Rare-Event Sampling: Occupation-Based Performance Measures for Parallel   Tempering and Infinite Swapping Monte Carlo Methods
  In the present paper we identify a rigorous property of a number of tempering-based Monte Carlo sampling methods, including parallel tempering as well as partial and infinite swapping. Based on this property we develop a variety of performance measures for such rare-event sampling methods that are broadly applicable, informative, and straightforward to implement. We illustrate the use of these performance measures with a series of applications involving the equilibrium properties of simple Lennard-Jones clusters, applications for which the performance levels of partial and infinite swapping approaches are found to be higher than those of conventional parallel tempering. 
J. D. Doll, Nuria Plattner, David L. Freeman, Yufei Liu and Paul   Dupuis
cond-mat.stat-mech cond-mat.mtrl-sci