Artificial Intelligence Review - Special issue on lazy learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Computation in Dynamic and Uncertain Environments (Studies in Computational Intelligence)
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
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 'Noisy constrained MOPs' 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.