• Structural Engineering and Mechanics
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• 제81권2호
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• pp.195-203
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• 2022

This paper presents the quantification and location damage detection of plane and space truss structures in a two-phase method to reduce the computations efforts significantly. In the first phase, a proposed damage indicator based on the residual force vector concept is used to get the suspected damaged members. In the second phase, using damage quantification as a variable, a teaching-learning based optimization algorithm (TLBO) is used to obtain the damage quantification value of the suspected members obtained in the first phase. TLBO is a relatively modern algorithm that has proved distinguished in solving optimization problems. For more verification of TLBO effeciency, the classical particle swarm optimization (PSO) is used in the second phase to make a comparison between TLBO and PSO algorithms. As it is clear, the first phase reduces the search space in the second phase, leading to considerable reduction in computations efforts. The method is applied on three examples, including plane and space trusses. Results have proved the capability of the proposed method to precisely detect the quantification and location of damage easily with low computational efforts, and the efficiency of TLBO in comparison to the classical PSO.

• Membrane and Water Treatment
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• 제9권2호
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• pp.129-136
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• 2018

In this contribution, the control of multivariable reverse osmosis (RO) desalination plant using proportional-integral-derivative (PID) controllers is presented. First, feed-forward compensators are designed using simplified decoupling method and then the PID controllers are tuned for flux (flow-rate) and conductivity (salinity). The tuning of PID controllers is accomplished by minimization of the integral of squared error (ISE). The ISEs are minimized using a recently proposed algorithm named as teacher-learner-based-optimization (TLBO). TLBO algorithm is used due to being simple and being free from algorithm-specific parameters. A comparative analysis is carried out to prove the supremacy of TLBO algorithm over other state-of-art algorithms like particle swarm optimization (PSO), artificial bee colony (ABC) and differential evolution (DE). The simulation results and comparisons show that the purposed method performs better in terms of performance and can successfully be applied for tuning of PID controllers for RO desalination plants.

• Structural Engineering and Mechanics
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• 제47권2호
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• pp.209-225
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• 2013

A procedure employing a Teaching-Learning Based Optimization (TLBO) method is developed to design discrete pin jointed structures. TLBO process consists of two parts: the first part represents learning from teacher and the second part illustrates learning by interaction among the learners. The results are compared with those obtained using other various evolutionary optimization methods considering the best solution, average solution, and computational effort. Consequently, the TLBO algorithm works effectively and demonstrates remarkable performance for the optimization of engineering design applications.

• Structural Engineering and Mechanics
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• 제67권6호
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• pp.617-628
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• 2018

This paper deals with the maximization of the critical buckling load of simply supported antisymmetric angle-ply plates resting on Pasternak foundation subjected to compressive loads using teaching learning based optimization method (TLBO). The first order shear deformation theory is used to obtain governing equations of the laminated plate. In the present optimization problem, the objective function is to maximize the buckling load factor and the design variables are the fibre orientation angles in the layers. Computer programming is developed in the MATLAB environment to estimate optimum stacking sequences of laminated plates. A comparison also has been performed between the TLBO, genetic algorithm (GA) and differential evolution algorithm (DE). Some examples are solved to show the applicability and usefulness of the TLBO for maximizing the buckling load of the plate via finding optimum stacking sequences of the plate. Additionally, the influences of different number of layers, plate aspect ratios, foundation parameters and load ratios on the optimal solutions are investigated.

• 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.

• 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.

• 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.

• 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.

• 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.

• 전기학회논문지P
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• 제67권3호
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.