期刊
STRUCTURES
卷 33, 期 -, 页码 1439-1456出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.istruc.2021.05.035
关键词
Structural optimization; Large-scale steel trusses; Principle of virtual work; Guided stochastic search; High-dimensional optimization; Integrated force method
This study successfully applies the guided stochastic search (GSS) to more challenging high-dimensional truss optimization problems, handling large-scale truss optimization problems with thousands of design variables. The efficiency of the algorithm in dealing with high-dimensional instances of large-scale steel trusses with up to 15048 discrete design variables is demonstrated through numerical results.
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.
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