Genetic algorithm-based optimum vehicle suspension design using minimum dynamic pavement load as a design criterion

In this paper, the design of a passive vehicle suspension system was handled in the framework of nonlinear optimization. The variance of the dynamic load resulting from the vibrating vehicle operating at a constant speed was used as the performance measure of a suspension system. Using a quarter-car model, the performance measure was derived as an integration of a complex function of several variables. A genetic algorithm is applied to solve the nonlinear optimization problem. It was found from the sensitivity analysis that appropriate mutation rate, crossover rate and population size are 1.0%, 25% and 100, respectively. The optimum design parameters of the suspension systems obtained are k s = 622,180 N / m , k t = 1,705,449 N / m and c s = 26,582 N s / m , respectively.