• 전기학회논문지
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• 제64권1호
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• pp.90-98
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• 2015

In this paper, an advanced teaching-learning based optimization(TLBO) method for the magnetic levitation controller of Maglev transportation system is proposed to optimize the control performances. An attraction-type levitation system is intrinsically unstable and requires a delicate control. It is difficult to completely satisfy the desired performance through the methods using conventional methods and intelligent optimizations. In the paper, we use TLBO and clonal selection algorithm to choose the optimal control parameters for the magnetic levitation controller. To verify the proposed algorithm, we compare control performances of the proposed method with the genetic algorithm and the particle swarm optimization. The simulation results show that the proposed method is more effective than conventional methods.

• Advances in robotics research
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• 제2권1호
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• pp.13-31
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• 2018

This paper presents the performance of Teaching-Learning-Based Optimization (TLBO) algorithm for optimum static balancing of a robot manipulator. Static balancing of robot manipulator is an important aspect of the overall robot performance and the most demanding process in any robot system to match the need for the production requirements. The average force on the gripper in the working area is considered as an objective function. Length of the links, angle between them and stiffness of springs are considered as the design variables. Three robot manipulator configurations are optimized. The results show the better or competitive performance of the TLBO algorithm over the other optimization algorithms considered by the previous researchers.

• Structural Engineering and Mechanics
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• 제71권2호
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• pp.139-151
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• 2019

In a project schedule, it is possible to reduce the time required to complete a project by allocating extra resources for critical activities. However, accelerating a project causes additional expense. This issue is addressed by finding optimal set of time-cost alternatives and is known as the time-cost trade-off problem in the literature. The aim of this study is to identify the optimal set of time-cost alternatives using a multiobjective teaching-learning-based optimization (TLBO) algorithm integrated with the non-dominated sorting concept and is applied to successfully optimize the projects ranging from a small to medium large projects. Numerical simulations indicate that the utilized model searches and identifies optimal / near optimal trade-offs between project time and cost in construction engineering and management. Therefore, it is concluded that the developed TLBO-based multiobjective approach offers satisfactorily solutions for time-cost trade-off optimization problems.

• Computers and Concrete
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• 제34권2호
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• pp.151-168
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• 2024

This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R² and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R² and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.

• Advances in Computational Design
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• 제2권4호
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• pp.313-331
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• 2017

In this study, teaching-learning based optimization (TLBO) is improved by incorporating model of multiple teachers, adaptive teaching factor, self-motivated learning, and learning through tutorial. Modified TLBO (MTLBO) is applied for simultaneous topology, shape, and size optimization of space and planar trusses to study its effectiveness. All the benchmark problems are subjected to stress, displacement, and kinematic stability constraints while design variables are discrete and continuous. Analyses of unacceptable and singular topologies are prohibited by seeing element connectivity through Grubler's criterion and the positive definiteness. Performance of MTLBO is compared to TLBO and state-of-the-art algorithms available in literature, such as a genetic algorithm (GA), improved GA, force method and GA, ant colony optimization, adaptive multi-population differential evolution, a firefly algorithm, group search optimization (GSO), improved GSO, and intelligent garbage can decision-making model evolution algorithm. It is observed that MTLBO has performed better or found nearly the same optimum solutions.

• Steel and Composite Structures
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• 제28권4호
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• pp.509-516
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• 2018

The differential quadrature (DQ) and teaching-learning based optimization (TLBO) methods are coupled to introduce a hybrid numerical method for maximizing fundamental natural frequency of laminated composite skew plates. The fiber(s) orientations are selected as design variable(s). The first-order shear deformation theory (FSDT) is used to obtain the governing equations of the plate. The equations of motion and the related boundary conditions are discretized in space domain by employing the DQ method. The discretized equations are transferred from the time domain into the frequency domain to obtain the fundamental natural frequency. Then, the DQ solution is coupled with the TLBO method to find the maximum frequency of the plate and its related optimum stacking sequences of the laminate. Convergence and applicability of the proposed method are shown and the optimum fundamental frequency parameter of the plates with different skew angle, boundary conditions, number of layers and aspect ratio are obtained. The obtained results can be used as a benchmark for further studies.

• Smart Structures and Systems
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• 제32권1호
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• pp.37-47
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• 2023

Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• Structural Engineering and Mechanics
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• 제63권5호
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• pp.607-615
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• 2017

This study presents optimum design of plane steel bridges considering corrosion effect by using teaching-learning based optimization (TLBO) method. Optimum solutions of three different bridge problems are linearly carried out including and excluding corrosion effect. The member cross sections are selected from a pre-specified list of 128 W profiles taken from American Institute of Steel Construction (AISC). A computer program is coded in MATLAB to carry out optimum design interacting with SAP2000 using OAPI (Open Application Programming Interface). The stress constraints are incorporated as indicated in AISC Allowable Stress Design (ASD) specifications and also displacement constraints are applied in optimum design. The results obtained from analysis show that the corrosion effect on steel profile surfaces causes a crucial increase on the minimum steel weight of bridges. Moreover, the results show that the method proposed is applicable and robust to reach the destination even for complex problems.

• Steel and Composite Structures
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• 제33권5호
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• pp.747-753
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• 2019

Metaheuristic algorithm is used to solve the weight minimization problem of truss structures considering shape, and sizing design variables. The cross-sectional areas of the line element in trusses are the design variables for size optimization and the changeable joint coordinates are the shape optimization used in this study. The design of plane and spatial truss structures are optimized by metaheuristic technique named Teaching-Learning-Based Optimization (TLBO). Finite element analyses of structures and optimization process are carried out by the computer program visually developed by the authors coded in MATLAB. The four benchmark problems (trusses 2D ten-bar, 3D thirty-seven-bar, 3D seventy-two-bar and 2D two-hundred-bar) taken from literature are optimized and the optimal solution compared the results given by previous studies.