@proceedings {6707,
	title = {Discrete Large-scale Multi-Objective Teaching-Learning-Based Optimization Algorithm},
	journal = {4th International Conference on Networking, Information Systems \& Security},
	year = {2021},
	abstract = {
	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.

},
	url = {https://dl.acm.org/doi/abs/10.1145/3454127.3456609},
	author = {Aouadj, Wafa and Abdessemed, Mohamed-Rida and Rachid Seghir}
}
