Thin-walled U-shaped boom segments of cranes exhibit complex sectional deformations, posing challenges for accurate dynamic and stability analysis. This study proposes an integrated framework combining data-driven modeling, stability assessment, and optimization. Sixteen physically meaningful deformation modes are extracted via principal component analysis from free-vibration data, forming the basis of an efficient higher-order beam model. Building on this model, a buckling analysis incorporating geometric nonlinearity is performed. An improved adaptive particle swarm optimization algorithm is then employed to minimize structural mass, constrained by natural frequencies and critical buckling loads. Numerical results demonstrate a 5% mass reduction while meeting all performance requirements, providing an effective tool for the refined lightweight design of crane booms.
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