Knowledge Discovery for Pareto Based Multiobjective Optimization in Simulation

We present a novel knowledge discovery approach for automatic feasible design space approximation and parameter optimization in arbitrary multiobjective blackbox simulations. Our approach does not need any supervision of simulation experts. Usually simulation experts conduct simulation experiments for a predetermined system specification by manually reducing the complexity and number of simulation runs by varying input parameters through educated assumptions and according to prior defined goals. This leads to a error-prone trial-and-error approach for determining suitable parameters for successful simulations.In contrast, our approach autonomously discovers unknown relationships in model behavior and approximates the feasible design space. Furthermore, we show how Pareto gradient information can be obtained from this design space approximation for state-of-the-art optimization algorithms. Our approach gains its efficiency from a novel spline-based sampling of the parameter space in combination within novel forest-based simulation dataflow analysis. We have applied our new method to several artificial and real-world scenarios and the results show that our approach is able to discover relationships between parameters and simulation goals. Additionally, the computed multiobjective solutions are close to the Pareto front.