tlbo algorithm

The resilient modulus (MR) of road materials is one of the most important parameters in the analysis and design of pavement. This parameter is used in both empirical methods and mechanistic-empirical methods as the main parameter for expressing the stiffness and behavior of road construction materials. To determine this parameter in the laboratory, it is necessary to perform a dynamic tri-axial loading test under various confining and deviator stresses, which is a time- and cost-intensive approach. In this paper, a wavelet neural network (WNN) hybridized with the teacher learning based optimization (TLBO) algorithm was used to model the MR of unbound subbase materials. The input variables included maximum dry density, uniformity coefficient, curvature coefficient, percent passing No. 200 sieve, confining stress, and deviator stress and output variable was resilient modulus of the unbound subbase materials. The results of this study indicate that increasing the number of neurons in the hidden layer to more than 20 neurons has little effect on increasing the accuracy of the wavelet neural network and the Mexican Hat wavelet function has the best result in predicting the resilient modulus. The results of this study also indicate that the WNN-TLBO method is more accurate than the ANN method in predicting the MR of unbound subbase materials. External validation results indicate that the WNN-TLBO method satisfy all the necessary criteria, which indicates the high predictive potential of this method. The results of sensitivity analysis indicate that the degree of importance of the confined stress is higher than other variables for predicting the resilience modulus. A parametric analysis was also done to study the effects of each input variable on the MR.

Roadheader machine is one of those machines that have high drilling capability in rocks with low to medium strength. Hence they are widely used in underground excavations. Estimating the performance of roadheader machine is one of the main and important issues in estimating the approximate project completion time as well as project costs. Therefore, the purpose of this paper is to propose intelligent forecasting models for estimating the performance of roadheader machine by two intelligent methods (the firefly algorithm (FA) and the Teaching-learning based optimization algorithm (TLBO)) and using a database (a case study). Is. In these models, the Schmidt hammer rebound values and the rock quality degree (RQD) are used as input parameters and the cutting rate of the roadheader is used as the output parameter. Finally, to evaluate the accuracy of the models and modeling, the indices of square correlation coefficient (R2), variance account for (VAF), root mean square error (RMSE) and mean square error (MSE) have been used. The results indicated that the two models have strong potentials to estimate roadheader performance with high degrees of accuracy and robustness.

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.

Image encryption is a crucial component of data security nowadays. Due to its inherent properties such as sensitivity to initial condition, ergodicity and systematic parametrization chaos systems have been extensively used in image encryption. However, one needs to optimize the chaos for each image being encrypted to provide high quality encryption. Features such as high entropy or low pixel correlation are measures of interest in image encryption. In this paper, we propose a novel technique to optimize a chaos based image encryption algorithm. The image is confused using logistic map as chaos function and diffused by modified Knuth shuffling algorithm. We use the logistic map parameters as encryption key. We then harness TLBO and GSA evolutionary algorithms to speed up the optimization process of these parameters. The optimization method yields parameters that lead to lowest correlation among adjacent pixels or highest entropy. We compare the quality and security of our cryptosystem with some famous image encryption methods in the literature.

Placement and installation of capacitors in electrical power distribution systems have being done in order to reducing losses , improving voltage profile and realising network lines capacity. In this Thesis , capacitor Placement and sizing of it are reviewed in radial distribution systems to minimizing three objective’s functions , system’s losses , Compensation costs and Voltage Profile Index and it implemented in the actual system by using teaching and learning Algorithm (TLBO) .This Algorithm based on real class’s relationships and including two phases of teacher and student. Also because of the relationships in this algorithm compare to the other algorithm’s methods , there are fewer formula and involves less inputs, therefore, the convergence speed is high and the response time is fewer. locate and determine the optimal capacity of the capacitor with respect to the fixed load modle and three objective functions have been studied. This Algorithm has been Implemented on a distributed networks of 22 - 69 – 85 bus and 4 bus distribution network of Nezamieh’s Ahvaz relevant to Oil Pipelines and Telecommunication Company of Iran and The results were analyzed by MATLAB simulation.These results represent better situation compare to other technics with 55% reducing of total cost in cost function and 53% reducting of losses in losses function and up to 99% improving in voltage profile index..