Un Enfoque Evolutivo Multi-Objetivo al Problema de la Construcción de Grupos de Estudiantes Universitarios

The creation of working groups of students in education is a common process that is often developed by the teacher intuitively. However, such a process is actually a complex task since various students and criteria must be taken into account. In general, these criteria are often in conflict because they are a reflection of the educational interests of teachers and on the other hand, the individual preferences of students. In this sense, this paper has as general goal: to propose a mathematicalcomputational solution that efficiently automatizes, in terms of computational time and solution quality, the creation of working groups of college students. The results obtained from two real scenarios of the Universidad Tecnica Estatal de Quevedo indicate that the proposal is an effective alternative to the traditional model. &nbsp

Creación automática de equipos de estudiantes universitarios: una experiencia desde la asignatura Inglés / Automatic Building of University Student Teams: an experience from English subject

Uno de los principales objetivos en la educación es lograr que los estudiantes desarrollen la capacidad de trabajo en equipo. Esta capacidad potencia la socialización entre los estudiantes y la resolución de problemas complejos. Comúnmente, la creación de estos equipos es realizada por el docente de la asignatura, quien debe tener en cuenta múltiples criterios como la presencia de un estudiante líder y equipos heterogéneos. Cuando la asignatura tiene poco estudiantes, esta tarea suele ser fácil. Sin embargo, cuando se debe tener en cuenta a numerosos estudiantes, la tarea se torna compleja y por lo general no existe garantía de que los equipos creados cumplan con los criterios deseados. En este sentido, con el objetivo de favorecer el desarrollo óptimo de esta tarea docente, la presente investigación propone una solución computacional que automatiza la creación de equipos de trabajo de estudiantes. Específicamente, la tarea de la creación de los equipos se modeló matemáticamente como un problema de optimización de tipo combinatorio y multi-objetivo, que fue resuelto a su vez por un algoritmo evolutivo basado en los conceptos de Dominancia de Pareto. Para validar las propuestas, se realizaron varios experimentos computacionales que involucran escenarios reales, relacionados con la Unidad de Aprendizaje Inglés en varias carreras de la Universidad Técnica Estatal de Quevedo. ABSTRACTOne of the main goals for Higher Education is to educate students to work in teams. Such a skill not only improves their social behavior in the community, but also the ability for solving complex problems. Usually, the process of making teams is carried out by professor of the subject, who has to take into account several criteria (e.g. the presence of leader, heterogeneity of the team according the level of knowledge, sex, among others). When the subject has just few students, this task becomes easy. However, in the case of classes with a large number of students, this task becomes complex and there is no warranty about the accomplishment of the considered criteria. In that sense, the present work proposes a computational solution that automatizes the task of student teams building. Specifically, it was approached as a multi-objective combinatorial optimization problem, which was solved using a Pareto Dominance-based algorithm. In order to validate the proposal we performed several computational experiments involving real case studies from the English subject of three careers at the Technical State University of Quevedo. Results show that the proposed approach is able to build balanced teams according to the considered criteria

Wind turbine blade geometry design based on multi-objective optimization using metaheuristics

Abstract: The application of Evolutionary Algorithms (EAs) to wind turbine blade design can be interesting, by reducing the number of aerodynamic-to-structural design loops in the conventional design process, hence reducing the design time and cost. Recent developments showed satisfactory results with this approach, mostly combining Genetic Algorithms (GAs) with the Blade Element Momentum (BEM) theory. The general objective of the present work is to define and evaluate a design methodology for the rotor blade geometry in order to maximize the energy production of wind turbines and minimize the mass of the blade itself, using for that purpose stochastic multi-objective optimization methods. Therefore, the multi-objective optimization problem and its constraints were formulated, and the vector representation of the optimization parameters was defined. An optimization benchmark problem was proposed, which represents the wind conditions and present wind turbine concepts found in Brazil. This problem was used as a test-bed for the performance comparison of several metaheuristics, and also for the validation of the defined design methodology. A variable speed pitch-controlled 2.5 MW Direct-Drive Synchronous Generator (DDSG) turbine with a rotor diameter of 120 m was chosen as concept. Five different Multi-objective Evolutionary Algorithms (MOEAs) were selected for evaluation in solving this benchmark problem: Non-dominated Sorting Genetic Algorithm version II (NSGA-II), Quantum-inspired Multi-objective Evolutionary Algorithm (QMEA), two approaches of the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multi-objective Optimization Differential Evolution Algorithm (MODE). The results have shown that the two best performing techniques in this type of problem are NSGA-II and MOEA/D, one having more spread and evenly spaced solutions, and the other having a better convergence in the region of interest. QMEA was the worst MOEA in convergence and MODE the worst one in solutions distribution. But the differences in overall performance were slight, because the algorithms have alternated their positions in the evaluation rank of each metric. This was also evident by the fact that the known Pareto Front (PF) consisted of solutions from several techniques, with each dominating a different region of the objective space. Detailed analysis of the best blade design showed that the output of the design methodology is feasible in practice, given that flow conditions and operational features of the rotor were as desired, and also that the blade geometry is very smooth and easy to manufacture. Moreover, this geometry is easily exported to a Computer-Aided Design (CAD) or Computer-Aided Engineering (CAE) software. In this way, the design methodology defined by the present work was validated