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.

This article shows that any type of binary data can be defined as a collection from codewords of variable length. This feature helps us to define an Injective and surjective function from the suggested codewords to the required codewords. Therefore, by replacing the new codewords, the binary data becomes another binary data regarding the intended goals. One of these goals is to reduce data size. It means that instead of the original codewords of each binary data, it replaced the Huffman codewords to reduce the data size. One of the features of this method is the result of positive compression for any type of binary data, that is, regardless of the size of the code table, the difference between the original data size and the data size after compression will be greater than or equal to zero. Another important and practical feature of this method is the use of symmetric codewords instead of the suggested codewords in order to create symmetry, reversibility and error resistance properties with two-way decoding.

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.

The last decade has seen the important role of container terminals in the functionality of global trade centers. From another point of view, the high cost of Quay Cranes (QCs) is a motivation for solving a set of real-world problems including Quay Crane Assignment Problem (QCAP) and the Quay Crane Scheduling Problem (QCSP) in the hotspot of research. The main innovation of this proposal is to integrate both QCAP and QCSP to improve the performance of QC with emphasis on optimization, i. e., QCASP. A real case study in Iran was applied to validate the proposed problem which was formulated by a Mixed Integer Linear Programming (MILP). Due to the inherent complexity of the problem proposed in the real-world cases, the Teaching-Learning-Based-Optimization (TLBO) algorithm was used to  nd an optimal/global solution in a reasonable span of time. The applied TLBO was tuned by Taguchi method and validated in small instances in comparison with an exact method. The computational results showed that our proposed TLBO algorithm could solve Quay Crane Assignment and Scheduling Problem (QCASP), especially in large-size instances, successfully. Finally, some managerial implications are recommended to consider the benefits of the proposed methodology and the high-efficiency of the algorithm regarding the real case study presented.

In this paper, a new method has been proposed for the topology optimization of trusses. The method takes advantage of the equation of equilibrium between internal and external forces in the flexibility method of structural analysis. The internal forces are written as the multiplication of cross-sections of members into their stresses. The stress constraints (i. e. limits on the stress values) are then imposed on the problem and eventually, the topology optimization of trusses ends up in a Linear Programming (LP) problem. The solution to the LP problem is straightforward and results in a global optimum. Accordingly, the outcome of our formulation is a global optimum. When the displacement constraints are included among the constraints, the truss topology optimization turns into a nonlinear optimization problem. To convert the problem to a linear programming problem, we used discrete design variables and converted the problem to a binary (zero-one) integer programming. Several examples were solved and compared to the published examples in the literature. It was observed that our method of truss topology optimization ends up with the same results as the previous research works, but with much less calculations. Nevertheless, our results are proved to be the global optimum, whereas the methods used in the literature cannot prove their global optimality.

Over the last decades there has been a growing interest in algorithms inspired by the observation of natural phenomenon. Recently-introduced Binary Particle Swarm Optimization (BPSO) is considered as a powerful solution scheme, which is able to help PSO to be applied to continuous and discrete binary optimization problems. In this paper, the BPSO is addressed in details; the disadvantages of the BPSO are investigated and the solutions to these disadvantages are introduced. To show the efficiency and effectiveness of the proposed BPSOs over the original BPSO, they are applied to several benchmark problems including unimodal and multimodal functions. The results obtained show that the proposed BPSOs perform much better than the original BPSO and can be considered as alternative approaches for solving different problems.

This paper proposes an effective algorithm based on the level set method (LSM) to solve shape and topology optimization problems. Since the conventional LSM has several limitations, a binary level set method (BLSM) is used instead. In the BLSM, the level set function can only take 1 and -1 values at convergence. Thus, it is related to phase-field methods. We don’t need to solve the Hamilton-Jacobi equation, so it is free of the CFL condition and the reinitialization scheme. This favorable properties lead to a great time advantage in this method. In this paper, the BLSM is implemented with the additive operator splitting (AOS) scheme and several numerical issues of the implementation are discussed.The proposed scheme is much more efficient than the conventional level set method. Several 2D examples are presented which demonstrate the effectiveness and robustness of the proposed method.

Particle swarm optimization is an efficient population based algorithm used to solve real valued and nonlinear continuous optimization problems. To resolve binary optimization problems with PSO, binary particle swarm optimization (BPSO) has been developed. In this paper, optimum design of plates using BPSO is presented. The objective function aims at finding the optimum weight of plates with the nodal displacements selected as constraints.Numerical examples show that BPSO can be a suitable algorithm to solve optimization problems in binary search space.

The analysis of structures and, in particular, the determination of their failure mode is an essential requirement in the  eld of civil engineering. Obtaining this information in high-rise structures or structures with complex irregular layouts is a di cult process, which is very timeconsuming even with the use of specialized computer software. In this research, a new optimization algorithm, named Binary Dolphin Echolocation Algorithm, is introduced by modifying the Dolphin Echolocation Optimization Algorithm. In recent years, meta-heuristic algorithms have been used extensively in engineering optimization problems. This research presents an automated approach to assess plastic loads and failure modes of 2D frames. The plastic analysis of moment frames has been optimized using the Binary Dolphin Echolocation Optimization Algorithm. Not only the proposed algorithm is applicable in this speci c plastic analysis problem, but also it can be extensively used in any binary optimization problems. The method of elementary mechanism combination in the plastic analysis of moment frames is based on creating the basic collapse mechanisms, which, following their combination, should reach the minimum coe cient of plastic collapse loads. By using virtual work theory, the collapse load factor of each mechanism is achievable. The critical collapse load factor is then the minimum of them among all possible collapse mechanisms. The e ciency of this algorithm is veri ed using two frame samples with their exact response in the search for minimum load factors and their corresponding collapse mode. A comparison of the results shows that the proposed method provides outstanding results with high precision and speed and demonstrates the structure's failure mechanism. Meanwhile, the modi cations made in this method have signi cantly reduced the volume of calculations. Moreover, applying changes to the Dolphin Echolocation Optimization Algorithm led to the use of this optimization algorithm for binary problems for the  rst time, ultimately resulting in a good convergence rate.