  The importance-sampling Monte Carlo algorithm appears to be the universally
optimal solution to the problem of sampling the state space of statistical
mechanical systems according to the relative importance of configurations for
the partition function or thermal averages of interest. While this is true in
terms of its simplicity and universal applicability, the resulting approach
suffers from the presence of temporal correlations of successive samples
naturally implied by the Markov chain underlying the importance-sampling
simulation. In many situations, these autocorrelations are moderate and can be
easily accounted for by an appropriately adapted analysis of simulation data.
They turn out to be a major hurdle, however, in the vicinity of phase
transitions or for systems with complex free-energy landscapes. The critical
slowing down close to continuous transitions is most efficiently reduced by the
application of cluster algorithms, where they are available. For first-order
transitions and disordered systems, on the other hand, macroscopic energy
barriers need to be overcome to prevent dynamic ergodicity breaking. In this
situation, generalized-ensemble techniques such as the multicanonical
simulation method can effect impressive speedups, allowing to sample the full
free-energy landscape. The Potts model features continuous as well as
first-order phase transitions and is thus a prototypic example for studying
phase transitions and new algorithmic approaches. I discuss the possibilities
of bringing together cluster and generalized-ensemble methods to combine the
benefits of both techniques. The resulting algorithm allows for the efficient
estimation of the random-cluster partition function encoding the information of
all Potts models, even with a non-integer number of states, for all
temperatures in a single simulation run per system size.
