Santos, Thiago FontesXavier, Sebastião Martins2019-06-112019-06-112018SANTOS, T. F.; XAVIER, S. M. A convergence indicator for multi-objective optimisation algorithms. TEMA. Tendências em Matemática Aplicada e Computacional, v. 19, n. 3, p. 437-448, 2018. Disponível em: <http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512018000300437>. Acesso em: 19 mar. 2019.2179-8451http://www.repositorio.ufop.br/handle/123456789/11525The algorithms of multi-objective optimisation had a relative growth in the last years. Thereby, it requires some way of comparing the results of these. In this sense, performance measures play a key role. In general, it’s considered some properties of these algorithms such as capacity, convergence, diversity or convergence-diversity. There are some known measures such as generational distance (GD), inverted generational distance (IGD), hypervolume (HV), Spread(∆), Averaged Hausdorff distance (∆p), R2-indicator, among others. In this paper, we focuses on proposing a new indicator to measure convergence based on the traditional formula for Shannon entropy. The main features about this measure are: 1) It does not require to know the true Pareto set and 2) Medium computational cost when compared with Hypervolume.en-USabertoShannon entropyPerformance measureArtigo publicado em periodicoTodo o conteúdo do periódico Tema, exceto onde identificado, está licenciado sob uma licença Creative Commons 4.0 que permite copiar, distribuir e transmitir o trabalho em qualquer suporte ou formato desde que sejam citados o autor e o licenciante. Fonte: Tema <http://www.scielo.br/scielo.php?script=sci_serial&pid=2179-8451&lng=en&nrm=iso>. Acesso em: 13 abr. 2019.http://dx.doi.org/10.5540/tema.2018.019.03.0437