<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aouadj, Wafa</style></author><author><style face="normal" font="default" size="100%">Abdessemed, Mohamed Rida</style></author><author><style face="normal" font="default" size="100%">Seghir, Rachid</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discrete Large-scale Multi-Objective Teaching-Learning-Based Optimization Algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 4th International Conference on Networking, Information Systems &amp; Security</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://dl.acm.org/doi/abs/10.1145/3454127.3456609</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>