PMID- 33983197
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210514
IS  - 1539-4522 (Electronic)
IS  - 1559-128X (Linking)
VI  - 60
IP  - 11
DP  - 2021 Apr 10
TI  - Hybrid IPSO-IAGA-BPNN algorithm-based rapid multi-objective optimization of a 
      fully parameterized spaceborne primary mirror.
PG  - 3031-3043
LID - 10.1364/AO.419227 [doi]
AB  - 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.
FAU - Qin, Tao
AU  - Qin T
FAU - Guo, JunLi
AU  - Guo J
FAU - Jing, ZiJian
AU  - Jing Z
FAU - Han, PeiXian
AU  - Han P
FAU - Qi, Bo
AU  - Qi B
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Appl Opt
JT  - Applied optics
JID - 0247660
SB  - IM
EDAT- 2021/05/14 06:00
MHDA- 2021/05/14 06:01
CRDT- 2021/05/13 12:31
PHST- 2021/05/13 12:31 [entrez]
PHST- 2021/05/14 06:00 [pubmed]
PHST- 2021/05/14 06:01 [medline]
AID - 449872 [pii]
AID - 10.1364/AO.419227 [doi]
PST - ppublish
SO  - Appl Opt. 2021 Apr 10;60(11):3031-3043. doi: 10.1364/AO.419227.