A hybrid topology optimization algorithm for structural design

Kong Tian Zuo; Li Ping Chen; Yun Qing Zhang; Jingzhou Yang

doi:10.1080/03052150500323856

A hybrid topology optimization algorithm for structural design

Kong Tian Zuo, Li Ping Chen, Yun Qing Zhang, Jingzhou Yang

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

A hybrid algorithm for solving structural topology optimization problems is presented. This hybrid algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This hybrid algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value during the optimization. This hybrid algorithm has improved calculation efficiency and accelerated convergence when compared with the MMA or MGCMMA algorithm, which is demonstrated with three examples.

Original language	English
Pages (from-to)	849-866
Number of pages	18
Journal	Engineering Optimization
Volume	37
Issue number	8
DOIs	https://doi.org/10.1080/03052150500323856
State	Published - Dec 1 2005

Keywords

Hybrid algorithm
MGCMMA
MMA
Structural optimization
Topology optimization

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10.1080/03052150500323856

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title = "A hybrid topology optimization algorithm for structural design",

abstract = "A hybrid algorithm for solving structural topology optimization problems is presented. This hybrid algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This hybrid algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value during the optimization. This hybrid algorithm has improved calculation efficiency and accelerated convergence when compared with the MMA or MGCMMA algorithm, which is demonstrated with three examples.",

keywords = "Hybrid algorithm, MGCMMA, MMA, Structural optimization, Topology optimization",

author = "Zuo, {Kong Tian} and Chen, {Li Ping} and Zhang, {Yun Qing} and Jingzhou Yang",

note = "Funding Information: The authors thank the {\textquoteleft}National 921 project for manned space shuttles{\textquoteright} and the {\textquoteleft}National 863 project{\textquoteright} for financial support. They also gratefully acknowledge Professor K. Svanberg for valuable discussion about MMA and GCMMA algorithms and convergence.",

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T1 - A hybrid topology optimization algorithm for structural design

AU - Zuo, Kong Tian

AU - Chen, Li Ping

AU - Zhang, Yun Qing

AU - Yang, Jingzhou

N1 - Funding Information: The authors thank the ‘National 921 project for manned space shuttles’ and the ‘National 863 project’ for financial support. They also gratefully acknowledge Professor K. Svanberg for valuable discussion about MMA and GCMMA algorithms and convergence.

PY - 2005/12/1

Y1 - 2005/12/1

N2 - A hybrid algorithm for solving structural topology optimization problems is presented. This hybrid algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This hybrid algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value during the optimization. This hybrid algorithm has improved calculation efficiency and accelerated convergence when compared with the MMA or MGCMMA algorithm, which is demonstrated with three examples.

AB - A hybrid algorithm for solving structural topology optimization problems is presented. This hybrid algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This hybrid algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value during the optimization. This hybrid algorithm has improved calculation efficiency and accelerated convergence when compared with the MMA or MGCMMA algorithm, which is demonstrated with three examples.

KW - Hybrid algorithm

KW - MGCMMA

KW - MMA

KW - Structural optimization

KW - Topology optimization

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A hybrid topology optimization algorithm for structural design

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