Main Beam Optimization of Overhead Crane based on AL-Kriging Surrogate Model and PSO Algorithm

In order to solve the expensive calculation cost of crane structure optimization combining finite element model with swarm intelligence algorithm, a crane girder optimization method is constructed in this paper based on active learning Kriging surrogate model and particle swarm intelligence optimization algorithm. In this method, the effective sample points required for optimization is selected by the EFF active learning function, so as to construct a surrogate model that meets the accuracy requirements with as fewer high-fidelity samples as possible. Finally, the structural optimization is completed based on the constructed surrogate model through the particle swarm optimization algorithm. Through the engineering case, under the condition of obtaining the same optimization results, the optimization cost time could be saved by about 70% compared with the static Kriging surrogate model, the number of call samples is only 27% of the static surrogate model, which verifies the feasibility and effectiveness of the established optimization method.