Optimum design of run-flat tire insert rubber by genetic algorithm

A generalized multi-objective optimization method making use of genetic algorithm (GA) is introduced, in order to simultaneously improve the riding comfort and the durability of run-flat tire by optimally tailoring the shape and stiffness of the sidewall insert rubber. The sensitivity analysis invoking the CPU time-consuming finite element analyses is replaced with the genetic evolution and the fitness of each genome in the population is evaluated by utilizing the response surfaces of objective functions approximated by ANN. It is confirmed through the numerical experiment that a number of Pareto solutions of the shape and stiffness of the sidewall insert rubber for different combinations of weighting factors can be successfully obtained. As well, the reliability of the Pareto solutions has been justified from the comparison with the direct finite element analysis.

► A generalized multi-objective optimization method using GA and ANN is introduced. ► Run-flat tire insert rubber is optimally designed by the MOGA optimization method. ► This method can be applied to the discrete-type design optimization problems.