01.1 - Graduação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/2
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Item Análise de performance de algoritmos estocásticos aplicados ao problema do caixeiro viajante(2024-10-09) Lima, Lucas Gabriel Oliveira Sales; Monteiro, Cleviton Vinícius Fonsêca; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/7636465842833021Optimization algorithms are increasingly relevant tools in modern companies because they are capable of optimizing processes and resources, ensuring more efficient results and timely processing for decision making. Comparing these algorithms is a common process during their adoption studies. However, the use of complex methodologies can often lead to the choice of an imprecise algorithm, since its result may not reflect the reality of a company seeking to implement applications with limited resources. In view of this problem, the need arises to evaluate these algorithms from a new perspective. The main objective of this work is to propose a reflection on the way experiments in algorithms are conducted. The present study carried out experiments with optimization algorithms using computational resources similar to those found in most companies, comparing them with another work in which optimizations and tunings were used in these same algorithms. For the experiment, the traveling salesman problem was used, through 15 benchmarking divided into 3 categories, according to the size of each article. Finally, statistical metrics were obtained for the performance of each algorithm, which, when compared to the reference article, showed shorter execution times without compromising the accuracy of the results. Probabilistic algorithms are of great financial importance to companies that need to manage resources quickly, such as airports and shipyards. Therefore, the appropriate choice of parameters provides a more accurate view of reality.Item Alocação otimizada de horários acadêmicos com disponibilidade restrita de professores usando algoritmos genéticos(2022-06-01) Demiro, Matheus Paulo dos Santos; Garrozi, Cícero; http://lattes.cnpq.br/0488054917286587; http://lattes.cnpq.br/8926398361586659The generation of academic timetables is one of the most complex and arduous activities faced by educational institutions at the beginning of each academic period. In most cases, the solution found for this problem, commonly called “timetabling” in the literature, is performed manually, which makes the process very tiring and time-consuming for institutions. This problem is considered a great challenge in combinatorial optimization, due to the wide set of variables and constraints involved, being considered an NP-Complete problem, where there is no possibility of solution through conventional programming methods. This article deals with the use of genetic algorithm techniques to find an optimal solution to the problem of scheduling academic schedules that takes into account the restrictions of the student and the faculty, in order to favor the academic performance of students and adapt to availability from the students . teachers. For this work, it is expected to develop a genetic algorithm that is able to obtain valid results that meet the constraints of the problem in a reasonably considerable time. Technically, it is expected that the algorithm, from a set of input data, processes and returns a solution that has the highest fitness value - the lowest number of infractions committed - between generations of individuals (solutions). This article uses data from the Information Systems course grid at the Federal University of Rio Grande Norte as a base. After modifications in the base and the experiments were carried out, the genetic algorithm proved to be efficient and managed to achieve the objectives, generating adequate academic schedules and compatible with the established restrictions.