Reliability-based structural optimization using adaptive neural network multisphere importance sampling

An innovative adaptive neural network multisphere importance sampling (ANNM-IS) is proposed and integrated with symbiotic organism search (SOS) to form a framework for finding an engineering optimal design. Building a single sphere in IS to enhance the computational efficiency has been used for deca...

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Bibliographic Details
Published in:Structural and multidisciplinary optimization 2023-05, Vol.66 (5), p.119, Article 119
Main Authors: Thedy, John, Liao, Kuo-Wei
Format: Article
Language:English
Subjects:
Adaptive sampling
Computational Mathematics and Numerical Analysis
Control systems design
Engineering
Engineering Design
Importance sampling
Iterative methods
Neural networks
Optimization
Research Paper
Sampling methods
Structural reliability
Theoretical and Applied Mechanics
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Summary:An innovative adaptive neural network multisphere importance sampling (ANNM-IS) is proposed and integrated with symbiotic organism search (SOS) to form a framework for finding an engineering optimal design. Building a single sphere in IS to enhance the computational efficiency has been used for decades, ANNM-IS provides a pioneering idea, in which multi-spheres are built. “Adaptive point”, found by neural network (NN), is proposed to help for generating multiple spheres. ANNM-IS is further integrated with SOS to update NN for next iteration. As optimization iterations increase, adaptive NN provides more accurate reliability estimates. A two-step SOS, considering exploration and exploitation, is designed to enhance the search performance. Four reliability problem are first solved to confirm the correctness and effectiveness of ANNM-IS, then another four structural optimization problem including a building controller design and a 25-bar truss design are solved. Results shown that the proposed method drastically reduces the amount of function evaluation and computation time without sacrificing accuracy in reliability compared to those of other sampling methods. The developed framework can solve a complex structural optimization problem of accurate reliability with affordable price. The supporting source codes are available for download at https://github.com/johnthedy/RBDO-using-MIS-NN-SOS .
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-023-03571-3