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

Abstract

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.

Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

“…In addition, the comparison of the results and time spent between BR-RNN-NSGA-II and the Monte Carlo method is shown in Table 7, which shows that BR-RNN-NSGA-II outperforms the Monte Carlo method in terms of GMDRMS, FMF, mass and time spent. Compared with [13,15], BR-RNN-NSGA-II offers higher prediction accuracy and better search efficiency.…”

“…Wang et al used the response surface method to optimize the structure of the 2 m space reflector and reduced the mirror deformation error [14]. Qin et al developed a surrogate model with high prediction accuracy by fusing particle swarm algorithm, genetic algorithm, and backpropagation neural network for multi-aperture primary mirror [15]. Wang et al used the neural network method to establish the optimization objective prediction model of the primary mirror to optimize the mirror deformation, stiffness, and mass of the primary mirror [16].…”

Optimization Design of Large-Aperture Primary Mirror for a Space Remote Camera

“…Considering the processing and manufacturing capability of the SiC reflector, the minimum thickness of the tendons is 2 mm. The optimizing formulas of the primary mirror assembly can be defined, and the optimization formula for the main mirror component is defined, and the target functions and restraints are given in Equation (10). The structure of lightweight ribs and the concave groove size of flexible hinges were selected as the initial structure of the optimization design.…”

“…Considering the processing and manufacturing capability of the SiC reflector, the minimum thickness of the tendons is 2 mm. The optimizing formulas of the primary mirror assembly can be defined, and the optimization formula for the main mirror component is defined, and the target functions and restraints are given in Equation (10). (10) Parametric optimization design is a design process by modifying the initial thickness and height of lightweight bars and calculating the engineering results by the computer, which realizes the automation of the design process.…”

“…Fengchang Liu et al studied the primary mirror structure with 6-point back support and triangular lightweight form and optimized the design of the primary mirror structure parameters using a compromise programming method and topology optimization algorithm, which improved the overall performance of the primary mirror and reduced the weight, RMSx, and RMSz by 8.5%, 14.3%, and 10.5% in turn [ 9 ]. Qin Tao et al used a hybrid IPSO-IAGA-BPNN algorithm to optimize the structural parameters of the reflector, and the advantages and disadvantages of different optimization methods are also analyzed [ 10 ]. Zongxuan Li et al studied the silicon carbide primary mirror of a space telescope with the semi-enclosed triangular lightweight form at the back and three-point support method at the back, optimized the design and fabricated the primary mirror with a diameter Φ760 mm and flexible hinge, and the surface shape accuracy reached 0.02 λ [ 11 ].…”