A many-objective optimisation decision-making process applied to automotive diesel engine calibration

Authors:
Robert J. Lygoe;Mark Cary;Peter J. Fleming
Affiliations:
Powertrain Calibration & Development, Ford Motor Company, Essex, U.K.;Powertrain Calibration & Development, Ford Motor Company, Essex, U.K.;Automatic Control & Systems Engineering, The University of Sheffield, Sheffield, U.K.
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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Abstract

A novel process has been developed for reducing complexity in real-world, high-dimensional, multi-objective optimisation problems. This approach relies on being able to identify and exploit local harmony between objectives to reduce dimensionality. To achieve this, a systematic and modular process has been designed to cluster the Pareto-optimal front and apply a rule-based Principal Component Analysis including preference articulation for potential objective reduction. This many-objective optimisation decision-making process is demonstrated on a real-world, automotive diesel engine calibration optimisation problem comprising six objectives. The complexity reduction process resulted in three- and four-objective sub-problems. In the former, a significant improvement was achieved in one of the retained objectives at very little cost to the others.