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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Comparision Of Compuational Efficiency Of MOEA\D and NSGA-II For Passive Vehicle Suspension Optimization

Authors:

Tey Jing Yuen, Rahizar Ramli

Published in:

 

(2010).ECMS 2010 Proceedings edited by A Bargiela S A Ali D Crowley E J H Kerckhoffs. European Council for Modeling and Simulation. doi:10.7148/2010 

 

ISBN: 978-0-9564944-1-2

 

24th European Conference on Modelling and Simulation,

Simulation Meets Global Challenges

Kuala Lumpur, June 1-4 2010

 

Citation format:

Yuen, T. J., & Ramli, R. (2010). Comparision Of Compuational Efficiency Of MOEA\D and NSGA-II For Passive Vehicle Suspension Optimization. ECMS 2010 Proceedings edited by A Bargiela S A Ali D Crowley E J H Kerckhoffs (pp. 219-225). European Council for Modeling and Simulation. doi:10.7148/2010-0219-0225

DOI:

http://dx.doi.org/10.7148/2010-0219-0225

Abstract:

This paper evaluates new optimization algorithms for optimizing automotive suspension systems employing stochastic methods. This method is introduced as an alternative over the conventional approach, namely trial and error, or design of experiment (DOE), to efficiently optimize the suspension system. Optimizations algorithms employed are the multi-objective evolutionary algorithms based on decomposition (MOEA\D), and non-sorting genetic algorithm II (NSGA-II). A two-degree-of-freedom (2- DOF) linear quarter vehicle model (QVM) traversing a random road profile is utilized to describe the ride dynamics. The road irregularity is assumed as a Gaussian random process and represented as a simple exponential power spectral density (PSD). The evaluated performance indices are the discomfort parameter (ACC), suspension working space (SWS) and dynamic tyre load (DTL). The optimised design variables are the suspension stiffness, Ks and damping coefficient, Cs. In this paper, both algorithms are analyzed with different sets of experiments to compare their computational efficiency. The results indicated that MOEA\D is computationally efficient in searching for Pareto solutions compared to NSGA-II, and showed reasonable improvement in ride comfort.

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