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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    Titel :

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


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    Motorsports Engineering Conference & Exposition ; 2004



    Erscheinungsdatum :

    2004-11-30




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





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