Applying the Multi-objective Optimization Techniques in the Design of Suspension Systems
Adrian Florea,Ioana Ileana Cofaru, Lucian ROMAN, Nicolae Cofaru
Journal of Digital Information Management, Vol. 14, No. 6, 2016.
DOI: 
Link: http://dline.info/fpaper/jdim/v14i6/jdimv14i6_2.pdf
Abstract:
The questionable quality of the roads represents
the main factor of discomfort, being directly responsible
for the accidents, affecting car components,
but also the security of passengerscausing death and
serious injuries. According to statistics released by the
World Health Organization, road accidents, in underdeveloped
countries, tends to increase by 80 % in 2020
compared to 2000. In terms of road infrastructure,the lowand
middle-income countries are characterized by a higher
accident rate, reason for which the cars designers must
approach the suspension problem slightly different and
the parameters obtained by optimization algorithms should
be differentfrom the same model of car depending on
where they will be driven / sold.This paper presents the
optimization of a quarter-car model with two degree-offreedom
using evolutionary algorithms to determine the
optimal parameters for a vehicle suspension, in order to
improve ride comfort. The optimization problem consists
in minimizing the sprung mass acceleration and sprung
mass displacement subject to several constraints that
arise from kinematic considerations. The vehicle model
is considered to travel at a constant speed on a random
road profile generated according to the ISO 8608 standard.
The design variables to be optimized are the suspension
stiffness and damping coefficients. We analyzed
the algorithms in multiple scenarios so we can compare
their performance in terms of fast convergence and solution
diversity. The results showed that the optimization
algorithms find solutions in small number of iterations,
with slightly better performance obtained by Fast Pareto
Genetic Algorithm.