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Use of Genetic Algorithms with Multiple Metrics Aimed at the Optimization of Automotive Suspension Systems
ISSN: 0148-7191, e-ISSN: 2688-3627
Published November 30, 2004 by SAE International in United States
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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.
- Scott A. Mitchell - School of Computational Sciences, George Mason University
- Stephen Smith - School of Computational Sciences, George Mason University
- Alberto Damiano - School of Computational Sciences, George Mason University
- Joel Durgavich - School of Computational Sciences, George Mason University
- Rosalyn MacCracken - School of Computational Sciences, George Mason University
CitationMitchell, 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.
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