I have a reference value $R$ and a modelled value $M$. $M$ is generated using a stochastic algorithm with parameters $a$ and $b$.
The objective is to tune $a$ and $b$ so that $M$ is as close as $R$ possible (heuristically). But each run of $M$ takes around 30 seconds. Any "good" algorithm will be jammed by this computation, seemingly. Even so, what is better (than others) algorithm of choice?
If we are to allow $\vert M-R\vert$ converges quickly initially (and accept it to slowly converge in later stages), what types of algorithm should I look for? My colleague suggests Particle Swarm Optimisation (PSO). Is that a good choice?
Note: We need "good" fit but not "best" fit. Our tolerance is fairly large.