Multi-Objective Optimization of Vehicle Passive Suspension System Using NSGA-II, SPEA2 and PESA-II

This paper presents a constrained multi-objective optimization study of vehicle passive suspension system which is modeled as a passive half car ride model. For multi-objective optimization the most widely used multi-objective evolutionary algorithms such as NSGA-II, SPEA2 and PESA-II are employed. The potential of the MOEAs in obtaining the better Pareto front of optimal solutions and in maintaining the diversity among the optimal solutions is tested by conducting 2 and 3-objective optimization studies. The results show that NSGA-II is able to yield a better Pareto front in terms of minimizing the objective vector but SPEA2 and PESA-II has a better diversified set of optimal solutions. Overall, all three algorithms have performed equally in optimizing the problem with the nature of the equations is second order ordinary differential equations.