  The algorithms of multi-objective optimisation had a relative growth in the
last years. Thereby, it's 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($\Delta$), Averaged Hausdorff distance ($\Delta_p$), 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 tho know the true
Pareto set and 2) Medium computational cost when compared with Hypervolume.
