Uma abordagem através de algoritmos transgenéticos para o problema da configuração do traçado de uma rede de distribuição de gás natural

Este trabalho apresenta um algoritmo transgenético híbrido para a solução de um Problema de Configuração de uma Rede de Distribuição de Gás Natural. O problema da configuração dessas redes requer a definição de um traçado por onde os dutos devem ser colocados para atender aos clientes. É estudada neste trabalho uma maneira de conectar os clientes em uma rede com arquitetura em forma de árvore. O objetivo é minimizar o custo de construção da rede, mesmo que para isso alguns clientes que não proporcionam lucros deixem de ser atendidos. Esse problema pode ser formulado computacionalmente através do Problema de Steiner com Prêmios. Este é um problema de otimização combinatória da classe dos NPÁrduos. Este trabalho apresenta um algoritmo heurístico para a solução do problema. A abordagem utilizada é chamada de Algoritmos Transgenéticos, que se enquadram na categoria dos algoritmos evolucionários. Para a geração de soluções inicias é utilizado um algoritmo primaldual, e pathrelinking é usado como intensificador

Algoritmos e Arquiteturas VLSI para Detectores MIMO com Decisão Suave

The use of Multiple Input Multiple Output (MIMO) systems has permitted the recent evolution of wireless communication standards. The Spatial Multiplexing MIMO technique, in particular, provides a linear gain at the transmission capacity with the minimum between the numbers of transmit and receive antennas. To obtain a near capacity performance in SM-MIMO systems a soft decision Maximum A Posteriori Probability MIMO detector is necessary. However, such detector is too complex for practical solutions. Hence, the goal of a MIMO detector algorithm aimed for implementation is to get a good approximation of the ideal detector while keeping an acceptable complexity. Moreover, the algorithm needs to be mapped to a VLSI architecture with small area and high data rate. Since Spatial Multiplexing is a recent technique, it is argued that there is still much room for development of related algorithms and architectures. Therefore, this thesis focused on the study of sub optimum algorithms and VLSI architectures for broadband MIMO detector with soft decision. As a result, novel algorithms have been developed starting from proposals of optimizations for already established algorithms. Based on these results, new MIMO detector architectures with configurable modulation and competitive area, performance and data rate parameters are here proposed. The developed algorithms have been extensively simulated and the architectures were synthesized so that the results can serve as a reference for other works in the area

Estratégias de busca reativa utilizando aprendizagem por reforço e algoritmos de busca local

Técnicas de otimização conhecidas como as metaheurísticas tem conseguido resolversatisfatoriamente problemas conhecidos, mas desenvolvimento das metaheurísticas écaracterizado por escolha de parâmetros para sua execução, na qual a opção apropriadadestes parâmetros (valores). Onde o ajuste de parâmetro é essencial testa-se os parâmetrosaté que resultados viáveis sejam obtidos, normalmente feita pelo desenvolvedor que estaimplementando a metaheuristica. A qualidade dos resultados de uma instância1 de testenão será transferida para outras instâncias a serem testadas e seu feedback pode requererum processo lento de “tentativa e erro” onde o algoritmo têm que ser ajustado para umaaplicação especifica. Diante deste contexto das metaheurísticas surgiu a Busca Reativaque defende a integração entre o aprendizado de máquina dentro de buscas heurísticaspara solucionar problemas de otimização complexos. A partir da integração que a BuscaReativa propõe entre o aprendizado de máquina e as metaheurísticas, surgiu a ideia dese colocar a Aprendizagem por Reforço mais especificamente o algoritmo Q-learning deforma reativa, para selecionar qual busca local é a mais indicada em determinado instanteda busca, para suceder uma outra busca local que não pode mais melhorar a soluçãocorrente na metaheurística VNS. Assim, neste trabalho propomos uma implementação reativa,utilizando aprendizado por reforço para o auto-tuning do algoritmo implementado,aplicado ao problema do caixeiro viajante simétrico e ao problema escalonamento sondaspara manutenção de poços.

Formulações e algoritmos para o problema das p-medianas heterogêneo livre de penalidade

This work presents a new model for the Heterogeneous p-median Problem (HPM), proposed to recover the hidden category structures present in the data provided by a sorting task procedure, a popular approach to understand heterogeneous individual’s perception of products and brands. This new model is named as the Penalty-free Heterogeneous p-median Problem (PFHPM), a single-objective version of the original problem, the HPM. The main parameter in the HPM is also eliminated, the penalty factor. It is responsible for the weighting of the objective function terms. The adjusting of this parameter controls the way that the model recovers the hidden category structures present in data, and depends on a broad knowledge of the problem. Additionally, two complementary formulations for the PFHPM are shown, both mixed integer linear programming problems. From these additional formulations lower-bounds were obtained for the PFHPM. These values were used to validate a specialized Variable Neighborhood Search (VNS) algorithm, proposed to solve the PFHPM. This algorithm provided good quality solutions for the PFHPM, solving artificial generated instances from a Monte Carlo Simulation and real data instances, even with limited computational resources. Statistical analyses presented in this work suggest that the new algorithm and model, the PFHPM, can recover more accurately the original category structures related to heterogeneous individual’s perceptions than the original model and algorithm, the HPM. Finally, an illustrative application of the PFHPM is presented, as well as some insights about some new possibilities for it, extending the new model to fuzzy environments

Sobre a integração indefinida de funções racionais complexas: teoria e implementação de algoritmos racionais

We present indefinite integration algorithms for rational functions over subfields of the complex numbers, through an algebraic approach. We study the local algorithm of Bernoulli and rational algorithms for the class of functions in concern, namely, the algorithms of Hermite; Horowitz-Ostrogradsky; Rothstein-Trager and Lazard-Rioboo-Trager. We also study the algorithm of Rioboo for conversion of logarithms involving complex extensions into real arctangent functions, when these logarithms arise from the integration of rational functions with real coefficients. We conclude presenting pseudocodes and codes for implementation in the software Maxima concerning the algorithms studied in this work, as well as to algorithms for polynomial gcd computation; partial fraction decomposition; squarefree factorization; subresultant computation, among other side algorithms for the work. We also present the algorithm of Zeilberger-Almkvist for integration of hyperexpontential functions, as well as its pseudocode and code for Maxima. As an alternative for the algorithms of Rothstein-Trager and Lazard-Rioboo-Trager, we yet present a code for Benoulli’s algorithm for square-free denominators; and another for Czichowski’s algorithm, although this one is not studied in detail in the present work, due to the theoretical basis necessary to understand it, which is beyond this work’s scope. Several examples are provided in order to illustrate the working of the integration algorithms in this text

Distribuição de derivados de petróleo por redes de polidutos: uma abordagem através de algoritmos evolucionários híbridos para um problema triobjetivo

An important problem faced by the oil industry is to distribute multiple oil products through pipelines. Distribution is done in a network composed of refineries (source nodes), storage parks (intermediate nodes), and terminals (demand nodes) interconnected by a set of pipelines transporting oil and derivatives between adjacent areas. Constraints related to storage limits, delivery time, sources availability, sending and receiving limits, among others, must be satisfied. Some researchers deal with this problem under a discrete viewpoint in which the flow in the network is seen as batches sending. Usually, there is no separation device between batches of different products and the losses due to interfaces may be significant. Minimizing delivery time is a typical objective adopted by engineers when scheduling products sending in pipeline networks. However, costs incurred due to losses in interfaces cannot be disregarded. The cost also depends on pumping expenses, which are mostly due to the electricity cost. Since industrial electricity tariff varies over the day, pumping at different time periods have different cost. This work presents an experimental investigation of computational methods designed to deal with the problem of distributing oil derivatives in networks considering three minimization objectives simultaneously: delivery time, losses due to interfaces and electricity cost. The problem is NP-hard and is addressed with hybrid evolutionary algorithms. Hybridizations are mainly focused on Transgenetic Algorithms and classical multi-objective evolutionary algorithm architectures such as MOEA/D, NSGA2 and SPEA2. Three architectures named MOTA/D, NSTA and SPETA are applied to the problem. An experimental study compares the algorithms on thirty test cases. To analyse the results obtained with the algorithms Pareto-compliant quality indicators are used and the significance of the results evaluated with non-parametric statistical tests.

Metodologia de estimação de estados para tratamento de medições errôneas e registros não sincronizados em algoritmos de localização de faltas em linhas de transmissão

The performance of algorithms for fault location i n transmission lines is directly related to the accuracy of its input data. Thus, fa ctors such as errors in the line parameters, failures in synchronization of oscillographic recor ds and errors in measurements of voltage and current can significantly influence the accurac y of algorithms that use bad data to indicate the fault location. This work presents a new method ology for fault location in transmission lines based on the theory of state estimation in or der to determine the location of faults more accurately by considering realistic systematic erro rs that may be present in measurements of voltage and current. The methodology was implemente d in two stages: pre-fault and post- fault. In the first step, assuming non-synchronized data, the synchronization angle and positive sequence line parameters are estimated, an d in the second, the fault distance is estimated. Besides calculating the most likely faul t distance obtained from measurement errors, the variance associated with the distance f ound is also determined, using the errors theory. This is one of the main contributions of th is work, since, with the proposed algorithm, it is possible to determine a most likely zone of f ault incidence, with approximately 95,45% of confidence. Tests for evaluation and validation of the proposed algorithm were realized from actual records of faults and from simulations of fictitious transmission systems using ATP software. The obtained results are relevant to show that the proposed estimation approach works even adopting realistic variances, c ompatible with real equipments errors.

Análise de desempenho de algoritmos de compressão de dados com perda para aplicações industriais

The great amount of data generated as the result of the automation and process supervision in industry implies in two problems: a big demand of storage in discs and the difficulty in streaming this data through a telecommunications link. The lossy data compression algorithms were born in the 90’s with the goal of solving these problems and, by consequence, industries started to use those algorithms in industrial supervision systems to compress data in real time. These algorithms were projected to eliminate redundant and undesired information in a efficient and simple way. However, those algorithms parameters must be set for each process variable, becoming impracticable to configure this parameters for each variable in case of systems that monitor thousands of them. In that context, this paper propose the algorithm Adaptive Swinging Door Trending that consists in a adaptation of the Swinging Door Trending, as this main parameters are adjusted dynamically by the analysis of the signal tendencies in real time. It’s also proposed a comparative analysis of performance in lossy data compression algorithms applied on time series process variables and dynamometer cards. The algorithms used to compare were the piecewise linear and the transforms.

Algoritmos experimentais para o problema biobjetivo da árvore geradora quadrática em adjacência de arestas

The Quadratic Minimum Spanning Tree (QMST) problem is a generalization of the Minimum Spanning Tree problem in which, beyond linear costs associated to each edge, quadratic costs associated to each pair of edges must be considered. The quadratic costs are due to interaction costs between the edges. When interactions occur between adjacent edges only, the problem is named Adjacent Only Quadratic Minimum Spanning Tree (AQMST). Both QMST and AQMST are NP-hard and model a number of real world applications involving infrastructure networks design. Linear and quadratic costs are summed in the mono-objective versions of the problems. However, real world applications often deal with conflicting objectives. In those cases, considering linear and quadratic costs separately is more appropriate and multi-objective optimization provides a more realistic modelling. Exact and heuristic algorithms are investigated in this work for the Bi-objective Adjacent Only Quadratic Spanning Tree Problem. The following techniques are proposed: backtracking, branch-and-bound, Pareto Local Search, Greedy Randomized Adaptive Search Procedure, Simulated Annealing, NSGA-II, Transgenetic Algorithm, Particle Swarm Optimization and a hybridization of the Transgenetic Algorithm with the MOEA-D technique. Pareto compliant quality indicators are used to compare the algorithms on a set of benchmark instances proposed in literature.

Algoritmos experimentais para o problema biobjetivo da árvore geradora quadrática em adjacência de arestas

The Quadratic Minimum Spanning Tree (QMST) problem is a generalization of the Minimum Spanning Tree problem in which, beyond linear costs associated to each edge, quadratic costs associated to each pair of edges must be considered. The quadratic costs are due to interaction costs between the edges. When interactions occur between adjacent edges only, the problem is named Adjacent Only Quadratic Minimum Spanning Tree (AQMST). Both QMST and AQMST are NP-hard and model a number of real world applications involving infrastructure networks design. Linear and quadratic costs are summed in the mono-objective versions of the problems. However, real world applications often deal with conflicting objectives. In those cases, considering linear and quadratic costs separately is more appropriate and multi-objective optimization provides a more realistic modelling. Exact and heuristic algorithms are investigated in this work for the Bi-objective Adjacent Only Quadratic Spanning Tree Problem. The following techniques are proposed: backtracking, branch-and-bound, Pareto Local Search, Greedy Randomized Adaptive Search Procedure, Simulated Annealing, NSGA-II, Transgenetic Algorithm, Particle Swarm Optimization and a hybridization of the Transgenetic Algorithm with the MOEA-D technique. Pareto compliant quality indicators are used to compare the algorithms on a set of benchmark instances proposed in literature.

Estudo e desenvolvimento de algoritmos criptográficos para redes de sensores sem fio, utilizando técnicas de programação genética

Cryptography is the main form to obtain security in any network. Even in networks with great energy consumption restrictions, processing and memory limitations, as the Wireless Sensors Networks (WSN), this is no different. Aiming to improve the cryptography performance, security and the lifetime of these networks, we propose a new cryptographic algorithm developed through the Genetic Programming (GP) techniques. For the development of the cryptographic algorithm’s fitness criteria, established by the genetic GP, nine new cryptographic algorithms were tested: AES, Blowfish, DES, RC6, Skipjack, Twofish, T-DES, XTEA and XXTEA. Starting from these tests, fitness functions was build taking into account the execution time, occupied memory space, maximum deviation, irregular deviation and correlation coefficient. After obtaining the genetic GP, the CRYSEED and CRYSEED2 was created, algorithms for the 8-bits devices, optimized for WSNs, i.e., with low complexity, few memory consumption and good security for sensing and instrumentation applications.

Estudo e desenvolvimento de algoritmos criptográficos para redes de sensores sem fio, utilizando técnicas de programação genética

Cryptography is the main form to obtain security in any network. Even in networks with great energy consumption restrictions, processing and memory limitations, as the Wireless Sensors Networks (WSN), this is no different. Aiming to improve the cryptography performance, security and the lifetime of these networks, we propose a new cryptographic algorithm developed through the Genetic Programming (GP) techniques. For the development of the cryptographic algorithm’s fitness criteria, established by the genetic GP, nine new cryptographic algorithms were tested: AES, Blowfish, DES, RC6, Skipjack, Twofish, T-DES, XTEA and XXTEA. Starting from these tests, fitness functions was build taking into account the execution time, occupied memory space, maximum deviation, irregular deviation and correlation coefficient. After obtaining the genetic GP, the CRYSEED and CRYSEED2 was created, algorithms for the 8-bits devices, optimized for WSNs, i.e., with low complexity, few memory consumption and good security for sensing and instrumentation applications.

Algoritmos meta-heurísticos para a solução do problema do caixeiro viajante com múltiplas caronas

The Traveling Salesman with Multiple Ridesharing (TSP-MR) is a type of the Capacitated Traveling Salesman, which presents the possibility of sharing seats with passengers taking advantage of the paths the salesman travels through his cycle. The salesman shares the cost of a path with the boarded passengers. This model can portray a real situation in which, for example, drivers are willing to share parts of a trip with tourists that wish to move between two locations visited by the driver’s route, accepting to share the vehicle with other individuals visiting other locations within the cycle. This work proposes a mathematical formulation for the problem, and an exact and metaheuristics algorithms for its solution, comparing them.

Algoritmos meta-heurísticos para a solução do problema do caixeiro viajante com múltiplas caronas

The Traveling Salesman with Multiple Ridesharing (TSP-MR) is a type of the Capacitated Traveling Salesman, which presents the possibility of sharing seats with passengers taking advantage of the paths the salesman travels through his cycle. The salesman shares the cost of a path with the boarded passengers. This model can portray a real situation in which, for example, drivers are willing to share parts of a trip with tourists that wish to move between two locations visited by the driver’s route, accepting to share the vehicle with other individuals visiting other locations within the cycle. This work proposes a mathematical formulation for the problem, and an exact and metaheuristics algorithms for its solution, comparing them.

Estudo e implementação de algoritmos inteligentes para detecção e classificação de falhas na medição de gás natural

This master dissertation presents the study and implementation of inteligent algorithms to monitor the measurement of sensors involved in natural gas custody transfer processes. To create these algoritmhs Artificial Neural Networks are investigated because they have some particular properties, such as: learning, adaptation, prediction. A neural predictor is developed to reproduce the sensor output dynamic behavior, in such a way that its output is compared to the real sensor output. A recurrent neural network is used for this purpose, because of its ability to deal with dynamic information. The real sensor output and the estimated predictor output work as the basis for the creation of possible sensor fault detection and diagnosis strategies. Two competitive neural network architectures are investigated and their capabilities are used to classify different kinds of faults. The prediction algorithm and the fault detection classification strategies, as well as the obtained results, are presented