Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm

This study develops an enhanced coding strategy with adaptive parameter adjustment mechanisms to address the premature convergence issue inherent in conventional genetic algorithms (GAs). An improved adaptive genetic algorithm (IAGA) is proposed for optimizing the slit pattern configurations of 16 steel-frame-slotted steel plate shear wall (SSPSW) systems. The methodology incorporates a real-time probability modulation of the crossover and mutation operations based on population diversity metrics. ABAQUS finite element software and PYTHON interactive analysis were systematically used to evaluate the mechanical performance of the optimized configurations, focusing on achieving an optimal ductility–stiffness balance under cyclic loading conditions. The numerical results demonstrate that the IAGA achieves faster convergence than standard GAs. A higher aspect ratio of the inter-slot column (l/b) or a smaller aspect ratio of the slot (b/t) leads to better ductility and lower stiffness. It is recommended that the configuration with connections on two sides of an SSPSW frame be adopted.