A convergence indicator for multi-objective optimisation algorithms.

• Autor(a) principal: Santos, Thiago Fontes
• Data de Publicação: 2018
• Outros Autores: Xavier, Sebastião Martins
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFOP
• Texto Completo: http://www.repositorio.ufop.br/handle/123456789/11525; http://dx.doi.org/10.5540/tema.2018.019.03.0437
• topic: Shannon entropy; Performance measure

The 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.