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

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.