Projeto integrado de redes ópticas de longa distância e Metropolitanas usando algoritmos de inteligência computacional: estudo de caso para o estado de Pernambuco

Abstract

Nowadays, several network technologies with different prices and adaptations are appearing in the market. A network topology project involves several metrics; the metrics are used to evaluate a project. In the evaluation we use metrics such as robustness metrics (which help in the network’s ability to recover from a failure), blocking probability and energy consumption. The best way to optimize infrastructure in a network design would be to use the latest technologies, only the most efficient ones, even if such technologies are more expensive. However, of the metrics to be considered in this type of project, one of them is the cost (capital employed). Therefore, it is not always feasible to use the most expensive ones on the market. Many technical issues can help control the metrics of these projects, among which is the network topology (link interconnection). Multiobjective evolutionary algorithms (algorithms inspired by the evolution of the species) have been studied in the state of the art for the conception of network topologies. At the same time, clustering algorithms (algorithms specialized in separating samples into groups) have been used in other types of network studies. This study aimed to make use of computational intelligence algorithms in the construction of a network topology project, using the state of Pernambuco as a case study. In a first stage of the study, a clustering algorithm was used in the division of the state into groups. The intention of this part of the work was to measure the coverage of the network in relation to the entire size of the state, and thus ensure the completeness of the network. In addition, the clustering stage also aimed to propose a cost control model through the merging of different technologies for the network (Passive or active) depending on the function of the network segment. In a second step, an evolutionary multiobjective algorithm was used to compose several network topologies that served the clusters created in the previous step. This algorithm has evolved the various network topologies in order to improve four metrics, Blocking Probability, Cost, Energy Consumption and Algebraic Connectivity. The multiobjective algorithm was designed as a memetic algorithm, and, after a set of executions, the algorithm performances were compared with and without the alteration. The results of the tests, in the first stage, showed that the clustering techniques are quite efficient and adaptable to the proposed goal both in terms of network completeness and cost control. Already in the second stage, or multiobjective search stage, it was verified, through the use of a quality indicator (hypervolume), that there was an improvement of the algorithm in relation to convergence and diversity to the Pareto curve, with the use in its new form as memetic algorithm.