Use of Genetic Algorithms with Multiple Metrics Aimed at the Optimization of Automotive Suspension Systems

Scott A. Mitchell, Stephen Smith, Alberto Damiano, Joel Durgavich, Rosalyn MacCracken
2004 SAE Technical Paper Series   unpublished
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
more » ... endent 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.
doi:10.4271/2004-01-3520 fatcat:2sncedauvvehrgusmea7rrycp4