Navegando por Assunto "Genetics algorithms"

Agora exibindo 1 - 20 de 26

Acesso aberto (Open Access)
Uma abordagem heurística para os problemas de horários educacionais
(Universidade Federal do Pará, 2020-12-15) REIS, Williams Sousa dos; QUARESMA, João Nazareno Nonato; http://lattes.cnpq.br/7826389991864785
With each new school term, educational institutions have the hard work of allocating their class schedules, the problem implies in allocating time intervals and resources to activities, in such a way that they satisfy the restrictions imposed in the best possible way. This is a problem considered difficult to solve from the point of view of computational complexity theory. With this in mind, this work aims to develop a tool that can automate the time allocation process carried out by the Institute of Biodiversity and Forests (IBEF) at the Federal University of Western Pará (UFOPA), presenting a proposal based on the meta-heuristic known as genetic algorithm (AG). This proposal is submitted to several experiments with real data from that institution and the results are fully satisfactory, evidenced by overcoming the restrictions imposed by the problem in an acceptable computational time, bringing some advantages with the automation of the process, such as: reduction in time development of time allocation; and better reallocation and use of physical and human resources.
Acesso aberto (Open Access)
Uma abordagem para otimização do período de sensoriamento em rádio cognitivo com algoritmo genético multiobjetivo
(Universidade Federal do Pará, 2011-08-25) YOSHIOKA, Peterson Marcelo Santos; COSTA JÚNIOR, Carlos Tavares da; http://lattes.cnpq.br/6328549183075122
The spectral efficiency in networks based on cognitive radio (CR) technology can be compromised if the radio is used for a long time for the detection instead of data transmission. So it becomes necessary sensing schemes that have the purpose of obtaining the maximum possible use of spectrum, avoiding unnecessary sensing, as well as obtaining a minimum of interference in the transmission of the primary user due to incorrect detection of its transmission. In this paper, we propose the use of genetic algorithms for the adaptation of the sensing period. The goal is to obtain an optimal channels sensing period in order to maximize the discovery of spectrum opportunities and minimize the overhead due to the sensing. Most related works to this issue adopt fixed sensing overhead, not taking into account that some channels may have less tolerance to interference than others. The proposal presented in this work can adapt to the requirements of tolerance to interference with licensed channel by determining a period of sensing that optimizes the opportunities for any set amount of overhead. Our proposal achieves a gain up to 90% compared to nonoptimized techniques in terms of the number of opportunities found up to 40.9% gain in useful transmission and obtained a reduction in the time of interference of 66.83%. In addition, our proposal also achieves similar results to those obtained by an optimized proposal in the literature, with the advantage of allowing the adaptation of the sensing overhead.
Acesso aberto (Open Access)
Algoritmos culturais com abordagem memética e multipopulacional aplicados a problemas de otimização
(Universidade Federal do Pará, 2012-04-20) SILVA, Deam James Azevedo da; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
In many optimization problems is hard to reach a good result or a result close to the optimum value in a feasible time, especially when working on large scale. So, many of these problems are addressed by heuristics or metaheuristics running search for better solutions within the defined search space. Within the natural computing algorithms there are the cultural and genetic algorithms. These are evolutionary metaheuristics complement each other due to the dual mechanism of cultural heritage/genetic. The purpose of this paper is to study and use such mechanisms adding local search heuristics and multipopulation applied to combinatorial optimization problems (knapsack and travel salesman problems), constrained problems and multimodal functions. Some experiments have been conducted to assess the performance of the proposed combination of meta-heuristisc and heuristics mechanisms against approaches found in literature as applied to problem addressed here.
Acesso aberto (Open Access)
Alocação ótima de geração distribuída em redes de distribuição utilizando algoritmo híbrido baseado em cuckoo search e algoritmo genético
(Universidade Federal do Pará, 2018-09-02) OLIVEIRA, Victoria Yukie Matsunaga de; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
This thesis presents a novel Cuckoo Search (CS) algorithm called Cuckoo-GRN (Cuckoo Search with Genetically Replaced Nests), which incorporates the benefits of genetic algorithm (GA) into the CS algorithm. The proposed method handles the abandoned nests from CS more efficiently by genetically replacing them, significantly improving the performance of the algorithm by establishing optimal balance between diversification and intensification. The algorithm is used for the optimal location and size of distributed generation units in a distribution system, in order to minimise active power losses while improving system voltage stability and voltage profile. The allocation of single and multiple distribution generation units is considered. The proposed algorithm is extensively tested in mathematical benchmark functions as well as in the 33-bus and 119-bus distribution systems. Simulation results show that Cuckoo-GRN can lead to a substantial performance improvement over the original CS algorithm and others techniques currently known in literature, regarding not only the convergence but also the solution accuracy.
Acesso aberto (Open Access)
Antenas compactas de microondas de banda larga e banda ultra-larga (UWB)
(Universidade Federal do Pará, 2011-12-16) MÉLO, Dilermando Ramalho de; DMITRIEV, Victor Alexandrovich; http://lattes.cnpq.br/0684541646225359
In the last years, with the sprouting of new services and devices for the system of mobile communication that have large bandwidths of operation band frequency and occupying small volumes, the development of new antennas of broad bands and with reduced dimensions if became one of the main challenges of the research in the field of antennas. In the present work, two structures of large bandwidth antennas and dimensions reduced had been analyzed and optimized. In the first part, the wire built-in folded monopole antenna (W-BFMA) was investigated and optimized in different feeding impedances. For modeling of antenna structure W-BFMA the numerical method of moments (MoM) was used, and for its optimization the methods: parametric, hill climbing and genetic algorithm (GA) were used. Computational programs based in the Matlab language had been developed for modeling, optimizing, and generation of the main characteristic curves of the antenna. In the second part, two different configurations of planar monopole antennas using the technology ultrawideband (UWB) had been investigated and optimized with the aid of commercial program CST - Microwave Studio. Both UWB antennas had been fed by a line of microstrip in the impedance of 50Ω. The UWB antenna with the small return loss was constructed and measured experimentally. The main characteristic curves of the antenna as return losses, gain and radiation patterns had been analyzed. The simulated results had been compared with the measured results.
Acesso aberto (Open Access)
Avaliação da aprendizagem: uma abordagem qualitativa baseada em mapas conceituais, ontologias e algoritmos genéticos
(Universidade Federal do Pará, 2007-05-18) ROCHA, Francisco Edson Lopes da; FAVERO, Eloi Luiz; http://lattes.cnpq.br/1497269209026542
In the last two decades, the development of areas such as Computer Networks and Artificial Intelligence (AI) has favored the growth of other areas of knowledge, like Education. In this area, new discoveries have changed the focus of research from old behaviorist educational theories to constructivism, leading to a better understanding of how learning occurs. Meaningful Learning (ML) is a constructivist theory in evidence nowadays and the Concept Map (CM) is its main cognitive tool. Additionally, the recent developments on Distance Learning (DL) have made it possible to apply the educational process in a larger scale. In this thesis, automatic learning assessment mediated by concept maps is investigated. This is related to a qualitative approach, named as formative assessment, which is compliant with Bloom’s model, a reference for educational processes - teaching, learning, and learning assessment. The proposal presented in this thesis is seen as an alternative solution to an important issue in the area of Education: how to evaluate learning qualitatively, respecting each student’s cognitive processes? The integration of concept maps, domain ontologies, and genetic algorithms allows for advances in automatic learning assessment and assistance. The paradigm of mere quantitative assessment is broken, and a new approach to gradual and continuous assistance in learning is presented. Following this approach, it is possible to accompany students individually, respecting their idiosyncratic ways of learning, and also to group students based on specific cognitive characteristics or development degrees. This thesis begins a new research area, which can be synthesized as "Automatic qualitative assessment of learning centered in Concept Maps, based on AI techniques: ontologies and genetic algorithms". In this new research area, the thesis originated the following contributions: ² a prototype of an environment designed to aid teaching, learning, and learning assessment, founded upon Meaningful Learning, encompassing a concept map editor, an ontology editor, and an assessment module; ² A proposal concerning the use of genetic algorithms and ontologies in qualitative assessment/ assistance of learning, allowing for: – step-by-step individual assistance; – assistance to groups of students; – comparisons among students. Domain ontologies are generated by the teacher, who uses an ontology editor provided by the environment. They comprise the structural knowledge that must be learned by students before they can manage other forms of knowledge. The genetic algorithm was designed to run in two distinct modes: i) generating multiple CMs to compare with the student’s CM, allowing for learning assessment at any moment of the course; this assessment is relative, centered in a determined number of concepts which represent a partial structure of knowledge domain being studied.; and ii) generating an optimal CM according to the ontology created by the teacher, to permit a complete assessment of the learning of the knowledge domain which was studied. The proposed model was evaluated by the implementation of prototypes for the assessment tool. The genetic algorithm developed uses the ontologies as its search spaces. It emulates meaningful learning cognitive processes, and constructs CMs that can be semantically compared to that of the student. Its fitness function represents a way of measuring distances in the cognitive field, being the measurement unit given by a taxonomy that organizes semantic dimensions and, inside these, linking phrases. This taxonomy is used by teachers when they construct their ontologies, and by students when they construct their concept maps. The main challenges faced in the development of the research reported in this thesis were: 1) definition of a domain ontology model that could be applied to learning assessment; 2) definition of a method and a scale that could be applied to the cognitive domain; and 3) definition of a search mechanism in the ontology in accordance with constructivist theories of learning assessment. The research described in this thesis can be further developed with new functionalities or improvements in functionalities already implemented. Some possibilities are suggested in the end of the thesis, the main of which being the deployment of the environment in the Internet. This thesis has generated 7 (seven) scientific contributions, 1 (one) in a qualis A magazine, 1 (one) in a qualis B magazine, 2 (two) in international congresses, and 3(three) in national congresses. The results of this research advance what has already been attained by the AmAm/UFPA research group, in whose context this thesis is inserted.
Acesso aberto (Open Access)
Cálculo de equivalentes dinâmicos de sistema de potência usando algoritmos genéticos
(Universidade Federal do Pará, 2014-08-07) SANTOS, Pitther Negrão dos; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813; VIEIRA, João Paulo Abreu; http://lattes.cnpq.br/8188999223769913
This dissertation presents a genetic algorithm-based method for calculating of power system dynamic equivalents aiming to represent parts of a power system for transient stability analysis. The dynamic equivalent calculation is obtained through carrying out the parameter identification of synchronous generators located on frontier buses, linking the external and the studied subsystem. An index is used to assess the proximity between simulations carried out using the full and the reduced model following the large disturbances emerged in the studied subsystem. Different operating conditions are taken into account. The simulations were conducted using the softwares GAOT “The Genetic Algorithm Optimization Toolbox”,, ANAREDE and ANATEM. This method is tested on a Kundur’s two-area test system and on a Brazilian Interconnect Power System (BIPS). Test results validate the efficacy of the developed methodology in calculating the robust dynamic equivalents.
Acesso aberto (Open Access)
Classificação de dados utilizando algoritmos genéticos e lógica difusa
(Universidade Federal do Pará, 2008-12-14) KATO, Rodrigo Bentes; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
Several of the traditional techniques of Data Mining have been applied successfully and others have some limitations. Both, in performance and the quality of knowledge generated. Recent research has shown that the techniques in the field of IA, such as GA and Fuzzy sets, can be used successfully. In this research we are interested in investigating the applicability of a hybrid combination of genetic algorithms and fuzzy sets to find rules in large and complex spaces. This paper presents a Genetic Algorithm (GA), using Fuzzy Logic, for coding, assessment and reproduction of chromosomes, looking for classifying data using extracted rules for the automatic way with the evolution of chromosomes. The Fuzzy Logic is used to make the rules clearer and closer to human language, using linguistic representations to identify continuous data.
Acesso aberto (Open Access)
Comparação de métodos baseados em algoritmos genéticos para ajuste coordenado de estabilizadores de sistemas de potência
(Universidade Federal do Pará, 2014-11-27) VIEIRA, Celivan Ferreira; VIEIRA, João Paulo Abreu; http://lattes.cnpq.br/8188999223769913
This work presents a comparison study among three methodologies based on genetic algorithms applied to solve the power system stabilizers (PSS) tuning problem. The PSS tuning procedures are formulated as an optimization problem in order to: 1) maximize the closed-loop minimum damping ratio; 2) maximize the sum of the spectrum damping ratios; and 3) shift the lightly damped and undamped electromechanical modes of all plants to a prescribed zone in the s-plane. The three methodologies taking into account a pre-specified operating conditions. For this purpose, the system is represented by the state-space equations and the matrices associated with this modeling are obtained by using the academic version of the commercial software PacDyn. The simulations are carried out using the MATLAB plataform. The methodologies are applied to the well-known New England test system.
Acesso aberto (Open Access)
Desenvolvimento de uma ferramenta computacional para otimização de cálculo luminotécnico de interiores baseado em algoritmo genético
(Universidade Federal do Pará, 2019-09-17) MONTEIRO, Ana Laura Pinheiro Ruivo; TOSTES, Maria Emília de Lima; http://lattes.cnpq.br/4197618044519148
There is a large quantity of lamps and luminaires that present their on characteristics, such as the amount oflumens and life time. Thus, there are several possible combinations of lamps and luminaires that can be employed as a solution to adapt the lighting of a given indoor environment. What will differentia teach solution will be the cost of investment and the time of financial return. This work presents the development of a tool that provides the user with the possibility to carry out lighting studies for any internal business environment ,considering multiple scenarios and following the regulations set by the NBRISO/CIE 8995-1:2013. Studies are carried out in an optimized method by running a genetic algorithm, which has as objective function the minimization of time of financial return on investment of lamps and luminaires,which are necessary for the achievement of illuminance area. For the development of the tool spread sheets were associated with the Python programming language and the PyCharm as the development software. The lumens method was used for lighting sizing, the simple linear regression technique was used to estimate the rate of electrical power for a period of 10 years and NetPresent Value a long side the discounted pay back for analysis of financial return of the solutions generated by the tool. The developed tool was applied in four different scenarios in the Amazon Energy Efficiency Center(CEAMAZON) building. Valid solutions for all scenarios were found, that is, a pay back with in 10 years, taking into consideration the initial investment, annual consumption and maintenance,if any. The best solutions were simulated by the software DIALux as an aid in the projection of the distribution of luminaires in environments. The aspects described in this work show the functionality and applicability of this tool, in order to support the user in the planning of lighting projects, having achieved the established goal, showing functionality and effective ness.
Acesso aberto (Open Access)
Estratégia do planejamento e otimização de sistemas sem fio, considerando redes interferentes: abordagem baseada em cross-layer
(Universidade Federal do Pará, 2011-06-30) ARAÚJO, Jasmine Priscyla Leite de; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
In spite of the significant increase of the use of Wireless Local Area Network (WLAN) experienced in the last years, design aspects and capacity planning are still systematically neglected during the network implementation. Typically, a wireless local area network is designed and installed by networking professionals. These individuals are familiar with wired networks, but are often unfamiliar with wireless networks. Thus, wireless local area networks installations are prejudiced by the lack of an accurate performance evaluation model and to determine the location of the access point (AP), besides important factors of the environment are not considered in the project. These factors become more important when several APs are installed, sometimes without a frequency planning, to cover a unique building. Faults such as these can cause interference among cells generated by each PA. Therefore, the network will not obtain the QoS patterns required for each service. The present work provides a planning proposal to wireless networks regarding the influence of interference using computational intelligence just as Bayesian Networks. An extensive measurement campaign was done to evaluate the performance of two access points (PAs) under a multi user and interference scenarios. The data collected in the measurement campaign was used as input of the Bayesian networks and confirmed the influence of the interference in the QoS parameters. A genetic algorithm technique was used as a hybrid approach to wireless planning. Another technique, called particle swarm optimization (PSO) was used to compare the optimizations results from the QoS parameters to find the best distance from the AP to the receiver to guarantee the QoS ITU-T recommendations.
Acesso aberto (Open Access)
Estratégias evolucionárias para otimização no tratamento de dados ausentes por imputação múltipla de dados
(Universidade Federal do Pará, 2016-02-16) LOBATO, Fábio Manoel França; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858
The data analysis process includes information acquisition and organization in order to obtain knowledge from them, bringing scientific advances in various fields, as well as providing competitive advantages to corporations. In this context, an ubiquitous problem in the area deserves attention, the missing data, since most of the data analysis techniques can not deal satisfactorily with this problem, which negatively impacts the final results. In order to avoid the harmful effects of missing data, several studies have been proposed in the areas of statistical analysis and machine learning, especially the study of Multiple Data Imputation, which consists in the missing data substitution by plausible values. This methodology can be seen as a combinatorial optimization problem, where the goal is to find candidate values to substitute the missing ones in order to reduce the bias imposed by this issue. Metaheuristics, in particular, methods based in evolutionary computing have been successfully applied in combinatorial optimization problems. Despite the recent advances in this area, it is perceived some shortcomings in the modeling of imputation methods based on evolutionary computing. Aiming to fill these gaps in the literature, this thesis presents a description of multiple data imputation as a combinatorial optimization problem and proposes imputation methods based on evolutionary computing. In addition, due to the limitations found in the methods presented in the recent literature, and the necessity of adoption of different evaluation measures to assess the imputation methods performance, a multi-objective genetic algorithm for data imputation in pattern classification context is also proposed. This method proves to be flexible regarding to data types and avoid the complete-case analysis. Because the flexibility of the proposed approach, it is also possible to use it in other scenarios such as the unsupervised learning, multi-label classification and time series analysis.
Acesso aberto (Open Access)
Estudo da parametrização do algoritmo híbrido baseado no algoritmo cultural com algoritmo genético em uma abordagem multipopulacional
(Universidade Federal do Pará, 2015-10-01) SILVA JUNIOR, Joaquim Alberto Leite da; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
The purpose of this paper is to analyze the application of a hybrid cultural algorithm with population generated by multipopulation feature of the genetic algorithm, or, more specifically, to develop a parameter of the hybrid algorithm based on cultural algorithm with genetic algorithm for multidimensional knapsack problem in areas of computer science and computational intelligence. The aim of this work is to find the best parameters for hybrid cultural algorithm and over genetic algorithm, with model of islands (multipopulation characteristic) applied to combinatorial optimization problem called “Multidimensional Knapsack“. Several experiments are performed to make an assessment regarding of these mechanisms hybrids with other algorithms available in the literature.
Acesso aberto (Open Access)
Um framework para a previsão de cenários com o uso de sistemas híbridos neurogenéticos para compra e venda de energia elétrica no mercado futuro
(Universidade Federal do Pará, 2012-05-04) CONDE, Guilherme Augusto Barros; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
In the context of time series forecasting, is great the interest in studies of forecasting methods of time series that can identify existing structures and patterns in historical data, allowing generate the next patterns of the series. The proposal defended in this thesis is the development of a framework that uses the full potential of forecasting techniques (neural networks) with the optimization techniques (genetic algorithms) in a hybrid system that well enjoy the advantages of each of these techniques to the generation of future scenarios that can show, in aaddition to normal forecasts based on historical values, alternative pathways of the curves of time series analyzed.
Acesso aberto (Open Access)
Metaheurísticas populacionais: estudo comparativo na sintonia de parâmetros de controladores clássicos
(Universidade Federal do Pará, 2016-12-02) VIDAL, Juan Ferreira; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
Population metaheuristics are techniques belonging to the field of Computational Intelligence and are based on natural models, have emerged as alternatives to solve optimization problems where the traditional techniques cannot be applied, or even where a solution model for the problem is not available with which the solution is found through empirical means. Given these capabilities to provide acceptable solutions in a timely manner for most of the complex problems encountered, metaheuristics has been applied successfully in most of the control system problems found in the literature. This work presents in general how the metaheuristics are being applied in the solution of control problems and performs a comparative study of performance among four algorithms bioinspirados in the tuning of the PID parameters. The following algorithms were used: Genetic Algorithm (AG), Genetic Algorithm in the Islands Model (AGMI), Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO). The results demonstrate that the algorithms present an excellent performance in the tuning of the PID producing response that met the project requirements. Different systems with different characteristics were used to evaluate the algorithms. The PSO was shown as the best algorithm among the four used, producing response in a faster time and presented lower deviated standard in the trials.
Acesso aberto (Open Access)
Um método para determinação de pontos de operação com diversidade em linha digital de assinante usando balanceamento de espectro e algoritmo evolucionário
(Universidade Federal do Pará, 2011-02-17) BEZERRA, Johelden Campos; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284; PELAES, Evaldo Gonçalves; http://lattes.cnpq.br/0255430734381362
This work presents a method for finding diversity set operating points, which are Pareto optimal and diverse, to digital subscriber lines (DSL). Several works presented in the literature have proposed algorithms for optimizing data transmission in DSL lines, which results in a unique operating point for the modems. These works use spectrum balancing algorithms to solve the power allocation problem, which differs from the approach presented in this work. The proposed method, called diverseSB, uses a hybrid solution that consists of the non-dominated sorting genetic algorithm-II(NSGA-II), based on a multi-objective optimization, and a spectrum balancing algorithm. The simulation results showed that, for a given diversity, the computational cost for find the operating points with diversity using the diverseSB proposed algorithm is much smaller than “brute-force” search methods. In the proposed method, NSGA-II perform calls to the spectrum balancing algorithm adopted, so many tests involving the same number of calls to the algorithm were performed with the diverseSB proposed and with the brute-force search method, and the results of diverseSB proposed were better than brute-force search method. For example, to obtain a diverse set operating points the brute-force method performs 1,600 calls to the spectrum balancing algorithm and the diverseSB proposed performed 535 calls.
Acesso aberto (Open Access)
Metodologia baseada em sistema fuzzy intervalar do tipo-2 para detecção e identificação de faltas de incipientes em motores de indução
(Universidade Federal do Pará, 2013-02-27) ROCHA, Erick Melo; BARRA JUNIOR, Walter; http://lattes.cnpq.br/0492699174212608
Since the incorporation of automation in the production processes, aiming at order to improve productivity and quality of products and services, researches on more efficient methodologies for fault diagnosis became more intensive. Such techniques allow the early detection of faults, before then lead to failures. This work investigates techniques for detection and diagnosis of faults and its application to induction motors, limiting their study to two situations, namely: system free of faults and system under incipient partial short-circuit in the coils the stator winding. For faults detection, parametric analysis of fist order ARX (autoregressive with exogenous input) were applied. The parameters of identified ARX modes, which bring information about the dynamics of the dominant system, are recursively obtained by the techniques of recursive least squares (RLS). In order to evaluate the capability for early fault detection, a type-2 interval fuzzy system was developed. This kind of fuzzy system has capability to capture a larger set of uncertainties than conventional (type-1) fuzzy systems. The footprint of uncertainty (FOU), characteristic of type-2 fuzzy system, is a way to accounts for uncertainties coming from noise and numerical errors from the process of parameter estimation. The ARX model parameters are the inputs to the supervisor system. Genetic algorithms (GA’s) were used for optimization of SIF interval type-2, aiming at to reduce the diagnostic error. The results obtained in tests of computer simulation show the effectiveness of the proposed methodology.
Acesso aberto (Open Access)
Metodologia para compressão de sinais de energia elétrica a partir de registros de forma de onda utilizando algorítmos genéticos e redes neurais artificiais
(Universidade Federal do Pará, 2016-12-16) BARROS, Fabíola Graziela Noronha; NUNES, Marcus Vinícius Alves; http://lattes.cnpq.br/9533143193581447; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813
This thesis proposes a methodology for compression of electrical power signals from waveform records in electric systems, using genetic algorithm (GA) and artificial neural network (ANN).The genetic algorithm is used to select and preserve the points that better characterize the waveform contoursA and the artificial neural network is used in the compression of other points as well as on the signal reconstruction process. Thus, the data resulting are formed by a part of the original signal and by a compressed complementary part in the form of synaptic weights. The proposed methodology selects and preserves a percentage of the original signal samples, which are aspects not explored in the literature. The method was tested using field data obtained from an oscillographic recorder installed in a 230kV electrical power system. The results presented compression rates ranging from 88.36 to 95.86 for preservation rates ranging from 2.5 to 10 , respectively.
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)
Otimização de cobertura, consumo de energia, roteamento e agregação de dados em rede de sensores sem fio utilizando algoritmos genéticos e lógica fuzzy
(Universidade Federal do Pará, 2011-03-04) NUNES, Thiêgo Maciel; MONTEIRO, Dionne Cavalcante; http://lattes.cnpq.br/4423219093583221; CERQUEIRA, Eduardo Coelho; http://lattes.cnpq.br/1028151705135221
The Wireless Sensor Networks (WSN) have limited capacities for processing, storage, communication (bandwidth) and power source, besides having features and basic requirements of a WSN such as: the need for self-organization, communication with diffusion of short-range and multihop routing. This work proposes a tool that optimizes the positioning and the packages delivered through the use of Genetic Algorithm (GA). To resolve the routing problem that improves power consumption and maximize data aggregation is proposed the use of fuzzy logic in the routing protocol Ad hoc Ondemand Distance Vector (AODV). This customization is entitled AODV - Fuzzy for Wireless Sensor Networks (AODV-FWSN). The results show that the proposed solution is efficient and can prolong the life of the WSN and improve the rate of data delivery when compared to similar solutions.