Multi-Objective Optimization of Space Camera Primary Mirror Structure Based on Dynamic Constraint SHAMODE Algorithm

Aiming at the structural lightweight design of a 700 mm aperture primary mirror for a space camera, a novel success history-based adaptive multi-objective differential evolution algorithm with dynamic constraint handling is proposed to solve the multi-objective optimization problem of simultaneously minimizing mass and compliance under strict constraints for surface error and first-order modal frequency. Firstly, a surrogate model for the mirror was constructed using the Kriging algorithm based on Optimal Latin Hypercube Sampling, establishing a mapping relationship between input design variables and output responses, thereby replacing computationally expensive finite element simulations. Subsequently, a dynamic constraint adjustment mechanism was introduced into the Success History-based Adaptive Multi-Object Differential Evolution algorithm for the surrogate model, dynamically relaxing and tightening constraint violation requirements during iteration. This allows for utilizing promising yet infeasible solutions for rapid convergence while ensuring the feasibility of the final solutions. Comparisons with 13 advanced constrained multi-objective optimization algorithms demonstrate that the proposed algorithm exhibits excellent convergence, diversity, and consistency. Finally, the optimal solution was selected from the Pareto front obtained by the proposed algorithm, and the design variable values were adjusted according to manufacturing constraints to yield the final optimization result, which was then verified by finite element simulation. The simulation results show that the final mirror structure meets all performance constraints, demonstrating the effectiveness and engineering applicability of the proposed algorithm for the structural lightweight design of space camera mirrors.