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 paper presents a method where a multi-objective genetic algorithm is combined with response surface methods in order to assess the robustness of the identified Pareto optimal solutions.
The objects of study are two different concepts of hydraulic actuation systems, which 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 tradeoff between the energy consumption and the control error for each actuation system.
With the help of response surface methods sensitivity analysis have been performed at different regions on the Pareto front. Thus it could be determined how different design parameters affect the system at different points on the Pareto front.