Team Formation Optimization Problem Resolution Based On The Knowledge Difference Cost

In collaborative projects context, collaborators are usually partitioned into working teams to perform collaborative tasks in a shared environment or platform. Indeed, one of the grouping criteria most used is based on the collaborators’ skills and on forming similar teams according to the skills of their members which is a costly and a complex problem. In this paper, we address the optimization problem of forming automatically working teams by minimizing the knowledge-difference cost among formed teams. The knowledge index of each group depends on the skills of their collaborators’ members in the form of a sum of collaborators’ fuzzy rating indexes. The proposed algorithm is called improved particle swarm optimization with multi-parent order crossover (IPSOMPOX). To evaluate the performance of the proposed algorithm, we apply it on a real-life DBLP dataset with different numbers of teams and collaborators. The proposed algorithm is compared with the standard PSO and has proved better performance as shown in our results.