Student group formation multiobjective optimization with a hybrid of genetic algorithm and particle swarm optimization

Abstract

Importance of interaction among students in university classroom has been increasing because acquiring skills through the interaction can be the basis of future collaboration with diverse people. In this article, the author suggested multiobjective optimization of student group formation for smooth and effective groupwork in university classroom based on this argument. The author applied a hybrid of genetic algorithm and particle swarm optimization (GAPSO) to the multiobjective optimization. Also, the author focused on learning performance and attendance of each student and attepted to find group formation so that the distribution of learning performance and attendance in each student group become as homogeneous among the groups as possible. Comparing with hillclimbing algorithm, GA only, and PSO only in the group formation optimization, the optimization algorithm with GAPSO has the potential to find as many optimum as possible in short time.