Alexandre, Rafael FredericoBarbosa, Carlos Henrique Nogueira de ResendeVasconcelos, João Antônio de2018-10-092018-10-092018ALEXANDRE, R. F.; BARBOSA, C. H. N. de R.; VASCONCELOS, J. A. de. LONSA : a labeling-oriented non-dominated sorting algorithm for evolutionary many-objective optimization. Swarm and Evolutionary Computation, v. 38, p. 275-286, fev. 2018. Disponível em: <https://www.sciencedirect.com/science/article/pii/S2210650217306806>. Acesso em: 03 mai. 2018.22106502http://www.repositorio.ufop.br/handle/123456789/10328Multiobjective algorithms are powerful in tackling complex optmization problems mathematically represented by two or more conflicting objective functions and their constraints. Sorting a set of current solutions across non-dominated fronts is the key step for the searching process to finally identify which ones are the best solutions. To perform that step, a high computational effort is demanded, especially if the size of the solution set is huge or the mathematical model corresponds to a many-objective problem. In order to overcome this, a new labeling-oriented algorithm is proposed in this paper to speed up the solution-to-front assignment by avoiding usual dominance tests. Along with this algorithm, called Labeling-Oriented Non-dominated Sorting Algorithm (LONSA), the associated methodology is carefully detailed to clearly explain how the classification of the solution set is successfully achieved. This work presents a comparison between LONSA and other well-known algorithms usually found in the literature. The simulation results have shown a better performance of the proposed algorithm against nine chosen strategies in terms of computational time as well as number of comparisons.en-USrestritoMultiobjective optimizationSolution labelingNon-dominanceArtigo publicado em periodicohttps://www.sciencedirect.com/science/article/pii/S2210650217306806#!