No optimizer is good in this case.
That is, all of the classical optimizers ABSOLUTELY assume that your objective is a well defined function, in the sense that if you pass it the same set of values twice in a row, you would get the same result back. This is terribly important in how they are written.
Depending on the optimizer, some require different degrees of differentiability, smoothness. The optimizers like fmincon, fminunc, etc., assume that your function is everywhere differentiable. Without that, they can fail. Other optimizers, fminsearch, for example, can tolerate failures of differentiability, but they still can easily get lost on a problem that is not differentiable. Others are more extreme yet, for example, GA, for example, can even handle discontinuous problems. But even there, GA does not worry about noise in the objective function itself, presuming that it does not exist. That is, GA presumes your objective function is deterministic, that there is no randomness in what it is given.
In fact, pretty much everything would assume that if you look back at a point already checked, that the function value does not change. This now gets into the realms of stochastic optimization and response surface optimization.
I would note that the phrase "stochastic optimization" actually includes two classes of probem, one where the search scheme is a randomly generated one, but the objective function is deterministic (Tools like genetic algorithms, simulated annealing, etc. are typically in this class. So they are random in how they move, but they presume a deterministic objective.) The other side of stochastic optimization involves the optimization of a non-deterministic objective.
Your problem is of the latter form. So take care in what you read. You might want to concentrate on tools from response surface methodology, as they are more targeted at your problem.
This field uses statistical modeling of the objective, then using that estimated model to solve for the optimum value. You can then repeat this process, with a new experiment centered around the point chosen from the previous iteration, thus making an iterative scheme which will (hopefully) converge to the solution you wish to see. Are there any tools in MATLAB which do this process directly and do all the work for you? Not really that I know about. But you can use the stats toolbox to formulate such an iteration. That is, start with a set of points. Estimate a low order polynomial model, typically a quadratic one. Solve for the desired minimum or maximum as you need, then repeat the process, centered around that point. A problem in such an algorithm is that it could fail, IF the locally quadratic model I just mentioned was in fact hyperbolic in nature. Then there would be no minimum or maximum. In that case, you would need to start thinking about things like trust regions. The process could get complicated. (But it might actually be a fun code to write...)
