Interactive evolutionary multiobjective optimization for hydraulic valve controller parameters

This paper proposes a novel approach of interactive evolutionary multiobjective optimization. The approach combines an evolutionary algorithm with an instance based supervised online learning scheme for user preferences. Interactive preference articulation constitutes an alternative to a priori as well as a posteriori decision making in multiobjective optimization. The proposed scheme is applied to the interactive user guided parameter optimization of hydraulic valve controllers. The tight integration of expert knowledge into the design and optimization process results in an efficient search of feasible solutions. The proposed interactive scheme extends the scope and applicability of computational intelligence aided design to mechatronic and engineering problems.