Tsallis's generalized simulated annealing (GSA), which has been widely used in many fields as a global optimization tool, is composed of three parts: visiting distribution, accepting rule, and cooling schedule. The most complicated of these is visiting distribution. Although Tsallis and Stariolo did provide a heuristic algorithm to generate a random number for the visiting distribution, empirical simulations have shown that it is inappropriate. In this study a more appropriate algorithm, provided by the inspiration of Mantegna, is composed of two steps. The first is the calculation v = x/|y|^1/, where x and y are two independent normal variates with zero means and standard deviation σ_x and σ_y respectively. The second is a nonlinear transformation w = [K(q) - 1]exp(-|v/C(q)|) + 1v. Theoretical arguments are provided for the choice of , σ_x, σ_y, and K(q); whereas for C(q), an empirical approximation is used instead. The range of q is in the open interval of (1.0, 3.0). It is shown that both old and new algorithms behave like a normal and Cauchy variates, respectively, when q is in the right limit of 1 or equals 2.0, values which match their corresponding visiting distributions exactly. The criteria for comparison are Kolmogorov's statistic and hypothesis testing. Based on the criteria, extensive simulations confirm that the new algorithm is superior to the old when q ∈ (1.0, 2.5]. As for q ∈ (2.5, 3.0), the values are about the same.