Use of Genetic Algorithms with Multiple Metrics Aimed at the Optimization of Automotive Suspension Systems 2004-01-3520
Suspension models are highly multivariate and require a nonlinear system to model the movements and interaction of the parameters within the suspension system. Multiple metrics must be considered to determine an optimal result.
This paper describes a system for the use of a Genetic Algorithm for the optimization of automotive suspension geometries, a description of the suspension model, and the scoring mechanism. The results of this model evaluate the impact of multiple independent metrics. A combined objective function score is determined with the assistance of a user selectable weighting of metrics. The optimization algorithm is also compared to a discrete grid search.
Citation: Mitchell, S., Smith, S., Damiano, A., Durgavich, J. et al., "Use of Genetic Algorithms with Multiple Metrics Aimed at the Optimization of Automotive Suspension Systems," SAE Technical Paper 2004-01-3520, 2004, https://doi.org/10.4271/2004-01-3520. Download Citation
Scott A. Mitchell, Stephen Smith, Alberto Damiano, Joel Durgavich, Rosalyn MacCracken
School of Computational Sciences, George Mason University