A new hybrid method for size and topology optimization of truss structures using modified ALGA and QPGA
| Autor: | Iman Hajirasouliha, Nima Noii, Mehmet Metin Kunt, Iman Aghayan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Strategy and Management augmented Lagrangian Truss 02 engineering and technology finite element analysis 01 natural sciences Nonlinear programming 0202 electrical engineering electronic engineering information engineering Applied mathematics structural optimization 0101 mathematics Moore–Penrose pseudoinverse Civil and Structural Engineering Mathematics Stiffness matrix Building construction Augmented Lagrangian method Topology optimization hybrid genetics algorithm 010101 applied mathematics Truss bridge quadratic penalty function 020201 artificial intelligence & image processing TH1-9745 Cholesky decomposition |
| Zdroj: | Journal of Civil Engineering and Management; Vol 23 No 2 (2017); 252-262 Journal of Civil Engineering and Management, Vol 23, Iss 2 (2017) |
| ISSN: | 1392-3730 1822-3605 |
| Popis: | Modified Augmented Lagrangian Genetic Algorithm (ALGA) and Quadratic Penalty Function Genetic Algorithm (QPGA) optimization methods are proposed to obtain truss structures with minimum structural weight using both continuous and discrete design variables. To achieve robust solutions, Compressed Sparse Row (CSR) with reordering of Cholesky factorization and Moore Penrose Pseudoinverse are used in case of non-singular and singular stiffness matrix, respectively. The efficiency of the proposed nonlinear optimization methods is demonstrated on several practical examples. The results obtained from the Pratt truss bridge show that the optimum design solution using discrete parameters is 21% lighter than the traditional design with uniform cross sections. Similarly, the results obtained from the 57-bar planar tower truss indicate that the proposed design method using continuous and discrete design parameters can be up to 29% and 9% lighter than traditional design solutions, respectively. Through sensitivity analysis, it is shown that the proposed methodology is robust and leads to significant improvements in convergence rates, which should prove useful in large-scale applications. First published online:12 Feb 2016 |
| Databáze: | OpenAIRE |
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