Uncertainty of constraint function in Evolutionary Multi-objective Optimization

Engine calibration, the tuning process of controller parameters in automotive engine development, can be formulated as a multi-objective optimization problem (MOP) because it has various competing objectives. Experiment-based evolutionary multi-objective optimization is a promising approach for automatic engine calibration. In engine calibration, severe restrictions such as legislation of exhaust emissions appear as constraints on MOPs. Since the emission quantities observed by the instruments via experiments are used as the constraints, observation noise has to be considered. In this paper, we define this problem as dasiaNoisy constrained MOPspsila and investigate the difficulties for evolutionary multi-objective optimization (EMO). To overcome the difficulties, we introduce a constraint estimation approach. Moreover, a Pre-selection algorithm, an acceleration method for EMO, is employed to reduce the number of evaluations for expensive evaluation cost problems. The effectiveness of the proposed methods is demonstrated through numerical experiments.