High-dimensional optimization of large-scale steel truss structures using guided stochastic search
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Date
2021
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Elsevier Science inc
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Abstract
Despite a plethora of truss optimization algorithms devised in the recent literature of structural optimization, still high-dimensional large-scale truss optimization problems have not been properly tackled basically due to the excessive computational effort required to handle the foregoing instances. In this study, application of a recently developed design-driven heuristic, namely guided stochastic search (GSS), is extended to a more challenging class of truss optimization problems having thousands of design variables. Two variants of the algorithm, namely GSSA and GSSB, have been employed for sizing optimization of four high-dimensional examples of steel trusses, i.e., a 2075-member single-layer onion dome, a 2688-member double-layer open dome, a 6000-member doublelayer scallop dome, and a 15048-member double-layer grid as per AISC-LRFD specification. The numerical results obtained indicate the efficiency of GSSA and GSSB in handling high-dimensional instances of large-scale steel trusses with up to 15048 discrete design variables.
Description
Aminbakhsh, Saman/0000-0002-4389-1910; Kazemzadeh Azad, Saeid/0000-0001-9309-607X
Keywords
Structural optimization, Large-scale steel trusses, Principle of virtual work, Guided stochastic search, High-dimensional optimization, Integrated force method
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Citation
18
WoS Q
Q2
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Source
Volume
33
Issue
Start Page
1439
End Page
1456