Optimization Algorithms for Tuning Suspension Systems Used in Ground Vehicles

According to bibliography many researchers have contributed in the area of optimization of the suspension characteristics used in ground vehicles. In a number of papers different versions of Evolution Strategies are implemented for achieving this goal. A disadvantage of such methods is the large number of iterations for the convergence of the algorithm. We propose the acceleration of evolution strategy with means of deterministic algorithms, such as the steepest descent method, and we prove that such combination yields significantly faster and more reliable convergence. The method combines the advantages of two categories of optimization algorithms, deterministic and stochastic. The half-car model of suspension systems, described with non-linear differential equations and subject to various road profiles, constitutes a suitable basis for the implementation of numerical optimization algorithms. The common objective of the presented optimization procedures is the improvement of the passengers' ride comfort, leading to minimization of the maximum acceleration of the sprung mass, with respect to the geometrical constraints of the suspension as well as the necessary traction of the vehicle. The comparison between the performances of the implemented methodologies states clearly the advantages of the proposed algorithm when the problem is of higher complexity, meaning that the number of design variables is increased and also many several local minima of the objective function are introduced. In addition, plans and topics for further research are reported.