  We study the mechanism behind dynamical trappings experienced during
Wang-Landau sampling of continuous systems reported by several authors.
Trapping is caused by the random walker coming close to a local energy
extremum, although the mechanism is different from that of critical slowing
down encountered in conventional molecular dynamics or Monte Carlo simulations.
When trapped, the random walker misses entire or even several stages of
Wang-Landau modification factor reduction, leading to inadequate sampling of
configuration space and a rough density-of-states even though the modification
factor has been reduced to very small values. Trapping is dependent on specific
systems, the choice of energy bins, and Monte Carlo step size, making it highly
unpredictable. A general, simple, and effective solution is proposed where the
configurations of multiple parallel Wang-Landau trajectories are inter-swapped
to prevent trapping. We also explain why swapping frees the random walker from
such traps. The efficacy of the proposed algorithm is demonstrated.
