A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied to some applications in the literature successfully. In this paper, a hybrid genetic algorithm (GA) -TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. To escape infeasible solutions emerged in the problem of interest due to realistic constraints, a combination of a random key and priority-base encoding scheme is proposed. To assess the quality of the proposed hybrid GA-TLBO algorithm, some numerical examples are conducted. Then, the results are compared with those of GA, TLBO and exact algorithms.

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.

One of the most important concerns for power system operators is how to execute the restoration process after having a blackout. In doing so, the parallel restoration is the most common method in which the desired islands are first formed and then the load of each section is restored separately at the same time. In the next step, the islands must be synchronized with having a minimum standing phase angle (SPA) between them. To do this, an optimal multi-objective scheme is defined in this paper in order to coordinate both load restoration and SPA reduction problems. The objective functions of the proposed model are the minimization of the static phase angle and the energy not supplied in which the desired constraints are also considered. For optimization process the teaching and learning optimization algorithm (TLBO) is used as a proposed technique and compared with some other intelligent algorithms. The simulations are performed by creating a connection between MATLAB software and DIGSILENT. The results obtained on the IEEE 39-bus power system show the efficiency of the proposed model.

A high accurate and low-voltage analog CMOS current divider which operates with a single power supply voltage is designed in 0.18mm CMOS standard technology. The proposed divider uses a differential amplifier and transistor in triode region in order to perform the division. The proposed divider is modeled with neural network while TLBO algorithm is used to optimize it. The proposed optimization method shows a close characteristic to the ideal current-input voltageoutput divider behavior over wide input range. By using the achieved results of the TLBO algorithm simulation results using HSPICE shows the maximum linearity error less than 0.5%.The total power consumption is below 0.14 mW with a single 1.5 V power supply. The proposed divider was laid out in standard 0.18mm CMOS technology and shows high linearity.The output voltage offset is less than 3 mV under all situations. The proposed scheme has potential to be employed in modern high-performance low-voltage analog signal processing systems.

In this research, a new intelligent control design using Teaching-Learning-Based-Optimization (TLBO) algorithm to optimize PID controller coefficients is presented. This method has been applied on the twin rotor system which has been constructed in Control Engineering Lab at Arak University. The purpose of controlling the twin rotor system is to stabilize the system in the zero degree horizontal position. After modeling and obtaining the state space description, the PID controller is designed and implemented on the system. In this study, by reviewing meta-heuristic optimization methods such as particle swarm optimization algorithm, genetic algorithm, colonial competition algorithm and differential evolution algorithm, the optimization results were compared with the above-mentioned meta-heuristic methods. With the optimization performed by the teaching and learning algorithm, the stability and faster performance of the system compared to other meta-heuristic methods can be seen. The merit of TLBO is that it does not have control parameters, which makes it convenient to employ. The simulation results of the PID controller for a twin rotor system show the effectiveness of the proposed methods.

Digital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for researchers and scholars. In this paper, we presented a new high-capacity, clear, and robust audio signaling scheme based on the DWT conversion synergy and golden ratio advantages using the TLBO algorithm. We used the TLBO algorithm to determine the effective frame length and embedded range, and the golden ratio to determine the appropriate embedded locations for each frame. First, the main audio signal was broken down into several sub-bands using a DWT in a specific frequency range. Since the human auditory system is not sensitive to changes in high-frequency bands, to increase the clarity and capacity of these sub-bands to embed bits we used the watermark signal. Moreover, to increase the resistance to common attacks, we framed the high-frequency bandwidth and then used the average of the frames as a key value. Our main idea was to embed an 8-bit signal simultaneously in the host signal. Experimental results showed that the proposed method is free from significant noticeable distortion (SNR about 29. 68dB) and increases the resistance to common signal processing attacks such as high pass filter, echo, resampling, MPEG (MP3), etc.

Introduction:  As one of the most essential needs of living beings, clean air quality has been threatened by natural and human activities. In recent years, dust storms have been increasing spatially and temporally, causing numerous damages to social, economic, and environmental health for the residents of the southern and southwestern regions of Iran. In the present study, MODIS sensor data were used to investigate dust storms and detect horizontal optical depth. Materials and Methods: The  advantages of MODIS sensor data include high spectral and temporal resolution. Additionally, meteorological station data were collected based on the study period. After preprocessing the data and preparing field observations, the necessary features for modeling were extracted using the differential method between selected bands of each MODIS sensor image, along with features extracted from ground-based meteorological station sensors. After further investigations and evaluations and using the viewpoints of meteorological experts, 36 differential features from various MODIS image bands and six features from ground-based meteorological station data, totaling 42 features, were extracted. Subsequently, using feature selection techniques, the best features were identified. A novel method named ML-Based GMDH, which improves the GMDH neural network by altering partial functions with machine learning models, was employed to detect dust concentration and horizontal optical depth. To achieve optimal accuracy, the hyper-parameters of this model were heuristically tuned using the TLBO optimization algorithm. Additionally, machine learning methods such as Basic GMDH, SVM, MLP, MLR, RF, and their ensemble models were implemented to compare with the main approach. According to the results, the TLBO-tuned ML-Based GMDH method provided superior accuracy in detecting dust concentration compared to the aforementioned machine-learning methods. Results and Discussion: The SVM-PSO method was selected as the best method in the feature selection phase, the RF method was chosen as the best method among basic classification methods, and the Ensemble SVM and Ensemble RF methods were selected as the best methods in the ensemble and classification phase. It was also observed that using the ensemble approach led to a desirable improvement in horizontal optical depth classification. In the second approach, a method titled ML-Based GMDH, which improves the GMDH neural network by altering partial functions with machine learning algorithms, was used for estimating dust concentration. Additionally, to achieve suitable accuracy, the hyper-parameters of this model were finely tuned using the TLBO optimization algorithm. The results showed that this method provided appropriate accuracy in estimating dust concentration and horizontal optical depth, out performing the best-selected methods from the first approach

Adaptive steganography methods use variable embedding capacity according to the uniformity or edges of image areas. ALSBMR is an adaptive method with two main stages: Selecting suitable pixels, and embedding them using the LSBMR method. This method utilizes two adaptive keys between the sender and the receiver to determine the block rotation angle and select the embedding path. In the original method, the keys are randomly selected by the sender with no specific criteria and then sent to the receiver. The proposed method models key selection as an optimization problem and uses Genetic algorithm (GA) and Teaching-Learning-Based Optimization (TLBO) to find the optimal keys. Two fitness functions are used to further evaluate the difference as well as the histogram difference between the cover and stego images. The results show that the image embedded with the proposed method has improved quality and security compared to the base method. Since all steganography methods require embedding keys, intelligent key selection can improve the performance of existing steganography methods.

Background: Wireless sensor networks include a set of non-rechargeable sensor nodes that interact for particular purposes. Since the sensors are non-rechargeable, one of the most important challenges of the wireless sensor network is the optimal use of the energy of sensors. The selection of the appropriate cluster heads for clustering and hierarchical routing is effective in enhancing the performance and reducing the energy consumption of sensors. Aim: Clustering sensors in different groups is one way to reduce the energy consumption of sensor nodes. In the clustering process, selecting the appropriate sensor nodes for clustering plays an important role in clustering. The use of multistep routes to transmit the data collected by the cluster heads also has a key role in the cluster head energy consumption. Multistep routing uses less energy to send information. Methods: In this paper, after distributing the sensor nodes in the environment, we use a Teaching-Learning-Based Optimization (TLBO) algorithm to select the appropriate cluster heads from the existing sensor nodes. The teaching-learning philosophy has been inspired by a classroom and imitates the effect of a teacher on learner output. After collecting the data of each cluster to send the information to the sink, the cluster heads use the Tabu Search (TS) algorithm and determine the subsequent step for the transmission of information. Findings: The simulation results indicate that the protocol proposed in this research (TLSIA) has a higher last node dead than the LEACH algorithm by 75%, ASLPR algorithm by 25%, and COARP algorithm by 10%. Conclusion: Given the limited energy of the sensors and the non-rechargeability of the batteries, the use of swarm intelligence algorithms in WSNs can decrease the energy consumption of sensor nodes and, eventually, increase the WSN lifetime.