Evolutionary Algorithms for Passive Suspension Systems

Abstract

This paper presents the evolutionary algorithms (EAs) to determine a set of optimum system parameters for the passive suspension system (PSS) so that the best performance of the system can be achieved. The selected fitness functions are chosen according to the analysis of desired performance including sprung mass acceleration (SMA), suspension deflection (SD) and tire deflection (TD) in the frequency domain. Besides, we compare the evolution processes and search results of genetic algorithms (GAs) and evolutionary programming (EP), and find that both methods are effective. All the simulation results illustrate that the EAs can provide better ride comfort and road holding ability in comparison with the commonly used PSS.