R2W is based on a random population of possible solutions, which is generated in a restricted region of the search space.
The R2W population evolves by using Equations (9) to (11).
Figures 1 and 2 show simplified flowcharts for PSO and R2W.
In R2W, the restriction factor ([delta]) and the number of elements of the population (Pop) are empirical constants.
Thus, the proposed inverse problem approach, employing PSO and R2W, with noiseless pseudo-experimental data, has estimated correctly the suspension parameters.
However, the relative errors of the estimated parameters were less than the noise level, indicating that the proposed inverse problem approach was very efficient by using R2W or PSO.
Comparing the results shown in Tables 5 and 6, it is verified that R2W spent more computational time than PSO for the cases with noise level equal to 0, 1 and 5%.
The evolutions of the objective function by using PSO and R2W were presented in Figures 5 and 6 for the cases reported in Tables 5 and 6.
In the present work, a field experiment to estimate the damping coefficient and the suspension stiffness of a tracked vehicle with ten road wheels was proposed based on an inverse problem approach by using stochastic optimization methods: PSO and R2W.