Navegando por Assunto "Bio-inspired algorithms"

Agora exibindo 1 - 3 de 3

Acesso aberto (Open Access)
Análise e otimização de coberturas de invisibilidade esféricas estratificadas em camadas homogêneas e isotrópicas
(Universidade Federal do Pará, 2012-06-29) MARTINS, Tiago Carvalho; DMITRIEV, Victor Alexandrovich; http://lattes.cnpq.br/3139536479960191
In this work, we analyze and optimize invisibility cloaks stratified in concentric spherical homogeneous and isotropic layers, in which both the total scattering cross section and the number of layers have been minimized. In order to increase the range of frequencies in which there is invisibility, dispersive effects are taken into account. In microwaves, We obtained discretized invisibility cloaks (obtained from anisotropic cloaks) with significant reductions (greater than 20 dB) of the total scattering cross section, for only 20 layers (which is achieved in the literature with at least 80 layers). We obtained a reduction of 32 dB in the total scattering cross section for a cloak stratified in only 13 layers. This result was obtained in microwaves. In microwaves, we optimized dispersive invisibility cloaks which present a bandwidth 5.4 times larger than would be obtained by a optimized cloak without dispersive effects. Cloaks are designed to operate in optical frequencies, for a wide range of frequencies.
Acesso aberto (Open Access)
Avaliação de técnicas de paralelização de algoritmos bioinspirados utilizando computação GPU: um estudo de casos para otimização de roteamento em redes ópticas
(Universidade Federal do Pará, 2015-03-06) TADAIESKY, Vincent Willian Araújo; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858
The applications on distribution logistics are diverse, such as the transportation planning and delivery of goods or in telecommunication networks data routing. Given the breadth and capillarity of these problems, studies have been developed to reduce network operating costs of this magnitude, especially regarding the demand for electricity. Therefore, this work proposes a method of resolution of routing problems with high demand. The proposed method is based on bio-inspired algorithms, which combined with other methods, ensure the integrity of the solutions, as well as its proximity to optimum. Nevertheless, such algorithms becomes computationally expensive as the application complexity in question grows and, therefore, multiprocessor environment, like GPU Computing platforms, has being widely used to increase bio-inspired algorithms performance. Thus, this work aims perform tests about the widespread parallelization techniques of these algorithms, intending to make an evaluation of which strategies has better relation with each tested algorithm. In order to do this, the routing problem in WDW optics networks with high demand level was used as a case study, in which it is needed define which are the better routes to demands sent simultaneously. The algorithms that assisted the tests were Genetic Algorithms and Swarm Particle Optimization, which are highly disseminated. The results show that the parallelization strategy to be used depends as much on the platform in which has been implemented, as the problem to be treaty.
Acesso aberto (Open Access)
Geração de tarefas de ensino adaptadas através de algoritmos bio-inspirados para crianças em fase inicial da alfabetização
(Universidade Federal do Pará, 2018-09-14) SOUZA JÚNIOR, Gilberto Nerino; MONTEIRO, Dionne Cavalcante; http://lattes.cnpq.br/4423219093583221; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858
Advances in learning systems over the past two decades have enabled the development of technologies that help in the engagement of students. Although these systems may use behavioral procedures to improve reading skills, better outcomes for each student are obtained in the manual elaboration of a set of tasks by educational experts. However, the use of a manual process requires too much time, effort and subjectivity for the creation of tasks. Additionally, even with the aid of computational processes, the automatic generation may be impracticable due to the high search space for the possible combinations of tasks. This process could consider adapting the difficulty of a task to the student's knowledge, something little explored in educational work for children at the beginning of reading learning. The present thesis implements an approach to generate teaching tasks from the Matching-to-Sample procedure, adapting its difficulties through bio-inspired optimization meta-heuristics. This approach uses pre-test results applied to students and the configuration of teaching contents determined by educational tutors; these data allow the use of the algorithms to generate tasks and then the tasks can be presented in learning software. Experiments demonstrated a better convergence of the genetic algorithms for this domain, being able to generate tasks on a level of difficulty adapted to the students, and also according to pretests and configurations of attributes of the tasks defined by behavioral psychologists. As validation for this study, the tasks were applied to a group of students in the early stages of literacy achieving satisfactory effects in the individual learning process. In addition, two interactive learning software were implemented through a digital game and a web application, where the use of the digital game with playful features showed superior acceptance in the use of teaching tasks adapted for children in the initial phase of literacy.