Understanding Global Optimization VI

Question

Hello,
I have a task which requires using global potimization, however, I am not really familiar with it. I've tried looking at the example VI's, however I have a hard time understaniding where my data would go.
My actual task includes measuring mechanical impedance (as a spectrum of complex numbers) and fitting a model of 4 parameters on it with the global optimization finding the best values. (That's how it was described by previous ones to work the best, that's why I'm trying to implement it this way). The outcome of this process would be minimizing the difference between the measured impedance and the model fitted on it. 
Our research group uses a program with a working implementation for this, but we want to implement this model fitting it in Labview.
The impedance data is a complex number, so it has real and imaginary parts as well, with some model parameters being determinde by both real and imaginary part of the data.
I've tried using a "simple" Lev-Mar fit on my data as a first trial by modifying the VIs from this topic (using typecasting to input complex data to the Lev-Mar vi), but it only found values similar to my desired fit (done with the existing program) if I set the estimation very close to the values I get in my program. That's why I wanted to move on to global optimization, to get labview to find these estimation values in some boundaries.
And that's where I am stuck:
I have a complex impedance (so real and imaginary parts for frequency points) and an equation that is supposed to give the output. If I take for example the "Two Circles Optimization" example vi, where should I drop my impedance spectrum for the VI to analyze? As the Lev-Mar had clear inputs for my data (as X and Y) I knew where to wire my data, but with the global optimization I do not know, where to wire it.
I have an impression that I should use a VI for the objective function to handle the complex input as well. 
The objective function for the optimization should be the mean of "[Z(omega)-Zfit(omega)]/Z(omega)" for each frequency points, with Z being the measured and Zfit being the fitted impedance with the parameter estimation. Zfit=A+j*omega*B+(C-j*D)/(omega^[(2/pi)*arctan(C/D)] (j is the imaginary unit)
So where should I put my impedance data in the example vi to be used by the parameter function?
Thank you, hope my explanation is more or less clear
Engage! using LV2012
Solved!
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Answer 1

Hello,
I had some time to go on with this part of the project.  I figured out finally the way I should use these vis to take my data, calculate the objective function and pass the parameters in the end. However, I am not really familiar with optimization and have a hard time tweaking them to work. 
I have created a simple vi to include a few kinds of optimization and values I receive from and old existing program I want to replace. (It is said to include a kind of global optimization with a random search method, however, I am not sure of this and cannot ask the original programmer.)
However, there are huge differences in the paramters and the adherences of the curves to the original data. What should I do to receive more or less similar data from Labview to my previous program (MyProgram)? (Something like less than 5 percent difference.)
And I have a strange problem as well: if I enter some initial values I receive an error that says: error in Armijo stepsize reduction, and I do not know, what values make this appear and what makes it disappear. So I randomly changed this to some value and finally they disappeared.
Can you have a look at my code, what should I improve? (I know, it is a complete mess, it is just for demonstrating purposes fo seeing the differrences of the optimization vis).
(My measurement is an impedance measurement and I want to get some parameters in the end: A, B, C and D).
Engage! using LV2012
Attachments:
OptTest.llb ‏142 KB