Making inexpensive and efficient is one of the main requirements in the structural design process. In recent decades, the Optimization algorithms have been developed for use in the engineering sciences. Optimization algorithms based on the natural phenomena are most useful criteria in structural design. In engineering, truss is commonly used in steel structures. In this paper, Teaching-Learning-Based Optimization (TLBO) algorithm has been used to optimize the steel truss. TLBO can calculate accurate and optimum value of a functions, using only the common controlling parameters such as the mean value in each iteration. Four steel trusses have been analyzed to investigate how TLBO improves designing process of the steel trusses. The results shown that TLBO algorithm has satisfactory performance with less calculation than other Optimization algorithms.

During the recent decade, meta-heuristic algorithms have been developed by researchers for structural Optimization. Multiobjective Optimization aims at finding a trade-off between objective functions. In this paper, Teaching-Learning-Base Optimization (TLBO) algorithm is developed for multiobjective Optimization of steel frames, called Non-dominated Sorting Teaching-Learning-Based Optimization (NSTLBO) algorithm. To evaluate the efficiency of the algorithm, five benchmark steel frames including three moment resisting frame and two braced frames are optimized using NSTLBO. The weight of the structure and the maximum displacement are considered as the objective functions to be minimized. Sizes of the elements are employed as design variables. The structures analyzed using a developed code in MATLAB, and designed according to the AISC-LRFD specifications. Results including trade-off between the objective functions showed the efficiency of the algorithm.

Increasing the profits and reducing the risks have always been of the most important issues of concern to the investors in the financial markets. In recent years, many solutions and proposals have been suggested in respect to the frequency of portfolio Optimization issue, with the highest return and the lowest possible risk. One of the most prominent suggestions is the Markowitz Model which is mostly known as the Modern Portfolio Theory. On the other hand, the TLBO algorithm which has been presented in 2010 is one of the most efficient meta-heuristic methods to solve the Optimization problem.In this study, we are attempting to solve the portfolio Optimization problem, according to the framework of the model introduced by Markowitz and using TLBO algorithm. For this purpose, the data related to the returns of 20 companies listed in TSE during the period 2012-2016 were collected. It is worth mentioning that four criteria including variance, mean absolute deviation, semi-variance and conditional value at risk (CvaR) were used in order to measure the risk level in this investigation.

High dimensionality is the biggest problem when working with large datasets. Feature selection is a procedure for reducing the dimensionality of datasets by removing additional and irrelevant features; the most effective features in the dataset will remain, increasing the algorithms’ performance. In this paper, a novel procedure for feature selection is presented that includes a binary teaching learning-based Optimization algorithm with mutation (BMTLBO). The TLBO algorithm is one of the most efficient and practical Optimization techniques. Although this algorithm has fast convergence speed and it benefits from exploration capability, there may be a possibility of trapping into a local optimum. So, we try to establish a balance between exploration and exploitation. The proposed method is in two parts: First, we used the binary version of the TLBO algorithm for feature selection and added a mutation operator to implement a strong local search capability (BMTLBO). Second, we used a modified TLBO algorithm with the self-learning phase (SLTLBO) for training a neural network to show the application of the classification problem to evaluate the performance of the procedures of the method. We tested the proposed method on 14 datasets in terms of classification accuracy and the number of features. The results showed BMTLBO outperformed the standard TLBO algorithm and proved the potency of the proposed method. The results are very promising and close to optimal.

The complicated problem of truss shape and size Optimization with multiple frequency constraints is investigated in this paper. A recently developed metaheuristics called teachinglearning- based Optimization (TLBO) algorithm is used for the first time to solve this kind of problem. Contrary to other metaheuristics, the procedure of TLBO is simple to implement since no tuning parameters need to be adjusted. Analyses of structures are performed by a finite element code in MATLAB which is used in conjunction with an Optimization code based on TLBO. Various benchmark problems are solved with this technique and the results are compared with those found by other methods including metaheuristics such as PSO, HS and FA. In all test cases, the results show that TLBO leads to very satisfactory results i.e. lighter structures which satisfy all frequency constraints. The results of this study indicate excellent inherent capacity of the approach in dealing with complicated dynamic non-linear Optimization problems.

The Traveling Salesman Problem (TSP) is a well-known problem in Optimization and graph theory, where finding the optimal solution has always been of significant interest. Optimal solutions to TSP can help reduce costs and increase efficiency across various fields. Heuristic algorithms are often employed to solve TSP, as they are more efficient than exact methods due to the complexity and large search space of the problem. In this study, meta-heuristic algorithms such as the Genetic algorithm and the Teaching-Learning Based Optimization (TLBO) algorithm are used to solve the TSP. Additionally, a discrete mutation phase is introduced to the TLBO algorithm to enhance its performance in solving the TSP. The results indicate that, in testing two specific models of the TSP, the modified TLBO algorithm outperforms both the Genetic algorithm and the standard TLBO algorithm in terms of convergence to the optimal solution and response time.

Because the Internet of Things (IoT) deals with large amounts of data, it is not easy to process and store this amount of data. However, many of its applications suffer from cloud computing challenges such as latency, location awareness and real-time mobility support. Fog calculations help provide solutions to these challenges. This paper includes an IoT network simulation for allocating optimal shared resources in fog computing to solve the mix integer nonlinear programming (MINLP) problem, which aims to maximize the profitability of cloud service providers through fog computing. The network architecture consists of three layers: cloud service providers, fog nodes, and users. In this paper, the three-layer network is simulated and the algorithm used in this problem is the Teaching–learning-based Optimization (TLBO) algorithm, which uses two phases of learning and teaching for the three parameters of cloud service providers’ revenue, average delay and user satisfaction for selecting the best node with the aim of allocating shared resources. This algorithm is implemented on the model and compared with a random method. This model and algorithm increases the profit of service providers compared to the algorithms used to solve similar models.

This paper investigates the accuracy and convergence rate of different metaheuristic algorithms in determining the stiffness of structural elements using structural modal parameters and defining a suitable objective function. To achieve this purpose, three different structures, including a three-story one-dimensional frame, a six-story one-dimensional frame and a two-dimensional truss, were investigated. The metaheuristic algorithms, employed in this study, were Genetic algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization. The objective function utilized in this study consists of two terms; the first part involves the squared difference between the first frequency of the structure obtained from the responses of the investigated structure and the first frequency obtained from the hypothetical stiffness matrix in each generation of algorithms. The second part measures the norm of the difference between the first mode shape of the structure obtained from the responses of the investigated structure and the first mode shape obtained from the hypothetical stiffness matrix in each generation of algorithms. By minimizing the objective function, the Genetic algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization determined the element stiffness of the three-story, six-story and truss structures, thus demonstrating the high efficiency of metaheuristic algorithms in resolving unknown parameters of structures. The average run time for the Genetic algorithm was 3.38 seconds, 4.47 seconds, and 15.73 seconds for the three respective problems. For Particle Swarm Optimization, the times were 3.76 seconds, 6.47 seconds, and 16.76 seconds. The Teaching–learning-based Optimization achieved times of 1.92 seconds, 4.51 seconds, and 12.76 seconds. The Teaching–learning-based Optimization exhibited the highest convergence rate and the lowest error compared to the Genetic algorithm and Particle Swarm Optimization. For example, in the two-dimensional truss, the values of the objective function in the last iteration of the Genetic algorithm, Particle Swarm Optimization, and Teaching–learning-based Optimization were , and , respectively. The Particle Swarm Optimization demonstrated an acceptable convergence rate and error compared to the Genetic algorithm. The Genetic algorithm, however, displayed a significant error rate in determining the stiffness of structural elements compared to the other two algorithms.

The polite listener In England stares at the speaker attentively and blinks his eyes occasionally as a sign of interest. That eye-blink says nothing to Americans, who expect the listener to nod or to murmur something such as "mm - hmm”. And in some parts of  Far East, it is impolite to look at the other person at all during conversation.It is often argued that, in teaching foreign languages, culture and language are interwoven in a way that it is not possible to present language without its culture. However, until a new method of teaching language, called communicative approach, which is sociolinguistically oriented, had not come into existence, this belief was not used to be observed in textbooks and language classes. This method intends to create situations in the classroom in which foreign language can be used as naturally and authentically as the native speakers of target language use it for communicative purposes.The problem of teaching culture, nevertheless, does not end here. Nowadays, foreign languages are taught with different and varied objectives. Is it necessary for all foreign language learners to get familiar with the culture of foreign language they are supposed to learn? As Schumann (1984) claims, is it true that second language learning is impossible, unless one gives up his/her own native culture and adopts the culture of foreign language community? What are some relevant issues regarding teaching English in Iran? These are actually some basic questions raised in this article. Whereas definite answers are not necessarily provided in this paper, some areas of research related to culture, and teaching foreign language are put forward.