External cluster evaluation for a varying number of cluster
Most measures such as (Adjusted,Normalized) Rand index / normalized mutual information do perform an optimal n:m-like comparison by design.

They avoid having to make the assumption that cluster A in one clustering is cluster X in the other; instead they look at the resulting object pairs instead. Methods that rely on "set matching" are very uncommon because of this reason. It appears to mostly be  classification people that think you should be using the precision and recall of a 1:1 matching, because that is the measure they always use.

In the Rand index, Mutual Information, etc. moving a single element from a cluster into a small second cluster reduces the quality roughly by 1/n initially; and once the resulting cluster starts becoming larger even less than that (until eventually when most elements have moved, the index starts I proving again). That is not what I would call inappropriately worse, nor "not very good at comparing clusters of different sizes". I think you should provide examples for your claim.

I am not convinced by the Rezaei paper. On one hand, they seem to assume that behavior must be linear in their synthetic data change (Rand index would often be linear; it's the desirable adjustment for chance that produces the non-linearity, and his can we'll be used as an argument that the measure should not be linear). Second, they seem to require that every cluster must have equal weight, so increasing the size of a "perfectly found" cluster must not affect the outcome. Here, again, I beg to disagree. Cluster influence should depend on the number of objects in the cluster. From the "cluster size imbalance" plot my take is that once you add a lot of 1-element clusters, the proposed PSI measure starts looking much worse, because it cannot deal with such noise.