Multi-objective design optimization on crashworthiness of full vehicle based on adaptive radial basis function

An ARBF model method was suggested and combined with micro multi-objective genetic algorithm (μMOGA) to solve vehicle crashworthiness. In each iterative, sampling points were obtained by the optimal Latin hypercube design, while testing points were obtained by the inherit optimal Latin hypercube design, this method regarded the errors of testing points as fitness of intergeneration projection genetic algorithm (IP-GA), assessed the model systematically and got the optimal smooth parameters to maximize model accuracy, testing points added to sample space until reaching errors allowable of each crashworthiness objective. Then greed algorithm was adopted to filter the testing points from the last iterative to sampling space to increase accuracy. At last, μMOGA was applied to optimize the ARBF, and got Pareto and balanced each objective to get different best compromise solutions according to engineer experiments or engineering requirements.