Structural optimization with teaching-learning-based optimization algorithm

Dede, Tayfun; Ayvaz, Yusuf

doi:10.12989/sem.2013.47.4.495

Cited by 25 publications

(12 citation statements)

References 45 publications

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“…e detailed information about the TLBO algorithm can be obtained from [13,15,16,20,24,35]. e owchart of processes in MATLAB-SAP2000 OAPI developed to get optimum solutions is presented in Figure 3.…”

Section: Teaching-learning-basedmentioning

confidence: 99%

“…Aydogdu and Saka [14] used ant colony optimization for irregular steel frames including the elemental warping effect. Dede and Ayvaz [15] studied structural optimization problems using the teachinglearning-based optimization algorithm. Dede [16] applied teaching-learning-based optimization on the optimum design of grillage structures with respect to LRFD-AISC.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimum Design of Braced Steel Space Frames including Soil-Structure Interaction via Teaching-Learning-Based Optimization and Harmony Search Algorithms

Daloglu

Artar

Özgan

et al. 2018

Advances in Civil Engineering

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Optimum design of braced steel space frames including soil-structure interaction is studied by using harmony search (HS) and teaching-learning-based optimization (TLBO) algorithms. A three-parameter elastic foundation model is used to incorporate the soil-structure interaction effect. A 10-storey braced steel space frame example taken from literature is investigated according to four different bracing types for the cases with/without soil-structure interaction. X, V, Z, and eccentric V-shaped bracing types are considered in the study. Optimum solutions of examples are carried out by a computer program coded in MATLAB interacting with SAP2000-OAPI for two-way data exchange. e stress constraints according to AISC-ASD (American Institute of Steel Construction-Allowable Stress Design), maximum lateral displacement constraints, interstorey drift constraints, and beam-tocolumn connection constraints are taken into consideration in the optimum design process. e parameters of the foundation model are calculated depending on soil surface displacements by using an iterative approach. e results obtained in the study show that bracing types and soil-structure interaction play very important roles in the optimum design of steel space frames. Finally, the techniques used in the optimum design seem to be quite suitable for practical applications.

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Section: Teaching-learning-basedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimum Design of Braced Steel Space Frames including Soil-Structure Interaction via Teaching-Learning-Based Optimization and Harmony Search Algorithms

Daloglu

Artar

Özgan

et al. 2018

Advances in Civil Engineering

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show abstract

“…Specifically for structural design optimization, Toğan 47,48 studied the usage of TLBO in optimization design of steel frames and trusses. Dede and Ayvaz 49,50 employed TLBO method for sizing optimization and combined sizing and shape optimization of truss structures. Degertekin and Hayalioglu 51 also performed TLBO for sizing optimization of truss structures.…”

Section: Introductionmentioning

confidence: 99%

“…53 Comparing the optimization results obtained by various versions of TLBO with other stochastic optimization methods, it indicates TLBOs generated competitive results for truss structure optimization. 47–53…”

Section: Introductionmentioning

confidence: 99%

Augmented Lagrangian teaching–learning-based optimization for structural design

Dong

Yuan

2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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Stochastic optimization methods have been widely employed to find solutions to structural design optimization problems in the past two decades, especially for truss structures. The primary aim of this study is to introduce a design optimization method combining an augmented Lagrangian function and teaching-learning-based optimization for truss and nontruss structural design optimization. The augmented Lagrangian function serves as a constraint-handling tool in the proposed method and converts a constrained optimization problem into an unconstrained one. On the other hand, teaching-learning-based optimization is employed to resolve the transformed, unconstrained optimization problems. Since the proper values of the Lagrangian multipliers and penalty factors are unknown in advance, the proposed method is implemented in an iterative way to avoid the issue of selecting them, i.e. the Lagrangian multipliers and penalty factors are automatically updated according to the violation level of all constraints. To examine the performance of the proposed method, it is applied on a group of benchmark truss optimization problems and a group of nontruss optimization problems of aircraft wing structures. The computational results obtained by the proposed method are compared to the results produced by both other version of teaching-learning-based optimization and stochastic optimization methods.

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“…The TLBO algorithm has demonstrated fine performance for the large sized design variables used in the optimization of truss structures. 36–40 In this work, the unique feature of the problem, which is different from that of other optimization problems, consists in the extremely large range of the value of the design variable, which arises from the higher order polynomial used for the normalized time parameter with a value between 0 and 1.…”

Section: Introductionmentioning

confidence: 99%

Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm

Lin

Tsai

Hsiao

2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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An optimum design of variable input speed for the Geneva mechanism is aimed at improving the kinematic performance of the traditional Geneva mechanism by eliminating infinite angular jerks and reducing the peak angular acceleration of the Geneva wheel during the indexing motion. The normalized angular velocity and acceleration of the Geneva wheel corresponding to the normalized time are introduced. A polynomial function of the normalized time is used to describe the normalized angular position of the crank, and therefore, the corresponding polynomial coefficients are considered as the design variables. The optimum design task is very specialized and difficult to solve with some evolutionary and swarm optimization methods because of the extremely large range for the value of the design variable, arising from the utilization of a higher order polynomial for the normalized time parameter with a value between 0 and 1. A new evolutionary algorithm termed teaching-learning-based optimization comprises a teacher phase and a learner phase. In the teacher phase, the entire population can be gradually shifted to a more promising region, which may be very far from the relatively small initial region. The obtained optimal results are compared with those obtained using the length-adjustable deriving link method discussed in the literature. The findings show that the difference in the effectiveness of the variable input speed method and the length-adjustable driving link method for the reduction of the peak angular acceleration of the Geneva wheel is small.

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Structural optimization with teaching-learning-based optimization algorithm

Cited by 25 publications

References 45 publications

Optimum Design of Braced Steel Space Frames including Soil-Structure Interaction via Teaching-Learning-Based Optimization and Harmony Search Algorithms

Optimum Design of Braced Steel Space Frames including Soil-Structure Interaction via Teaching-Learning-Based Optimization and Harmony Search Algorithms

Augmented Lagrangian teaching–learning-based optimization for structural design

Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm

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