Navegando por Assunto "Bioinspired computation (BIC)"

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Acesso aberto (Open Access)
Uma nova solução para a otimização do despacho econômico e ambiental utilizando metaheurísticas da computação bio-inspirada
(Universidade Federal do Pará, 2016) NASCIMENTO, Manoel Henrique Reis; NUNES, Marcus Vinícius Alves; http://lattes.cnpq.br/9533143193581447
Due to the significant industrial growth in the North of Brazil, especially at the Industrial Pole of Manaus (PIM), it has been an increased necessity for energy generation, which in this region is provided by thermoelectric plants (UTEs) in over 90% of its total. Thus, it became necessary the use of computational tools that help the specialists or operators of electrical systems, for making decisions about the optimal power dispatch of each generating unit that contemplate not only to reduce costs but also reduce the atmospheric pollution levels. Optimization of Economic Dispatch (ED) is one of the oldest and most important tasks in power plant management, and currently, due to growing concerns about the environment, this problem is extended to the optimization of the Economic and Environmental Dispatch (EAD). This thesis has as main objective to analyze a new proposal to solve the old optimization problem of ED and the EAD implemented by several Deterministic methods (Iteration Lambda, Quadratic Programming and Newton method) and Heuristic methods (Genetic Algorithms, Particle Swarm, Differential evolution, Simulated Annealing, Optimization by Grey Wolf and Artificial Bee Colonies) for the ED problem. Non-dominated Sorting Genetic Algorithms (NSGA II and NSGA III), were used for evaluating the problem of EAD, considering the shutdown of the generators with higher losses and thus reducing the fuel cost. The method of incremental cost and transmission losses are used to determine the best active power values for each generating unit. It was ensured the energy balance between the total generated power, the demand of the electrical system, losses and minimizing, on the other hand, the total cost of fuel, reducing emissions, and further improving efficiency not only for generators but also to UTE as a whole. Consequently, the proposed new solution has the following contributions: contemplates the turning off generation systems that have higher fuel cost, reducing the overall costs and enabling predictive maintenance on these machines. This approach also determines optimal solutions for the power output in various scenarios characteristic and not characteristic of UTEs or power plants, considering changes in active power generation and reducing greenhouse gas emissions as NOx and CO2. To explore the feasibility of the new solution proposed by this theory, it was used as a test system a set of ten (10) generating units for the case study and three sets of generators´ parameters described in the literature. They were used for demonstrating the robustness of the proposed solution considering the use of various deterministic and Bioinspired computing methods for mono-objective and multi-objective optimization. The results presented here, from an analysis of several practical examples show the advantages of the new proposed solution.
Acesso aberto (Open Access)
Projeto e síntese de superfície seletiva de frequências para o padrão IEEE 802.15.3C via técnica de otimização híbrida multiobjetivo de alta precisão
(Universidade Federal do Pará, 2019-12-19) MOTA, Raimundo José Santos; CAVALCANTE, Gervásio Protásio dos Santos; http://lattes.cnpq.br/2265948982068382
Artficial Neural Networks (ANN) are inspired by the structure and functional aspects in biological neural networks. They are trained through mechanisms obtained from the physical properties of the processes involved, for example, electromagnetic waves. From the knowledge acquired through that experience and learning, they may be able to provide solutions for predicting users behavior and providing, within a region of interest, accurate strategy data for projects and sizing. Those who criticized the application of ANN acquired by nature-inspired algorithms, argued that the problems to be faced were usually without complexities, although the conventional methods that were proposed to solve these same problems were not eficient. Some spurious successes have occurred in certain well-behaved environments, but without_exibility when encountering diverse constraints. Adding to these developments, there is the evolutionary openness of computational tools, which has given extraordinary support for deepening techniques to solve and optimize previously unthought problems. In many optimization issues, the quality of a solution is defined by its performance against several conficting goals. Such coficting objectives cannot be signi_cantly reduced to a single value, for example using a weighted sum or other methodology, but must be considered independently of each other. To achieve accurate solutions with reduced computational costs and shorter processing times, we present the Multi-Objective Evolutionary Algorithms (MOEA), as well as Bioinspired Computation (BIC). Combining the advantages of the classical algorithms, the Metaheuristic Algorithms emerged irreversibly. In many optimization problems, the quality of a solution is defined by its performance in relation to several, coficting objectives. Such conficting goals cannot be sensibly reduced to a single value using a weighted sum or another aggregate function, but rather they must be considered independently from each other. Multi-Objective Evolutionary Algorithms (MOEAs) are a natural answer of this kind of evolution. In this work is presented a hybrid bioinspired optimization technique that associates a General Regression Neural Network _ GRNN with the Multi-Objective Bat Algorithm _ MOBA, for the design and synthesis of the Frequency Selective Surfaces _ FSS, aiming its application in data communication systems by difusion of millimeter waves, speci_cally, in the IEEE 802:15:3c standard. The designed device consists of planar arrangements of metallizations (patches), diamond-shaped, arranged over a RO4003 substrate. The FSS proposed in this study presents an operation with ultra-wide band characteristics, its patch designed to cover the range of 40:0 GHz at 70:0 GHz, i.e., 30:0 GHz bandwidth and 60:0 GHz resonance. The upper and lower cuto_ frequencies, referring to the transmission coe_cients scattering matrix (dB), were obtained at the cuto_ threshold at -10dB, to control the bandwidth of the device.
Acesso aberto (Open Access)
Síntese de superfícies seletivas de frequência para micro-ondas utilizando otimização multiobjetivo bioinspirada
(Universidade Federal do Pará, 2015-08-19) ALCÂNTARA NETO, Miércio Cardoso de; D'ASSUNCÃO, Adaildo Gomes; http://lattes.cnpq.br/4159638862269940; CAVALCANTE, Gervásio Protásio dos Santos; http://lattes.cnpq.br/2265948982068382
The evolution of computing has made possible substantial advances in research related to engineering and industrial projects. In these areas, the use of computational tools has been intensified for simulation and obtaining certain parameters of the project. However, the growing demand for precision and the gradual increase of the complexity of the structures and systems, results in a simulation process increasingly time-consuming, because the evaluation of a single criterion can consume several hours, as well as several days or even weeks. Therefore, a method that maximizes the time of simulation and optimization, can thus save time and money. In this context, bioinspired computing (BIC), presents accurate and efficient, where many traditional computational methods fail and consists of new mechanism to address such difficulties. Thus, in this work, a study about some of the algorithms used today for BIC design and optimization of general problems in engineering and industry. From now on, one sees develop a metaheuristic optimization code to provide lower-cost computational multiobjective and, consequently, less time for data processing. Initially, an electromagnetic research of frequency selective surfaces with triangular patch elements is done by computer simulations. The numerical analysis is carried out using a full-wave technique based on finite integrals implemented on commercial software performed for simulations in electromagnetism. The synthesis process consists of tuning the resonant frequency of the structures and the bandwidth according to the objectives in the cost function optimization algorithms. The modeling of structures is performed by an artificial neural network and optimization process is performed by meta-heuristics algorithms. The results obtained by these codes are compared to simulated ones by commercial software and measured. A good agreement between simulated and measured results was obtained, as well as a substantial reduction in the processing time of the structures. Finally, conclusions and proposals for further works are presented.