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.

In this paper, Teaching-Learning-Based Optimization (TLBO) algorithm is employed for controlling the speed of induction motors using fuzzy sliding mode controller. The proposed control scheme formulates the design of the controller as an optimization problem. First, a sliding mode speed controller with an integral switching surface is designed, in which the acceleration information for speed control is not required. In this case, the upper bound of the lumped uncertainties including the parameter uncertainties and the load disturbance must be available. The importance of this parameter on the system performance is illustrated. Then, the fuzzy sliding mode speed controller is designed to estimate the upper bound of the lumped uncertainties. Finally, TLBO algorithm is adopted to determine the optimal upper bound of the uncertainty. Simulation results are given to demonstrate the superiority of the proposed controller in comparison with the proportional-integrator, traditional sliding mode controller, fuzzy sliding mode controller and adaptive fuzzy sliding mode controller.

Wireless sensor networks consist of many fixed or mobile, non-rechargeable, low-cost, and low-consumption nodes. Energy consumption is one of the most important challenges due to the non-rechargeability or high cost of sensor nodes. Hence, it is of great importance to apply some methods to reduce the energy consumption of sensors. The use of clustering-based routing is a method that reduces the energy consumption of sensors. In the present article, the Self-Adaptive Multi-objective TLBO (SAMTLBO) algorithm is applied to select the optimal cluster headers. After this process, the sensors become the closest components to cluster headers and send the data to their cluster headers. Cluster headers receive, aggregate, and send data to the sink in multiple steps using the TLBO-TS hybrid algorithm that reduces the energy consumption of the cluster heads when sending data to the sink and, ultimately, an increase in the wireless sensor network’ s lifetime. The simulation results indicate that our proposed protocol (OCRP) show better performance by 35%, 17%, and 12% compared to ALSPR, CRPD, and COARP algorithms, respectively. Conclusion: Due to the limited energy of sensors, the use of meta-heuristic methods in clustering and routing improves network performance and increases the wireless sensor network's lifetime.

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.

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.