We carry out a study of the two-dimensional Blume-Capel model using the Wang-Landau Monte Carlo method which estimates the density of states g(E) directly. This work validates the applicability of this method to multiparametric systems, since only one computer run is needed for all range of macroscopic parameters (temperature, anisotropy, etc.). The location of the tricritical point is determined as kBTt/J=0.609(3), Dt/J=1.966(2), and is in excellent agreement with previous estimates. The free energy and the entropy, which are not directly accessible by conventional Monte Carlo simulations, are obtained simply using g(E).

Wang-Landau sampling (WLS) of large systems requires dividing the energy range into "windows" and joining the results of simulations in each window. The resulting density of states (and associated thermodynamic functions) are shown to suffer from boundary effects in simulations of lattice polymers and the five-state Potts model. Here, we implement WLS using adaptive windows.Instead of defining fixed energy windows (or windows in the energy-magnetization plane for the Potts model), the boundary positions depend on the set of energy values on which the histogram is flat at a given stage of the simulation. Shifting the windows each time the modification factor f is reduced, we eliminate border effects that arise in simulations using fixed windows. Adaptive windows extend significantly the range of system sizes that may be studied reliably using WLS.

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In this work we investigate the behavior of the microcanonical and canonical averages of the two-dimensional Ising model during the Wang-Landau simulation. The simulations were carried out using conventional Wang-Landau sampling and the $1/t$ scheme. Our findings reveal that the microcanonical average should not be accumulated during the initial modification factors \textit{f} and outline a criterion to define this limit. We show that updating the density of states only after every $L^2$ spin-flip trials leads to a much better precision. We present a mechanism to determine for the given model up to what final modification factor the simulations should be carried out. Altogether these small adjustments lead to an improved procedure for simulations with much more reliable results. We compare our results with $1/t$ simulations. We also present an application of the procedure to a self-avoiding homopolymer

In this work we propose a criterion to finish the simulations of the Wang-Landau sampling. Instead of determining a final modification factor for all simulations and every sample sizes, we investigate the behavior of the temperature of the peak of the specific heat during the simulations and finish them when this value varies bellow a given limit. As a result, different runs stop at different final modification factors. We show that in place of the temperature of the peak of the specific heat one can adopt alternatively the integrated heat transfer as a reference quantity. We apply this technique to the two-dimensional Ising model and a homopolymer. We verify that for the Ising model the mean order of the final modification factors are roughly the same for all lattice sizes, but for the homopolymer the order of the final modification factors increases with increasing polymer sizes. The results show that the simulations can be halted much earlier then it is conventional in Wang-Landau sampling, but manifold finite-size simulations are required in order to obtain accurate results. A brief application to the three-dimensional Ising model is also available.