Pareto optimization of a nonlinear vehicle model using multi-objective differential evolution algorithm with fuzzy inference-based adaptive mutation factor (MODE-FM)

In this study, a multi-objective differential evolution with fuzzy inference-based dynamic adaptable mutation factor with hybrid usage of non-dominated sorting and crowding distance (MODE-FM) is utilized for Pareto optimization of a ۵-degree of freedom nonlinear vehicle vibration model considering the five conflicting functions simultaneously, under different road inputs. The significant conflicting objective functions that have been observed here are, namely, vertical seat acceleration, vertical forward tire velocity, vertical rear tire velocity, relative displacement between sprung mass and forward tire and relative displacement between sprung mass and rear tire. Different road inputs are, namely, double-bump, stationary random road and non-stationary random road. It is exhibited that the optimum solutions of ۵-objective optimization contain those of ۲-objective optimization and, as a result, this important matter creates more options for optimal design of nonlinear vehicle vibration model.