Continuum structural topology design with genetic algorithms

Jakiela, Mark J.; Chapman, Colin D.; Duda, James W.; Adewuya, Adenike; Saitou, Kazuhiro

doi:10.1016/s0045-7825(99)00390-4

Cited by 177 publications

(80 citation statements)

References 28 publications

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“…Jakiela et al 39 state that, in general, GA based solutions may require 10 to 100 times the number of function evaluations as would be required by homogenization based solutions.…”

Section: B Cantilevered Beamsmentioning

confidence: 99%

Topology Optimization of Continuum Structures Using Element Exchange Method

Rouhi¹

2008

49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference &Amp;lt;br> 16th AIAA/ASME/AHS Ada

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In this research, a new zeroth-order (non-gradient based) topology optimization methodology for compliance minimization was developed. It is called the Element Exchange Method (EEM). The principal operation in this method is to convert the less effective solid elements into void elements and the more effective void elements into solid elements while maintaining the overall volume fraction constant. The methodology can be integrated with existing FEA codes to determine the stiffness or other structural characteristics of each candidate design during the optimization process.This thesis provides details of the EEM algorithm, the element exchange strategy, checkerboard control, and the convergence criteria. The results for several two-and three-dimensional benchmark problems are presented with comparisons to those found using other stochastic and gradient-based approaches. Although EEM is not as efficient as some gradient-based methods, it is found to be significantly more efficient than many other non-gradient methods reported in the literature such as GA and PSO.

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“…Jakiela et al 39 state that, in general, GA based solutions may require 10 to 100 times the number of function evaluations as would be required by homogenization based solutions.…”

Section: B Cantilevered Beamsmentioning

confidence: 99%

Topology Optimization of Continuum Structures Using Element Exchange Method

Rouhi¹

2008

49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference &Amp;lt;br> 16th AIAA/ASME/AHS Ada

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“…The solution converges to the optimal topology by searching of the material distribution in each finite element. As it shown in Figure 9, the float elements are eliminated automatically without need to using the connectivity analysis technique [5].…”

Section: Applicationmentioning

confidence: 99%

“…Hence, the original '0-1' optimization problem was attacked directly by using a bit-array representation and a genetic algorithm. The work of Sandgren and his co-workers, using bit-array representation, has been extended by Jakiela and his coworkers [3][4][5], by Schoenauer and his co-workers [6,7,9], by Fanjoy and Crossley [8,10], and, more recently, by Wang and Tai [11]. Although all these extensions can well prevent checkerboard patterns by exploiting a connectiva Corresponding author: matthieu.domaszewski@utbm.fr ity restriction, the other numerical instabilities in structural topology optimization such as mesh dependency and one-node connections still exist.…”

Section: Introductionmentioning

confidence: 99%

An adjacency representation for structural topology optimization using genetic algorithm

Sid

Domaszewski

Peyraut

2007

Int. J. Simul. Multidisci. Des. Optim.

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-A new approach for continuum structural topology optimization using genetic algorithms is presented in this paper. The proposed approach is based on a representation by adjacency where the principle is founded on the concept of connectivity of finite elements, considered as cells. This principle is expressed by an adjacency matrix similar to that used in the graph theory. The encoding of the structure solutions uses this matrix by transforming it into a binary string. The research of optimal solution, i.e. the optimal material distribution, is interpreted in this approach by the determination of the connectivity of elements (cells). Using density variable, the approach has some common points with the homogenization techniques. The proposed approach is tested with simple benchmark applications.

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“…Other methods developed after this include solid isotropic material with penalization (SIMP) (Bendsøe 1989;Zhou and Rozvany 1991), evolutionary structural optimization (ESO) (Xie and Steven 1993;Xie and Steven 1997), bidirectional evolutionary structural optimization (BESO) (Querin, Steven, and Xie 1998;Yang et al 1999;Aremu et al 2013), level-set method (Wang, Wang, and Guo 2003;Allaire, Jouve, and Toader 2004) and evolutionary-based algorithms, e.g. the genetic algorithm (GA) (Jakiela et al 2000) and differential evolution (DE) (Fiore et al 2016). Although many of the proposed topology optimization algorithms have been demonstrated for classical problems, such as Michell-type structures and cantilever beams with rectangular domains, less attention has been paid to applying these algorithms to three-dimensional (3D), real-life structures and real loading scenarios.…”

Section: Introductionmentioning

confidence: 99%

Topology optimization of geometrically nonlinear structures using an evolutionary optimization method

Abdi

Ashcroft

Wildman

2018

Engineering Optimization

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. ABSTRACTThe topology optimization using isolines/isosurfaces and extended finite element method (Iso-XFEM) is an evolutionary optimization method developed in previous studies to enable the generation of high-resolution topology optimized designs suitable for additive manufacture. Conventional approaches for topology optimization require additional postprocessing after optimization to generate a manufacturable topology with clearly defined smooth boundaries. Iso-XFEM aims to eliminate this timeconsuming post-processing stage by defining the boundaries using isovalues of a structural performance criterion and an extended finite element method (XFEM) scheme. In this article, the Iso-XFEM method is further developed to enable the topology optimization of geometrically nonlinear structures undergoing large deformations. This is achieved by implementing a total Lagrangian finite element formulation and defining a structural performance criterion appropriate for the objective function of the optimization problem. The Iso-XFEM solutions for geometrically nonlinear test cases implementing linear and nonlinear modelling are compared, and the suitability of nonlinear modelling for the topology optimization of geometrically nonlinear structures is investigated. ARTICLE HISTORY

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Continuum structural topology design with genetic algorithms

Cited by 177 publications

References 28 publications

Topology Optimization of Continuum Structures Using Element Exchange Method

Topology Optimization of Continuum Structures Using Element Exchange Method

An adjacency representation for structural topology optimization using genetic algorithm

Topology optimization of geometrically nonlinear structures using an evolutionary optimization method

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