PAGMO/PYGMO: Anyone understand the options for Corana’s Simulated Annealing?
Gonna answer my own question here. I climbed into the actual .cpp code and found the answers.
In Corana's method, you select how many total iterations N of annealing you want. Then the minimization is a nested series of loops where you vary the step sizes, number of step-size adjustments, and temperature values at user-defined intervals. In PAGMO, they changed this so you explicitly specify how many times you will do these. Those are the n_* parameters and bin_size. I don't think bin_size is a good name here, because it isn't actually a size. It is the number of steps taken through a bin range, such that N=n_T_adj * n_range_adj * bin_range. I think just calling it n_bins or n_bins_adj makes more sense. Every bin_size function evaluations, the stepsize is modified (see below for limits). 
In Corana's method you specify the multiplicative factor to decrease the temperature each time it is needed; it could be that you reach the minimum temp before running out of iterations, or vice versa. In PAGMO, the algorithm automatically computes the temperature-change factor so that you reach Tf at the end of the iteration sequence: r_t=(Tf/Ts)**(1/n_T_adj). 
The start_range is, I think, a bad name for this variable. The stepsize in the alorithm is a fraction between 0 and start_range which defines the width of the search bins between the upper and lower bounds for each variable. So if stepsize=0.5, width=0.5*(upper_bound-lower_bound). At each iteration, the step size is adjusted based on how many function calls were accepted. If the step size grows larger than start_range, it is reset to that value. I think I would call it step_limit instead. But there you go.
I'm using the PYGMO package to solve some nasty non-linear minimization problems, and am very interested in using their simulated_annealing algorithm, however it has a lot of hyper-parameters for which I don't really have any good intuition. These include:
Ts (float) – starting temperature
 Tf (float) – final temperature
 n_T_adj (int) – number of temperature adjustments in the annealing schedule
 n_range_adj (int) – number of adjustments of the search range performed at a constant temperature
 bin_size (int) – number of mutations that are used to compute the acceptance rate
 start_range (float) – starting range for mutating the decision vector
Let's say I have a 4 dimensional geometric registration (homography) problem with variables and search ranges:
x1: [-10,10] (a shift in x)
 x2: [10,30]  (a shift in y)
 x3: [-45,0]  (rotation angle)
 x4: [0.5,2]  (scaling/magnification factor)
And the cost function for a random (bad) choice of values is 50. A good value is around zero. 
I understand that Ts and Tf are for the Metropolis acceptance criterion of new solutions. That means Ts should be about the expected size of the initial changes in the cost function, and Tf small enough that no more changes are expected. 
In Corana's paper, there are many hyperparameters listed that make sense: N_s is the number of evaluation cycles before changing step sizes, N_T are the number of step-size changes before changing the temperature, and r_T is the factor by which the temp is reduced each time. However, I can't figure out how these correlate to pygmo's parameters of n_T_adj, n_range_adj, bin_size, and start_range.
I'm really curious if anyone can explain how pygmo's hyperparameters are used, and how they relate to the original paper by Corana et al?