The welding sequence has a significant influence on the magnitude and distribution of the final welding distortion, which often contributes negatively to the dimensional accuracy, structural instability, fabrication cost, and in-service performance of the welded assembly. Hence, it is of great significance to automatically plan optimal welding robot paths for minimum residual deformations in real-world engineering. In this study, an integrated optimisationOptimisation approach combining an artificial neural networkArtificial neural network (ANN) model and intelligent optimisation algorithmIntelligent optimisation algorithm was proposed to optimise welding sequences for minimising residual distortions in the assembly of thin-walled aluminium tubes. During the optimisationOptimisation process, an ANN model was successfully established and implanted into this optimisationOptimisation method to rapidly predict welding distortion within good computational precision. Subsequently, the Bees AlgorithmBees algorithm (BA) was developed to systematically search for the optimal welding sequence. This algorithmAlgorithms takes advantage of performing a multi-neighbourhood searchNeighbourhood search combined with a randomised global search to improve computational efficiency and solve premature convergence problems. Finally, a series of experiments were conducted to confirm the accuracy of the proposed BA. Verification experiments demonstrate that the acquired optimum welding sequence was in good agreement with the experimental results.