Sensitivity analysis in multi-objective evolutionary design

In real world engineering design problems we have to search for solutions that simultaneously optimize a wide range of different criteria. Furthermore, the optimal solutions also have to be robust. Therefore, this chapter describes a method where a multi-objective genetic algorithm is combined with response surface methods in order to assess the robustness of a set of identified optimal solutions. The multi-objective genetic algorithm is used in order to optimize two different concepts of hydraulic actuation systems. The different concepts have been modeled in a simulation environment to which the optimization strategy has been coupled. The outcome from the optimization is a set of Pareto optimal solutions that elucidate the trade-off between the energy consumption and the control error for each actuation system. Based on these Pareto fronts, promising regions could be identified for each concept. In these regions sensitivity analyses are performed with the help of response surface methods. It can then be determined how different design parameters affect the system for different optimal solutions.