Dans ce travail, nous présentons trois nouveaux algorithmes pour le problème de la clique de poids maximum. Les trois algorithmes dépendent d'un ordre initial des sommets. Deux ordres sont considérés, l'un en fonction de la pondération des sommets et l'autre en fonction de la taille voisinage des sommets. Le premier algorithme, que nous avons appelé BITCLIQUE, est une algorithme de séparation et évaluation. Il réunit efficacement plusieurs idées déjà utilisées avec succès pour résoudre le problème, comme l'utilisation d'une heuristique de coloration pondérée en nombres entiers pour l'évaluation ; et l'utilisation de vecteurs de bits pour simplifier les opérations sur le graphe. L'algorithme proposé surpasse les algorithmes par séparation et évaluation de l'état de l'art sur la plupart des instances considérées en terme de nombre de sous-problèmes énumérés ainsi que en terme de temps d'exécution. La seconde version est un algorithme des poupées russes, BITRDS, qui intègre une stratégie d'évaluation et de ramification de noeuds basée sur la coloration pondérée. Les simulations montrent que BITRDS réduit à la fois le nombre de sous-problèmes traités et le temps d'exécution par rapport à l'algorithme de l'état de l'art basée sur les poupées russes sur les graphes aléatoires avec une densité supérieure à 50%. Cette différence augmente à la mesure que la densité du graphe augmente. D'ailleurs, BITRDS est compétitif avec BITCLIQUE avec une meilleure performance sur les instances de graphes aléatoires avec une densité comprise entre 50% et 80%. Enfin, nous présentons une coopération entre la méthode poupées russes et la méthode de ``Resolution Search''. L'algorithme proposé, appelé BITBR, utilise au même temps la coloration pondérée et les limites supérieures donnés par les poupées pour trouver un ``nogood''. L'algorithme hybride réduit le nombre d'appels aux heuristiques de coloration pondérée, atteignant jusqu'à 1 ordre de grandeur par rapport à BITRDS. Plusieurs simulations sont réalisées avec la algorithmes proposés et les algorithmes de l'état de l'art. Les résultats des simulations sont rapportés pour chaque algorithme en utilisant les principaux instances disponibles dans la littérature. Enfin, les orientations futures de la recherche sont discutées. / In this work, we present three new exact algorithms for the maximum weight clique problem. The three algorithms depend on an initial ordering of the vertices. Two ordering are considered, as a function of the weights of the vertices or the weights of the neighborhoods of the vertices. This leads to two versions of each algorithm. The first one, called BITCLIQUE, is a combinatorial Branch & Bound algorithm. It effectively combines adaptations of several ideas already successfully employed to solve the problem, such as the use of a weighted integer coloring heuristic for pruning and branching, and the use of bitmap for simplifying the operations on the graph. The proposed algorithm outperforms state-of-the-art Branch & Bound algorithms in most instances of the considered in terms of the number of enumerated subproblems as well in terms of computational time The second one is a Russian Dolls, called BITRDS, which incorporates the pruning and branching strategies based on weighted coloring. Computational tests show that BITRDS reduces both the number of enumerated subproblems and execution time when compared to the previous state-of-art Russian Dolls algorithm for the problem in random graph instances with density above 50%. As graph density increases, this difference increases. Besides, BITRDS is competitive with BITCLIQUE with better performance in random graph instances with density between 50% and 80%. Finally, we present a cooperation between the Russian Dolls method and the Resolution Search method. The proposed algorithm, called BITBR, uses both the weighted coloring and upper bounds given by the dolls to find a nogood. The hybrid algorithm reduces the number of coloring heuristic calls, reaching up to 1 order of magnitude when compared with BITRDS. However, this reduction decreases the execution time only in a few instances. Several computational experiments are carried out with the proposed and state-of-the-art algorithms. Computational results are reported for each algorithm using the main instances available in the literature. Finally, future directions of research are discussed.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / A utilizaÃÃo de vetores de bits à prÃtica corrente na representaÃÃo de conjuntos por endereÃamento direto com o intuito de reduzir o espaÃo de memÃria necessÃrio e melhorar o desempenho de aplicaÃÃes com uso de tÃcnicas de paralelismo em bits. Nesta dissertaÃÃo, examinamos implementaÃÃes para representaÃÃo de conjuntos por endereÃamento direto. A estrutura bÃsica nessas implementaÃÃes à o vetor de bits. No entanto, alÃm dessa estrutura bÃsica, implementamos tambÃm duas variaÃÃes. A primeira delas consiste em uma estratificaÃÃo de vetores de bits, enquanto a segunda emprega uma tabela de dispersÃo. As operaÃÃes associadas Ãs estruturas implementadas sÃo a inclusÃo ou remoÃÃo de um elemento do conjunto e a uniÃo ou interseÃÃo de dois conjuntos. Especial atenÃÃo à dada ao uso de paralelismo em bits nessas operaÃÃes. As implementaÃÃes das diferentes estruturas nesta dissertaÃÃo utilizam uma interface e uma implementaÃÃo abstrata comuns, nas quais as operaÃÃes sÃo especificadas e o paralelismo em bits à explorado. A diferenÃa entre as implementaÃÃes està apenas na estrutura utilizada. Uma comparaÃÃo experimental à realizada entre as diferentes estruturas utilizando algoritmos enumerativos para o problema de conjunto independente mÃximo. Duas abordagens sÃo utilizadas na implementaÃÃo de algoritmos enumerativos para o problema de conjunto independente mÃximo, ambas explorando o potencial de paralelismo em bits na representaÃÃo do grafo e na operaÃÃo sobre subconjuntos de vÃrtices. A primeira delas à um algoritmo do tipo {em branch-and-boound} proposto na literatura e a segunda emprega o mÃtodo das bonecas russas. Em ambos os casos, o uso de paralelismo em bits proporciona ganhos de eficiÃncia quando empregado no cÃlculo de limites inferiores baseados em cobertura por cliques. Resultados de experimentos computacionais sÃo apresentados como forma de comparaÃÃo entre os dois algoritmos e como forma de avaliaÃÃo das estruturas implementadas. Esses resultados permitem concluir que o algoritmo baseado no mÃtodo das bonecas russas à mais eficiente quanto ao tempo de execuÃÃo e quanto ao consumo de memÃria. AlÃm disso, os resultados experimentais mostram tambÃm que o uso de estratificaÃÃo e tabelas de dispersÃo permitem ainda maior eficiÃncia no caso de grafos com muito vÃrtices e poucas arestas. / The use of bit vectors is a usual practice for represent sets by direct addressing with the aim of reduce memory consumed and improve efficiency of applications with the use of bit parallel techniques. In this text, we study implementations for represent sets by direct addressed. The basic structure in this implementations is the bit vector. Besides that basic implementation, we implement two variations also. The first one is a stratification of the bit vector, while the second uses a hash table. The operations linked to the implemented structure are include and remove an element and the union and intersection of two sets. Especial attention is given to the use of bit parallel in this condition. The implementation of the different structures in this work use an base interface and a base abstract class, where the operations are defined and the bit parallel is used. An experimental comparative between this structures is carry out using enumerative algorithms for the maximum stable set problem. Two approaches are used in the implementation of the enumerative algorithms for the maximum stable set problem, both using the bit parallel in the representation of the graph and on the operations with subsets of vertices. The first one is a known branch-and-bound algorithm and the second uses the Russian dolls method. In both cases, the use of bit parallel improve efficiency when the lower bounds are calculated based in a clique cover of the vertices. The results of computational experiments are presented as comparison between the two algorithms and as an assessment of the structures implemented. These results show that the algorithm based on the method Russian Dolls is more efficient regarding runtime and the memory consumed. Furthermore, the experimental results also show that the use stratification and hash tables also allow more efficiency in the case of sparse graphs.

Tato práce se zabývá technologií Multiprotocol Label Switching a to zejména moderními metodami, které je možné použít v rámci této technolologie. Jako příklad lze uvést využití podpory kvality služeb při směrování. V práci jsou navrhnuty a simulovány různé topologie a scénáře, které ověřují možnosti využití MPLS v podpoře kvality služeb.