Mixed-integer second-order cone programming for global optimization of compliance of frame structure with discrete design variables

Frame structures are extensively used in mechanical, civil, and aerospace engineering. Besides generating reasonable designs of frame structures themselves, frame topology optimization may serve as a tool providing us with conceptual designs of diverse engineering structures. Due to its nonconvexity...

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Bibliographic Details
Published in:Structural and multidisciplinary optimization Vol. 54; no. 2; pp. 301 - 316
Main Author: Kanno, Yoshihiro
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2016
Springer Nature B.V
Subjects:
Aerospace engineering
Algorithms
Computational Mathematics and Numerical Analysis
Continuity (mathematics)
Design optimization
Engineering
Engineering Design
Frame structures
Global optimization
Heuristic methods
Integers
Local optimization
Nonlinear programming
Research Paper
Theoretical and Applied Mechanics
Topology optimization
Discrete optimization
Global optimization
Frame topology optimization
Second-order cone programming
Mixed-integer programming
ISSN:1615-147X, 1615-1488
Online Access:Get full text
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Summary:Frame structures are extensively used in mechanical, civil, and aerospace engineering. Besides generating reasonable designs of frame structures themselves, frame topology optimization may serve as a tool providing us with conceptual designs of diverse engineering structures. Due to its nonconvexity, however, most of existing approaches to frame topology optimization are local optimization methods based on nonlinear programming with continuous design variables or (meta)heuristics allowing some discrete design variables. Presented in this paper is a new global optimization approach to the frame topology optimization with discrete design variables. It is shown that the compliance minimization problem with predetermined candidate cross-sections can be formulated as a mixed-integer second-order cone programming problem. The global optimal solution is then computed with an existing solver based on a branch-and-cut algorithm. Numerical experiments are performed to examine computational efficiency of the proposed approach.
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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-016-1406-5