This paper presents a teaching-learning-based optimization algorithm for discrete large-scale multi-objective problems (DLM-TLBO). Unlike the previous variants, the learning strategy used by each individual and the acquired knowledge are defined based on its level. The proposed approach is used to solve a bi-objective object clustering task (B-OCT) in a swarm robotic system, as a case study. The simple robots have as mission the gathering of a number of objects distributed randomly, while respecting two objectives: maximizing the clustering quality, and minimizing the energy consumed by these robots. The simulation results of the proposed algorithm are compared to those obtained by the well-known algorithm NSGA-II. The results show the superiority of the proposed DLM-TLBO in terms of the quality of the obtained Pareto front approximation and convergence speed.