Car suspension system design optimization through bio-inspired evolutionary algorithms

The car suspension system has a major impact over the drivers’ safety, since the public road might suffer various elevation changes and damage. This work focuses on finding optimal parameters of suspensions models, which are responsible for both safety and comfort of passengers while driving. We considered 4 different suspension architectures: 2-DOF, 3-DOF, 4-DOF quarter car and 5-DOF half car. These optimal configurations have been obtained using relatively recent proposed bio-inspired algorithms such as RDA, IRDA and MOGWO. All the simulations have been performed on different road profiles and different types of cars. Additionally, we propose 6 meta-optimization schemes called super-position (SP1 – SP6), that is to combine algorithms (RDA+MOGWO) in order to enhance the quality of the obtained configurations. The results show that the proposed super-position methods of RDA+MOGWO outperform previous similar methods, such as NSGA-II+SPEA2, by an increase of 3.44 % in quality.