  Population Monte Carlo simulations in the form commonly referred to as
population annealing can serve as a useful meta-algorithm for simulating
systems with complex free-energy landscapes. In the present paper we provide an
easily accessible introduction to the approach, focusing on spin systems as
simple example problems. While the method is very general and powerful, it also
comes with a number of tunable parameters. Here, we discuss the question of an
optimal choice of resampling protocol, that is shown to have significant
influence on the quality of results. While population annealing is an excellent
fit to the paradigm of massively parallel simulations, limitations in the
availability of parallel resources and especially memory can provide a
bottleneck to its efficacy. As we demonstrate for results of the Ising
ferromagnetic and spin-glass models, weighted averages of smaller-scale runs
can be easily combined to reduce both systematic and statistical errors in
order to avoid such bottlenecks.
