Simulation-based Numerical Optimization
In science and engineering, simulation is a powerful tool that predicts the behavior of physical systems. Moreover, progress made with algorithms and computational hardware yielded further improvements in simulation. Today's simulation tools have become more practical in dealing with complex design problems, for which you want to determine the large system parameters that maximize a specific objective.
Such design problems are in fact optimization problems. Instead of optimizing mathematical expressions, the optimization treats the simulation model as a black box to the optimizer. The simulation-based optimization process incorporated into Optimus, a numerical optimization software solution, adjusts the input variables of the simulation model to identify the levels that achieve the best possible outcome.
Usually, simulation-based optimization sets specific challenges because of their large size, inexact derivatives when available, and expensive computing time. Many conflicting objectives must be optimized simultaneously, which makes it even more challenging.
Categories of Numerical Optimization
Based on the number of objective functions, Optimus numerical optimization can be classified into subfields:
And based on the search methods, Optimus categorizes optimization methods into: