Canard is one of the aerodynamic add-on devices which can reduce drag coefficient of the car. In this paper, different parameters of the canard geometry are determined using a multi-objective optimisation. Design variables are entrance velocity (U), geometrical parameters of canard (L1, L2, r, α) and canard angle from horizontal axis (θ). The objective functions include magnitude of drag and lift coefficients that should be minimised and maximised, respectively. First, the neural network is trained by means of a series of ANSYS Fluent-based CFD calculations. A GMDH-type neural network is then applied to derive polynomials that compute the objective functions from input variables. Finally, Pareto optimal points for objective functions are obtained through using these polynomials and NSGA-II multi-objective optimisation. According to the results, the canard’s optimum state is specified as L1 = 0.37 m, L2 = 0.18 m, r = 0.09 m, α = 25°, θ = 20° with potential drag reduction of 4.5%.
