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A fuzzy-optima definition based multiobjective optimization of a racing car tyre-suspension system

When dealing with multiobjective optimization of the tyre-suspension system of a racing car, a large number of objectives have to be taken into account. Two different models have been used, both validated on data coming from an instrumented car, a differential equation based physical model and a neural network purely numerical method. Up to 23 objective functions have been defined, at least 14 of which showing to be in strict clash each other. The equivalent scalar function based formulation is intentionally avoided due to its well known limitations. A fuzzy definition of optima, being a generalization of Pareto-optimality, is applied to the problem. The result of such an approach is that subsets of Pareto-optimal solutions (being on such a problem a big portion of the entire search space) can be properly selected as a consequence of inputs from the designer. The obtained optimal solutions are compared with the reference vehicle and with the optima previously obtained with design of experiments techniques and different MO optimization strategies.