This thesis deals with optimization problems with main focus on logistic Vehicle Routing Problem (VRP). In the first part term optimization is established and most important optimization problems are presented. Next section deals with methods, which are capable of solving those problems. Furthermore it is explored how to apply those methods to specific VRP, along with presenting some enhancement of those algorithms. This thesis also introduces learning method capable of using knowledge of previous solutions. At the end of the paper, experiments are performed to tune the parameters of used algorithms and to discuss benefit of suggested improvements.

Made available in DSpace on 2014-12-17T15:48:03Z (GMT). No. of bitstreams: 1 AntonioAFN_DISSERT.pdf: 3187796 bytes, checksum: c8d44014d0b75e991f4f3b3473a8dcd5 (MD5) Previous issue date: 2012-08-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Committees of classifiers may be used to improve the accuracy of classification systems, in other words, different classifiers used to solve the same problem can be combined for creating a system of greater accuracy, called committees of classifiers. To that this to succeed is necessary that the classifiers make mistakes on different objects of the problem so that the errors of a classifier are ignored by the others correct classifiers when applying the method of combination of the committee. The characteristic of classifiers of err on different objects is called diversity. However, most measures of diversity could not describe this importance. Recently, were proposed two measures of the diversity (good and bad diversity) with the aim of helping to generate more accurate committees. This paper performs an experimental analysis of these measures applied directly on the building of the committees of classifiers. The method of construction adopted is modeled as a search problem by the set of characteristics of the databases of the problem and the best set of committee members in order to find the committee of classifiers to produce the most accurate classification. This problem is solved by metaheuristic optimization techniques, in their mono and multi-objective versions. Analyzes are performed to verify if use or add the measures of good diversity and bad diversity in the optimization objectives creates more accurate committees. Thus, the contribution of this study is to determine whether the measures of good diversity and bad diversity can be used in mono-objective and multi-objective optimization techniques as optimization objectives for building committees of classifiers more accurate than those built by the same process, but using only the accuracy classification as objective of optimization / Comit?s de classificadores podem ser empregados para melhorar a acur?cia de sistemas de classifica??o, ou seja, diferentes classificadores aplicados ? solu??o de um mesmo problema podem ser combinados gerando um sistema de maior acur?cia, denominado de comit?s de classificadores. Para que se obtenha sucesso ? necess?rio que os classificadores apresentem erros em diferentes objetos do problema para que assim os erros de um classificador sejam suprimidos pelo acerto dos demais na aplica??o do m?todo de combina??o do comit?. A caracter?stica dos classificadores de errarem em objetos diferentes ? denominada de diversidade. No entanto, as maiorias das medidas de diversidade n?o conseguiam descrever essa import?ncia. Recentemente, foram propostas duas medidas de diversidade (boa e m? diversidade) as medidas de boa e m? diversidade com o objetivo de auxiliar a gera??o de comit?s mais acurados. Este trabalho efetua uma an?lise experimental dessas medidas aplicadas diretamente na constru??o de comit?s de classificadores. O m?todo de constru??o adotado ? modelado como um problema de busca pelo melhor conjunto de caracter?sticas das bases de dados do problema e pelo melhor conjunto de membros do comit? a fim de encontrar o comit? de classificadores que apresente ? maior acur?cia de classifica??o. Esse problema ? resolvido atrav?s de t?cnicas de otimiza??o metaheur?sticas, nas vers?es mono e multiobjetivo. S?o efetuadas an?lises estat?sticas para verificar se usar ou adicionar as medidas de boa e m? diversidade como objetivos de otimiza??o resulte comit?s mais acurados. Assim, a contribui??o desse trabalho ? determinar se as medidas de boa e m? diversidade podem ser utilizadas em t?cnicas de otimiza??o mono e multiobjetivo como objetivos de otimiza??o para constru??o de comit?s de classificadores mais acurados que aqueles constru?dos pelo mesmo processo, por?m utilizando somente a acur?cia de classifica??o como objetivo de otimiza??o

Les travaux de thèse s’intéressent au réglage de la puissance active injectée dans le réseau, ce qui représente aujourd'hui l'une des problématiques principales du pilotage des parcs éoliens participant à la gestion du réseau. Dans le même temps, l'un des buts reste de maximiser la puissance extraite du vent en considérant les effets de couplage aérodynamique entre les éoliennes.La structure du contrôle-commande choisie est de type hiérarchisée et distribuée. Dans la première partie de la thèse, les travaux portent sur la commande de la turbine d'une éolienne autour des points de fonctionnement classiques mais également autour des points à puissance extraite réduite. En fait, cela relève d’une condition de fonctionnement nécessaire pour l'atteinte des objectifs imposés au pilotage d'un parc éolien.Dans la deuxième partie, le problème du contrôle à l'échelle d'un parc est posé sous la forme d'une optimisation distribuée parmi les turbines. Deux nouveaux algorithmes d'optimisation métaheuristique sont proposés et leur performance testée sur différents exemples de parcs éoliens. Les deux algorithmes s'appuient sur la méthode d'optimisation par essaim particulaire, qui est ici modifiée et adaptée pour les cas d'application aux systèmes multi agents. L'architecture de contrôlecommande globale est enfin évaluée en considérant les dynamiques des turbines contrôlées. Les simulations effectuées montrent des gains potentiels significatifs en puissance.Finalement, dans la troisième partie de la thèse, l'introduction d'une nouvelle étape de coopération au niveau des contrôleurs locaux des turbines, par l'utilisation de la technique de contrôle par consensus, permet d'améliorer les performances du système global. / In this PhD work we focus on the wind farm (WF) active power control since some of the new set grid requirements of interest can be expressed as specifications on its injection in the electric grid. Besides, one of our main objectives is related to the wind farm power maximization problem under the presence on non-negligible wake effect. The chosen WF control architecture has a two-layer hierarchical distributed structure. First of all, the wind turbine (WT) control is addressed. Here, a nonlinear controller lets a WT work in classic zones of functioning as well as track general deloaded power references. This last feature is a necessary condition to accomplish the WF control specifications. Secondly, the high level WF control problem is formulated as an optimization problem distributed among the WTs. Two novel distributed optimization algorithms are proposed, and their performance tested on different WF examples. Both are based on the well-known particle swarm optimization algorithm, which we modify and extend to be applicable in the multi-agent system framework. Finally, the overall WF control is evaluated by taking into account the WTs controlled dynamics. Simulations show potential significant power gains. Eventually, the introduction of a new control level in the hierarchical structure between the WF optimization and the WTs controllers is proposed. The idea is to let further cooperation among the WT local controllers, via a consensusbased technique, to enhance the overall system performance.

[pt] A solução do problema de planejamento da expansão da transmissão (PET) tem por objetivo geral identificar reforços a serem construídos na rede de forma a garantir a adequada interligação entre carga e geração, previstos para um determinado horizonte de estudo. No processo de solução desse problema, busca-se manter o equilíbrio ótimo entre os custos envolvidos (investimento e operação) e os níveis de qualidade e desempenho na operação do sistema reforçado. Nesse sentido, é proposta nesta dissertação de mestrado uma ferramenta de otimização especializada para solução do problema PET, a qual é baseada na técnica metaheurística Algoritmo Genético. A ferramenta proposta, denominada Algoritmo Genético Especializado (AGE-PET), faz uso de informações heurísticas fundamentadas em análises atualizadas de fluxo de potência da rede realizadas durante o processo evolutivo de solução do problema. Essas informações heurísticas são traduzidas por meio de índices de sensibilidade, os quais são integrados aos operadores genéticos inerentes à ferramenta, conduzindo a solução do problema na direção de planos de expansão de boa qualidade. Para análise e validação da metodologia proposta, é solucionado o problema PET estático de longo prazo, considerando o modelo linearizado DC com perdas ôhmicas e atendimento do critério de segurança N-1 para a rede de transmissão. Sistemas elétricos de transmissão com diferentes características e dimensões, incluindo um subsistema atual da rede interligada brasileira, são empregados nos estudos realizados. / [en] The main goal in the solution of the transmission expansion planning (TEP) is to identify reinforcements to be built in the network in order to guarantee the adequate interconnection between load and electric power generation, both foreseen for a given future planning horizon. In the process of solving this problem, the aim is to maintain the optimal balance between the costs involved (investment and operation) and the levels of quality and performance in the operation of the reinforced system. Thus, it is proposed in this dissertation a specialized optimization tool for solving the TEP problem, which is based on the metaheuristic Genetic Algorithm technique. The proposed tool, called Specialized Genetic Algorithm (SGA-TEP), makes use of heuristic information based on updated network power flow analyses carried out during the evolutionary process of solving the problem. This heuristic information is translated by means of sensitivity indices, which are integrated with the genetic operators inherent to the tool, leading to the solution of the problem in the direction of good quality expansion plans. For analysis and validation of the proposed methodology, the long-term static TEP problem is solved, considering the linearized DC model with ohmic losses and the compliance of the N-1 security criterion for the transmission network. Electric transmission systems with different characteristics and dimensions, including a recent subsystem of the Brazilian interconnected grid, are used in the case studies.