Optimal design of planar and space structures with genetic algorithms

By F. Erbatur, O. Hasançebi, I. Tütüncü, H. Kilic. 2000

320 citations recorded by the source corpus.

Abstract One of the most important practical considerations in the optimization of steel structures is that structural members are generally to be selected from available steel profiles. Furthermore, the solutions produced according to the national specifications are undoubtedly valuable from the point of view of applicability in real-life practice. This paper reports the development of a computer-based systematic approach for discrete optimal design of planar and space structures composed of one-dimensional elements. The main characteristic of the solution methodology is the use of a genetic algorithm (GA) as the optimizer. Applications and experience on steel frame and truss structures are discussed. The results of comparative studies of the GA against other various discrete and continuous optimization algorithms for a class of representative structural design problems are reported to show the efficiency of the former. It is observed that a GA often finds the region of the search space containing the global optimum, but not the true optimum itself. Also, in this study an approach based on a proposed multilevel optimization is tested and proved to overcome this shortcoming.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/2fc3fe7b81ed4bd1db4659f28fbdffb77cbe10d7

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In Civil and Structural Engineering

Citations in the corpus

Mathematical and Metaheuristic Applications in Design Optimization of Steel Frame Structures: An Extensive Review (2013, methodology): “[132] developed GAOS program which implements the constraints from the design code and determines the optimum readymade hot-rolled”
Size optimization of space trusses using Big Bang-Big Crunch algorithm (2009, methodology): “76 lb for the BB–BC [46], ACO [22], PSO [32] and GA [7], respectively.”
Optimization of 3d trusses with adaptive approach in genetic algorithms (2006, methodology): “Therefore, the penalty given to the individual becomes very small [14] when the power of the constraints is imposed.”
The harmony search heuristic algorithm for discrete structural optimization (2005, methodology): “…GA-based discrete optimization methods have been especially studied by many researchers, including Rajeev and Krishnamoorthy (1992, 1997), Lin and Hajela (1992), Wu and Chow (1995a,b), Galante (1996), Camp et al. (1998), Pezeshk et al. (2000), Erbatur et al. (2000), and Kaveh and Kalatjari (2002).”