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Use of Genetic Algorithms with Multiple Metrics Aimed at the Optimization of Automotive Suspension Systems
Technical Paper
2004-01-3520
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
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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Authors
- 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
Citation
Mitchell, 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.Also In
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