Multi-objective optimization method for surface shape error and stiffness of large aperture space mirror driven by TCL-OptiStruct-Python collaborative simulation in Isight

To address the challenge of collaborative optimization between surface shape accuracy and stiffness of large aperture space mirrors for optical remote sensors, a multiobjective optimization method driven by collaborative simulation is proposed based on the Isight platform. This method integrates TCL parametric modeling, OptiStruct structural analysis, and Python data processing. A $\mathbf{1. 4}$-meter aperture parametric mirror model is constructed, and a multi-objective optimization function is established under multiple operating conditions to minimize combined surface shape errors and self-weight rigid body displacement. The multi-island genetic algorithm is employed to automate the iterative Isight optimization process of surface shape errors and rigid body displacement. Additionally, an adaptive simulated annealing algorithm is applied to perform unload optimization of the mirror surface shape error in the vertical optical axis state, yielding an optimal set of unloading forces. This collaborative optimization method provides an efficient solution for structural parameter design and unloading configuration of optical remote sensor lenses.