BlueM.Opt is an optimization framework incorporating sensitivity analyses and various optimization algorithms for different simulation models, which can be freely combined.
Included optimization algorithms are:
- SensiPlot – Sensitivity analysis
- Parametric Evolution Strategy (PES) – Multi-objective evolutionary strategy
- Hooke & Jeeves – Hill-climbing algorithm for single-objective problems
- MetaEVO –Uses both global (evolutionary strategy) and local Hooke & Jeeves optimization algorithms sequentially
- Dynamically Dimensioned Search (DDS) – N-dimensional continuous global optimization algorithm
Interfaces to the following simulation models have been implemented: