摘要：

为了提升钢板焊接的精度, 提高船体质量和建造效率, 提出一种自适应黄金正弦螯虾优化算法(AGSCOA)-Stacking特征加权代理模型的方法, 用于解决船用钢板焊接余量预测问题。首先, 基于Stacking集成学习策略, 根据所提出的PC指标, 从多种机器学习模型中筛选出兼具高预测精度和差异性的基学习器。其次, 提出一种特征加权方法, 针对所筛选基学习器的预测性能进行自适应特征加权, 从而提高模型的泛化能力。最后, 对传统螯虾优化算法进行多方面改进, 引入正交折射反向学习机制来改进种群初始化, 确保初始种群质量; 提出自适应Lévy飞行策略来优化探索阶段, 避免陷入局部最优; 引入黄金正弦算法改进开发阶段, 平衡全局搜索与局部开发能力。利用改进后的AGSCOA对代理模型进行多参数优化, 从而提升模型预测精度。实验结果表明, AGSCOA在优化性能和收敛速度上表现出色, 所提出的代理模型相比线性加权集成学习代理模型、AGSCOA-SVR、AGSCOA-ET和AGSCOA-RF具有更高的预测精度, 均方根误差(RMSE)分别降低了14.29%、35.78%、17.48%和22.31%。

关键词: 焊接余量预测, Stacking集成学习, 代理模型, 螯虾优化算法, 折射反向学习机制, 黄金正弦算法

Abstract:

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.

Key words: welding margin prediction, Stacking ensemble learning, agent model, Crayfish Optimization Algorithm (COA), refractive opposition learning mechanism, golden sine algorithm