Hybrid IPSO-IAGA-BPNN algorithm-based rapid multi-objective optimization of a fully parameterized spaceborne primary mirror

The surface figure precision, weight, and dynamic performance of spaceborne primary mirrors depend on mirror structure parameters, which are usually optimized to improve the overall performance. To realize rapid multi-objective design optimization of a primary mirror with multiple apertures, a fully parameterized primary mirror structure is established. A surrogate model based on a hybrid of improved particle swarm optimization (IPSO), adaptive genetic algorithm (IAGA), and optimized back propagation neural network (IPSO-IAGA-BPNN) is developed to replace optomechanical simulation with its high computational cost. In this model, a self-adaptive inertia weight and a modified genetic operator are introduced into the particle swarm optimization (PSO) and adaptive genetic algorithm (AGA), respectively. The connection parameters of BPNN are optimized by the IPSO-IAGA algorithm for global searching capability. Further, the proposed IPSO-IAGA-BPNN, based on a rapid multi-objective optimization framework for a fully parameterized primary mirror structure, is established. Moreover, in addition to the proposed IPSO-IAGA-BPNN model, the Kriging, RSM, BPNN, GA-BPNN, PSO-BPNN, and PSO-GA-BPNN models are also analyzed as contrast models. The comparison results indicate that the predicted value obtained by IPSO-IAGA-BPNN is superior to the six other surrogate models since its mean absolute percentage error is less than 3% and its R 2 is more than 0.99. Finally, we present a Pareto-optimal primary mirror design and implement it through three optimization methods. The verification results show that the proposed method predicts mirror structural performance more accurately than existing surrogate-based methods, and promotes significantly superior computational efficiency compared to the conventional integration-based method.