I need to find an optimal set of "n" parameter values that minimize an objective function (a 2-hr simulation of a system). I have looked at genetic algorithm and simulated annealing methods, but was wondering if there are any better algorithms and guidance on their merits and limitations.

With the above optimization methods I can find the optimal parameter values that hold true for the entire simulation duration. Incase, I want to find the optimal "time varying" parameter values (parameter values change with time during the 2-hr simulation), are there any methods/ideas other than making each time varying parameter value a variable to optimize? Any thoughts?

1 Answer 1


Some thoughts:

  • There is something called local regression ("loess" for short, pronounced Low-Ess). Whether that's helpful is for you to decide.

  • I work on products for doing non-linear mixed effects modeling (NLME) in the context of pharmacometrics. So, for example, each subject can have a parameter called V, for volume of distribution (basically blood plasma volume) that affects the observed concentration after a dose of drug. In some models, V may change over time. If so, we try to find a time-varying input datum called a "covariate", such as body weight W. Then we might make a model where V = V0 + k W, where V0 is some kind of base volume, and k determines how much weight affects volume. So we're estimating non-time-varying parameters V0 and k, even though V is time-varying.

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.