Improving ride and handling of vehicle vibration model using Pareto robust genetic algorithms

In this paper, robust Pareto multi-objective optimum design of vehicle vibration model having parameters with probabilistic uncertainties is considered. In order to achieve optimum robust design against probabilistic uncertainties existing in reality, a multi-objective uniform-diversity genetic algorithm (MUGA) in conjunction with Monte Carlo simulation is used for Pareto optimum robust design of a vehicle vibration model. Ten conflicting objective functions have been considered are, namely, means and variances of vertical acceleration of seat, of vertical velocity of both forward and rear tires, of relative displacement between sprung mass and both forward and rear tires. An optimum design point is found from that Pareto front considering all conflicting objective functions. The robustness of the design obtained using such probabilistic approach is shown and compared with that of the design obtained using deterministic approach.