos técnicos e econômicos, entre o atendimento da carga exclusivamente via fontes renováveis e banco de baterias ou exclusivamente via grupo gerador, e o carregamento do banco de baterias somente pelas fontes renováveis ou também pelo grupo gerador. Desenvolve-se, também, um algoritmo de dimensionamento de SHGEs, com auxílio de algoritmos genéticos e simulated annealing, técnicas meta-heurísticas de otimização, visando apresentar a melhor configuração do sistema, em termos de equipamentos que resultem na melhor viabilidade técnica e econômica para uma dada condição de entrada definida pelo usuário. Por fim, é proposto um modelo de gestão do sistema, considerando formas de tarifação e sistemas de controle de carga, cujo objetivo é garantir uma relação adequada entre a disponibilidade energética do sistema de geração e a carga demandada. A estratégia de operação proposta combina as estratégias de operação descontínua do grupo gerador, da potência crítica e do ponto otimizado de contribuição do gerador no carregamento do banco de baterias, e seus resultados indicam que há redução nos custos de operação globais do sistema. Com relação ao dimensionamento ótimo, o algoritmo proposto, em comparação a outras ferramentas de otimização de SHGEs, apresenta bons resultados, sendo adequado à realidade nacional. O modelo de gestão do sistema propõe o estabelecimento de limites de consumo e demanda, adequados à realidade de comunidades isoladas atendidas por sistemas com fontes renováveis e, se corretamente empregados, podem ajudar a garantir a sustentabilidade dos sistemas.
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Pós-graduação em Química - IQ
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The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.
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This paper applies a genetic algorithm with hierarchically structured population to solve unconstrained optimization problems. The population has individuals distributed in several overlapping clusters, each one with a leader and a variable number of support individuals. The hierarchy establishes that leaders must be fitter than its supporters with the topological organization of the clusters following a tree. Computational tests evaluate different population structures, population sizes and crossover operators for better algorithm performance. A set of known benchmark test problems is solved and the results found are compared with those obtained from other methods described in the literature, namely, two genetic algorithms, a simulated annealing, a differential evolution and a particle swarm optimization. The results indicate that the method employed is capable of achieving better performance than the previous approaches in regard as the two criteria usually employed for comparisons: the number of function evaluations and rate of success. The method also has a superior performance if the number of problems solved is taken into account. (C) 2013 Elsevier B.V. All rights reserved.
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Pós-graduação em Engenharia Mecânica - FEG
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Pós-graduação em Ciência e Tecnologia de Materiais - FC
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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A radial basis function network (RBFN) circuit for function approximation is presented. Simulation and experimental results show that the network has good approximation capabilities. The RBFN was a squared hyperbolic secant with three adjustable parameters amplitude, width and center. To test the network a sinusoidal and sine function,vas approximated.
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This paper presents a mathematical model adapted from literature for the crop rotation problem with demand constraints (CRP-D). The main aim of the present work is to study metaheuristics and their performance in a real context. The proposed algorithms for solution of the CRP-D are a genetic algorithm, a simulated annealing and hybrid approaches: a genetic algorithm with simulated annealing and a genetic algorithm with local search algorithm. A new constructive heuristic was also developed to provide initial solutions for the metaheuristics. Computational experiments were performed using a real planting area and semi-randomly generated instances created by varying the number, positions and dimensions of the lots. The computational results showed that these algorithms determined good feasible solutions in a short computing time as compared with the time spent to get optimal solutions, thus proving their efficacy for dealing with this practical application of the CRP-D.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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The emergence of wavelength-division multiplexing (WDM) technology provides the capability for increasing the bandwidth of synchronous optical network (SONET) rings by grooming low-speed traffic streams onto different high-speed wavelength channels. Since the cost of SONET add–drop multiplexers (SADM) at each node dominates the total cost of these networks, how to assign the wavelength, groom the traffic, and bypass the traffic through the intermediate nodes has received a lot of attention from researchers recently. Moreover, the traffic pattern of the optical network changes from time to time. How to develop dynamic reconfiguration algorithms for traffic grooming is an important issue. In this paper, two cases (best fit and full fit) for handling reconfigurable SONET over WDM networks are proposed. For each approach, an integer linear programming model and heuristic algorithms (TS-1 and TS-2, based on the tabu search method) are given. The results demonstrate that the TS-1 algorithm can yield better solutions but has a greater running time than the greedy algorithm for the best fit case. For the full fit case, the tabu search heuristic yields competitive results compared with an earlier simulated annealing based method and it is more stable for the dynamic case.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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The irregular shape packing problem is approached. The container has a fixed width and an open dimension to be minimized. The proposed algorithm constructively creates the solution using an ordered list of items and a placement heuristic. Simulated annealing is the adopted metaheuristic to solve the optimization problem. A two-level algorithm is used to minimize the open dimension of the container. To ensure feasible layouts, the concept of collision free region is used. A collision free region represents all possible translations for an item to be placed and may be degenerated. For a moving item, the proposed placement heuristic detects the presence of exact fits (when the item is fully constrained by its surroundings) and exact slides (when the item position is constrained in all but one direction). The relevance of these positions is analyzed and a new placement heuristic is proposed. Computational comparisons on benchmark problems show that the proposed algorithm generated highly competitive solutions. Moreover, our algorithm updated some best known results. (C) 2012 Elsevier Ltd. All rights reserved.