If you wish to optimize a probabilistic (uncertain) system, the objective function to be optimized cannot be a single deterministic output. Rather, it must be a statistic. That is, if X was an output of a probabilistic model (and hence was output as a probability distribution A mathematical representation of the relative likelihood of a variable having certain specific values. It can be expressed as a PDF (or a PMF for discrete variables), CDF or CCDF.), optimizing X itself would be meaningless. Rather, you would need to optimize a particular statistic (e.g., the mean or 50th percentile) of the output X.
In order to do this within GoldSim, you must use SubModels. In particular, you must embed a SubModel A specialized element that allows you embed one complete GoldSim model within another GoldSim model. This facilitates, among other things, probabilistic optimization, explicit separation of uncertainty from variability, and manipulation of Monte Carlo statistics. within an outer model. The SubModel would be a fully dynamic Monte Carlo simulation A method for propagating (translating) uncertainties in model inputs into uncertainties in model results., and the outer model would be a static optimization. The optimization variables for the outer model would be statistics that have been exposed on the output interface of the SubModel.
- Defining the Optimization Settings
- Finding a Global Optimum in Complex Models with Multiple Optima
- Optimizing a Probabilistic Model
- Overview of Optimization
- Running the Optimization
- Saving Optimization Settings and Results
- Setting the Optimization Precision
- Specifying the Objective Function and Constraints
- Specifying the Optimization Variables
- Understanding Optimization Warning Messages