Approaches to Nonlinear Programming Problems: Taylor Series Expansion, RBF Surrogate Modeling, DOE-Based Dimensionality Reduction, and Adaptive Domain Splitting
This paper presents a comprehensive four-component optimization framework that combines DOE-based variable screening, RBF surrogate modeling, adaptive domain splitting, and a hybrid GA–SQP solver to significantly reduce computational effort and convergence time while maintaining accuracy across diverse nonlinear programming problems.