Currently the docs and the tutorial don't dive very deep into the HyperVolume input parameters when used as a convergence metric. In the examples they're (on the surface) quite arbitraily defined, especially the maximums, sometimes being 1, 1.01 or 1.1, and sometimes being 2 or 3. While the docs currently describes what it does and outputs, it doesn't really describe how the input parameters change its behaviour.

The docs and tutorial could include the following things:

• How the input parameters change the HyperVolume behaviour as an convergence metric
• How you can manually (or experimentally) determine realistic minimum and maximum HyperVolume input values
• When and how HyperVolume.from_outcomes() can be used (when expected values are defined, etc.)

The docs can be expanded at the HyperVolume class while the tutorial could be expanded at the Tracking convergence part of the Directed search tutorial.