Prediction of Welding Margin for Marine Steel Plates Based on AGSCOA-Stacking Feature Weighting

To enhance the accuracy of steel plate welding and improve the quality and construction efficiency of ship hulls, this study proposes an Adaptive Golden Sine Crayfish Optimization Algorithm (AGSCOA)-stacking feature-weighted agent modeling approach to solve the problem of welding margin prediction for marine steel plates. First, based on the stacking ensemble learning strategy, a base learner with high predictive accuracy and differentiation is selected from multiple machine learning models according to the proposed PC metrics. Second, a feature weighting method is proposed to improve the generalizability of the model by performing adaptive feature weighting for the prediction performance of the selected base learners. Finally, the traditional crayfish optimization algorithm is improved in various aspects: an orthogonal refractive inverse learning mechanism is proposed to improve population initialization to ensure initial population quality, an adaptive Lévy flight strategy is proposed to optimize the exploration phase to avoid being trapped in local optima, and a golden sine algorithm is proposed to improve the development phase to balance the global search with the local development capability. The improved AGSCOA is used to optimize the agent model with multiple parameters to enhance the model prediction accuracy. Experimental results show that AGSCOA demonstrates excellent performance in terms of optimization and convergence speed. The proposed surrogate model has higher prediction accuracy compared to the linear weighted ensemble learning surrogate model, AGSCOA-SVR, AGSCOA-ET, and AGSCOA-RF, with the Root Mean Square Error (RMSE) reduced by 14.29%, 35.78%, 17.48%, and 22.31% respectively.