Volume 19, Issue 8 (2019)                   Modares Mechanical Engineering 2019, 19(8): 1971-1978 | Back to browse issues page

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Bagheri M R, Mosayebi M, Mahdian A, Keshavarzi A. Pareto Optimization of a Three-Dimensional Full Vehicle Suspension Model Using Multi-Objective Genetic Algorithm. Modares Mechanical Engineering. 2019; 19 (8) :1971-1978
URL: http://journals.modares.ac.ir/article-15-20856-en.html
1- Department of Mechanical Engineering, Malek-Ashtar University Of Technology, Isfahan, Iran
2- Department of Mechanical Engineering, Malek-Ashtar University Of Technology, Isfahan, Iran , m.mosayebi@mut-es.ac.ir
3- Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran
Abstract:   (148 Views)
The present paper applies a multi-objective genetic algorithm for optimally design of a vehicle suspension. The vehicle model considers three-dimensional movements of vehicle body. In this full vehicle model having 8 degrees of freedom, vertical movement of passenger seat, vehicle body, and 4 tires as well as rotational movements of vehicle body create the degrees of freedom of the model. In this paper, applicable suspension parameters, consisting of passenger seat acceleration, vehicle body pitch angle, vehicle body roll angle, dynamic tire force, tire velocity, and suspension deflections are considered and optimized in optimization process. Different pairs of these parameters are selected as objective functions and optimized in multi-objective optimization processes, and Pareto solutions are obtained for pair of objective functions. In final optimization process, the Pareto solution related to the summation of dimensionless parameters in one suspension parameters group versus other group, is derived. In these Pareto solutions, there are important optimum points and designers can choose any optimum points for a particular purpose. Pareto optimization is better than other multi-objective optimization methods because there are more optimum points on Pareto front, where each point represents a level of optimization for the pairs of objective functions, and designers can choose any of the points to specific purpose.
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Received: 2018/06/8 | Accepted: 2019/01/26 | Published: 2019/08/12

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