Browse Publications Technical Papers 2004-01-3520

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.


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