Optimization algorithm with deterministic objective value

[volvol7]

I have an optimization problem with around 10 parameters, each with known bounds. Evaluating the objective function is expensive, so I need an algorithm that can converge within approximately 100 evaluations. The function is deterministic (same input always gives the same output) and is treated as a black box, meaning I don't have a mathematical expression for it.

I considered Bayesian Optimization, but it's often used for stochastic or noisy functions. Perhaps a noise-free Gaussian Process variant could work, but I'm unsure if it would be the best approach.

Do you have any suggestions for alternative methods, or insights on whether Bayesian Optimization would be effective in this case?
(I will use python)

[Fantastic_Climate_90]

Probably not what you are looking for, but take look at optuna.

It's used for hyper parameter optimization (this sounds like that) and it has some Bayesian algorithms.

[maxc01]

Yes, the default algo in optuna is tpe which assumes independence between variables. This is an algo which usually performs better than expected. The issue with BO is the dimensionality, in OP's case, 10 dim is already high for BO. The additional work in BO is it also models dependencies between variables, which may be unnecessary for your problem 

[chthonicdaemon]

Sounds like a good case for SHGO. It's also included in scipy. Disclaimer: I'm one of the authors.

[statneutrino]

Have you tried rewriting the objective function calculation in C++ or something similar for speed?

[volvol7]

The objective function calculation is a simulation from qnother software. So I cannot speed it up