# What are useful plots/statistics/metrics when analyzing the solution sensitivity in multi-objective optimization?

Consider an optimization problem with $$n>3$$ objectives. For handling this there exists often two approaches:

a) some weighting of the objectives,

b) fix an order of objectives and then optimize each one after another, allowing the objective of the previous optimization to only degrade by a small factor.

The weighting or the small factor is often difficult to estimate a priori. Assume now someone who wants to find a solution with a good balance between the objective, by manually tweaking these parameters and wants to identify cases by say 1% degrading in this objective I can gain 80% gain in a different objective.

What are useful plots/statistics/metrics for this, that help guide finding good parameters?