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A Study of the Effectiveness of Parametric Optimization Algorithms: Collision Impact Processes in Bumper Beams and Truck Cabins

This article compares various parametric optimization algorithms implemented in LS-OPT for solving high-speed and highly nonlinear impact problems using a bumper beam and a truck cabin as examples. The analysis of the results showed which algorithms were the most effective according to the criteria of accuracy and time. The metamodel based on the RBF neuron network and the space-filling point selection for optimizing the design of the bumper beam allowed reducing the mass of the structure by 16 % while maintaining the same parameters of rigidity and power consumption. To solve optimization problems concerning passive safety of complex structures such as truck cabins, the RBF neuron network model proved to be the most effective. It is therefore recommended as the primary model, while the linear polynomial model should be used only for preliminary analysis.