Teaching And Learning Based Optimization Research Articles

Optimization of BLDC-based electric vehicles: Vehicle dynamics modelling through dual-motor approach and designing a novel augmented TLBO algorithm for PID control

The work primarily focuses on increasing the efficiency of EV drive in electric two-wheeler by working on several aspects, such as modulating the vehicle’s design, optimizing the control strategy, and increasing the speed range using a dual-motor approach. The dynamics of electric two-wheeler have been discussed with a mathematical vehicle model and further tuning of several aspects. Besides, this paper also introduces a novel Augmented Teaching and Learning based Optimization (ATLBO) technique designed exclusively to control BLDC motors for the electric two-wheeler vehicle. Besides, the designed technique has been implemented for the widely used commercial e-bike of Hero Company. Most two-wheeler electric motors are mounted on the rear side of the wheel, which has a limited speed range. Therefore, an analysis has been performed to increase the vehicle’s speed range using a dual motor, from 45 km hr−1 to 62 km hr−1, proving to be a viable alternative to a single motor generally used in an electric bike. ATLBO technique has been designed against a conventional TLBO to optimize the proportional-integral-derivative (PID) controller for the speed control of a linear brushless DC (BLDC) motor. The proposed method has several advantages, including ease of implementation, a consistent convergence characteristic, and high computational efficiency. Furthermore, the literature has validated the merits of the presented novel control technique. The only disadvantage of using a dual motor is the initial cost, but the overall cost is moderated in the long-term usage for its augmented performance parameters. The performance parameters of the above technique are analyzed against other optimization techniques like conventional Teaching and Learning based optimization (TLBO), Particle Swarm Optimization (PSO). MATLAB/Simulink models the brushless DC motor and implements ATLBO, TLBO, and PSO algorithms. It has been found that the response obtained from ATLBO is comparatively much faster than other optimization techniques, which supports the motor for quick acceleration as well as more efficient in improving the step response characteristics such as rise time, settling time, and steady-state error in the speed control of a linear BLDC motor.

A Novel Combination of Genetic Algorithm, Particle Swarm Optimization, and Teaching-Learning-Based Optimization for Distribution Network Reconfiguration in Case of Faults

Reconfiguring distribution networks involves modifying their topological structure by managing switch states. This process is crucial in smart grids, as it can isolate faults, minimize power loss, and enhance system stability. However, in existing research, the reconfiguration task is often treated as a problem of either single- or multi-objective optimization and frequently overlooks the issue's multimodality. As a result, the solutions derived may be inadequate or unfeasible when facing environmental changes. In this study, the objective function of minimizing power loss considers the case of faults in the distribution grid. Coordinating the initial population division of the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) and the Teaching and Learning-Based Optimization (TLBO) algorithms accelerates the process of finding the optimal solution, resulting in faster and more reliable results. The proposed method was tested on the IEEE-33 bus test system and was compared with other methods, demonstrating reliable results and superior efficiency.

Optimization of distribution network operating parameters in grid tied microgrid with electric vehicle charging station placement and sizing in the presence of uncertainties

ABSTRACT The power generated by renewable sources is prone to uncertainties which encourages to perform effective modelling to ensure the reliable operation of the grid-connected microgrid (MG) system. This work focuses on the uncertainty modelling of primary sources of solar power, wind power and electric vehicle (EV) load considering three factors battery capacity, state of charge (SoC) and type of EVs charged by employing Monte-Carlo Simulation (MCS) to minimize the distribution system's voltage stability, reliability and power loss (VRP) index. The objective function is tested on the modified IEEE-33 bus distribution system under three diverse scenarios with optimum sizing and placement of RESs and EVCS. To obtain optimal solutions for the proposed problem in a reasonable computation time, modified version of teaching and learning-based optimization (TLBO) and the JAYA algorithm are applied as the rate of convergence is superior to other existing methods in the literature and does not require any precise control parameters. For all the scenarios, it can be seen that the modified JAYA algorithm outperforms TLBO and other existing approaches. The findings of the results reveal the efficacy of uncertainty modelling in a proposed grid-connected DC MG to curtail the VRP index.

Development and solving of optimization model for WEDM of AISI D3 using TLBO algorithm

Manufacturing companies employ advanced machining techniques to create complex and accurate products, which require selecting optimum process parameters to achieve the desired quality and cost-effectiveness. Traditional techniques such as Design of Experiments and Response Surface Methodology optimize locally and may not be sufficient for complex systems. This study aims to optimize the Wire Electrical Discharge Machining process of AISI D3 material using Response Surface Methodology and Teaching and Learning-Based Optimization (TLBO), which can optimize globally. The proposed model can improve the quality and cost-effectiveness of the machining process, making it more suitable for various manufacturing applications.

Efficient (2N+1) selective harmonic elimination in modular multilevel converters using an evolutionary many-tasking approach with prior knowledge

(2N+1) selective harmonic elimination pulse with modulation (SHE-PWM) is an important technique for a modular multi-level converter (MMC) to enhance the control ability and improve the fundamental wave amplitude accuracy of the output waveform. While finding optimal switch angles by solving the complex non-linear equation system is one of its main challenges. A current popular solution is to run a metaheuristic or evolutionary algorithm repeatedly on various modulation indexes. These methods ignore the fact that some of the optimization tasks are similar, and the optimization experiences could be shared among these similar tasks. To fill this gap, this study proposes an evolutionary many-tasking approach with prior knowledge (EMT-PK). EMT-PK facilitates positive knowledge transfer between similar tasks and is capable to solve all the sub SHE-PWM problems simultaneously through a single run. Numerical results on 7-, 9- and 11-level MMCs show that EMT-PK provides superior results for the (2N+1) SHE-PWM in comparison with 8 other widely used metaheuristics/evolutionary algorithms in this field including differential evolution (DE), ant colony optimization (ACO), teaching and learning-based optimization (TLBO), bee algorithm (BA), genetic algorithm (GA), particle swarm optimization (PSO), generalized pattern search (GPS) and asynchronous particle swarm optimization-genetic algorithm(APSO-GA). The superior performance of EMT-PK is further validated by the MATLAB Simulink simulation and the laboratory experiment on a 7-level MMC.

Comparative Study to Optimize Surface Roughness of the Titanium Alloy Ti-6Al-4V by Applying Taguchi, RSM and TLBO Methods

Titanium alloys are used in aeronautics and the shipbuilding industry for their good intrinsic properties, namely low density (40% less than steel), very good mechanical properties and resistance to corrosion. The purpose of this study is to optimize the cutting conditions during the turning of Ti-6Al-4V titanium alloy with Minimum of Quantity of Lubrication (MQL) conditions leading to minimize the surface roughness (Ra). The tests were carried out according to a Taguchi L18 design plan by varying four input factors namely: the cutting speed, the feed rate, the depth of cut and the cutting tool material (coated carbide with (PVD) (GC1125) and uncoated carbide (H13A)). Analysis of variance (ANOVA) was used to found the contribution of each factor and to determine which parameters had a significant influence on the surface roughness. The treatment of the results made it possible to propose a mathematical model, which allows predicting Ra. In addition, Taguchi Signal/Noise (S/N) analysis was used in order to optimize the cutting conditions permitting to minimize Ra. The Desirability Function (DF) was also determined. In addition, the obtained results were compared to the one determined using Response Surface Methodology (RSM) and Teaching and Learning Based Optimization (TLBO). It is important to note that the TLBO method gave a very satisfactory result.

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

Robust Facial Expression Recognition Using an Evolutionary Algorithm with a Deep Learning Model

The most important component that can express a person’s mental condition is facial expressions. A human can communicate around 55% of information non-verbally and the remaining 45% audibly. Automatic facial expression recognition (FER) has now become a challenging task in the surveying of computers. Applications of FER include understanding the behavior of humans and monitoring moods and psychological states. It even penetrates other domains—namely, robotics, criminology, smart healthcare systems, entertainment, security systems, holographic images, stress detection, and education. This study introduces a novel Robust Facial Expression Recognition using an Evolutionary Algorithm with Deep Learning (RFER-EADL) model. RFER-EADL aims to determine various kinds of emotions using computer vision and DL models. Primarily, RFER-EADL performs histogram equalization to normalize the intensity and contrast levels of the images of identical persons and expressions. Next, the deep convolutional neural network-based densely connected network (DenseNet-169) model is exploited with the chimp optimization algorithm (COA) as a hyperparameter-tuning approach. Finally, teaching and learning-based optimization (TLBO) with a long short-term memory (LSTM) model is employed for expression recognition and classification. The designs of COA and TLBO algorithms aided in the optimal parameter selection of the DenseNet and LSTM models, respectively. A brief simulation analysis of the benchmark dataset portrays the greater performance of the RFER-EADL model compared to other approaches.

Boosted backtracking search optimization with information exchange for photovoltaic system evaluation

AbstractThe determination of photovoltaic (PV) parameters is of great importance for the reliability of solar system operation, continuity of the load power consumption, and control management of the energy source. Therefore, this study proposes an advanced backtracking search optimization algorithm (BSA) equipped with teaching and learning‐based optimization (TLBO), named TLBOBSA, to accurately simulate the PV model. During the evaluation of the proposed algorithm, the concept of teaching from TLBO is introduced into the BSA to guide optimal individuals, thus improving the convergence rate of the algorithm. The learning behavior among individuals in the student phase of TLBO facilitates interindividual learning and provides beneficial information for its evolution, which is introduced into the BSA to ensure the diversity of the population. The comprehensive test results of different PV module models in different environmental conditions show that the proposed algorithm is more advantageous for parameter extraction than other existing algorithms. This can be seen in the simulation experiments of two commercial PV models, where the simulated current is consistent with the measured current at each measured voltage. This demonstrates that the proposed TLBOBSA is an accurate and reliable tool for evaluating unknown parameters of PV models.

Operation of the Egyptian Power Grid with Maximum Penetration Level of Renewable Energies Using Corona Virus Optimization Algorithm

Countries around the world are looking forward to fully sustainable energy by the middle of the century to meet Paris climate agreement goals. This paper presents a novel algorithm to optimally operate the Egyptian grid with maximum renewable power generation, minimum voltage deviation and minimum power losses. The optimal operation is performed using Corona Virus Algorithm (CVO). The proposed CVO is compared to the Teaching and Learning-Based Optimization (TLBO) algorithm in terms of voltage deviation, power losses and share of renewable energies. The real demand, solar irradiance and wind speed in typical winter and summer days are considered. The 2020 Egyptian grid model is developed, simulated, and optimized using DIgSILENT software application. The results have proved the effectiveness of the proposed CVO, compared to the TLBO, to operate the grid with the highest share possible of renewables. The paper is a step forward to achieve Egyptian government targets to reach 20% and 42% penetration level of renewable energies by 2022 and 2035, respectively.

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections, for a traveler. With this motivation, the current research paper presents an intelligent DoS attack detection with Congestion Control (IDoS-CC) technique for VANET. The presented IDoS-CC technique involves two-stage processes namely, Teaching and Learning Based Optimization (TLBO)-based Congestion Control (TLBO-CC) and Gated Recurrent Unit (GRU)-based DoS detection (GRU-DoSD). The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network. TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization. TLBO is applied to avoid congestion on roadways. Besides, GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network. The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

Applying GA-PSO-TLBO approach to engineering optimization problems.

Under addressing global competition, manufacturing companies strive to produce better and cheaper products more quickly. For a complex production system, the design problem is intrinsically a daunting optimization task often involving multiple disciplines, nonlinear mathematical model, and computation-intensive processes during manufacturing process. Here is a reason to develop a high performance algorithm for finding an optimal solution to the engineering design and/or optimization problems. In this paper, a hybrid metaheuristic approach is proposed for solving engineering optimization problems. A genetic algorithm (GA), particle swarm optimization (PSO), and teaching and learning-based optimization (TLBO), called the GA-PSO-TLBO approach, is used and demonstrated for the proposed hybrid metaheuristic approach. Since each approach has its strengths and weaknesses, the GA-PSO-TLBO approach provides an optimal strategy that maintains the strengths as well as mitigates the weaknesses, as needed. The performance of the GA-PSO-TLBO approach is compared with those of conventional approaches such as single metaheuristic approaches (GA, PSO and TLBO) and hybrid metaheuristic approaches (GA-PSO and GA-TLBO) using various types of engineering optimization problems. An additional analysis for reinforcing the performance of the GA-PSO-TLBO approach was also carried out. Experimental results proved that the GA-PSO-TLBO approach outperforms conventional competing approaches and demonstrates high flexibility and efficiency.

An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

Sarcasm is a state of speech in which the speaker says something that is externally unfriendly with a purpose of abusing/deriding the listener and/or a third person. Since sarcasm detection is mainly based on the context of utterances or sentences, it is hard to design a model to proficiently detect sarcasm in the domain of natural language processing (NLP). Despite the fact that various methods for detecting sarcasm have been created utilizing statistical machine learning and rule-based approaches, they are unable of discerning figurative meanings of words. The models developed using deep learning approaches have shown superior performance for sarcasm detection over traditional approaches. With this motivation, this paper develops novel deep learning (DL) enabled sarcasm detection and classification (DLE-SDC) model. The DLE-SDC technique primarily involves pre-processing stage which encompasses single character removal, multispaces removal, URL removal, stop word removal, and tokenization. Next to data preprocessing, the preprocessed data is converted into the feature vector by Glove Embeddings technique. Followed by, convolutional neural network with recurrent neural network (CNN-RNN) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the CNN+RNN technique, a hyper parameter tuning process utilizing teaching and learning based optimization (TLBO) algorithm is employed in such a way that the classification performance gets increased. The DLE-SDC model is validated using the benchmark dataset and the performance is examined interms of precision, recall, accuracy, and F1-score.

Modeling of Hyperparameter Tuned Hybrid CNN and LSTM for Prediction Model

The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, noisy, and chaotic data, stock market prediction is a huge difficulty, and as a result, investors find it difficult to invest their money in order to make a profit. In order to predict stock market movements, a number of strategies have been established. Earlier studies based on statistical models and machine learning techniques have been focused on short term stock price prediction. In this aspect, this study designs a novel hyperparameter tuned hybrid convolutional neural network with long short term memory (HPT-HCLSTM) for stock price prediction. The proposed HPT-HCLSTM technique encompasses three different processes namely pre-processing, prediction, and parameter optimization. The HPT-HCLSTM technique employs the HCLSTM technique for the prediction of stock prices. In addition, teaching and learning based optimization (TLBO) algorithm is applied for the hyperparameter optimization of the HCLSTM technique and thereby results in minimal error values. In order to demonstrate the enhanced prediction performance of the HPT-HCLSTM technique, a wide range of simulations were carried out and the results highlighted the better performance of the HPT-HCLSTM technique under several aspects. The HPT-HCLSTM technique is found to be a proper tool for forecasting stock prices. the HPT-HCLSTM technique has showcased better performance with the increased R2 value of 0.9154.

Trajectory optimization of an unmanned aerial–aquatic rotorcraft navigating between air and water

Unmanned aerial–aquatic vehicles are a new type of aircraft that can navigate in air and underwater. An unmanned aerial–aquatic rotorcraft (UAAR) is introduced to complete the task of navigating between air and underwater, and the trajectory optimization problem for this task is focused on in this study. The dynamics of a four-axle rotorcraft with eight rotors operating in air and underwater is described. On this basis, the trajectory optimization model is established, wherein the constraints on control variables and states in different media are included. The optimization index is denoted as the weighted sum of the terminal states. In view of the weakness of the teaching- and learning-based optimization (TLBO) algorithm, the formula for updating the individual grade in the teaching process is modified. Thus, this ensures that the algorithm avoids converging at the local optimum and improves the solution quality. Finally, an improved TLBO (ITLBO)-based trajectory optimization method for UAAR navigating between air and water is developed. The control variables are discretized with respect to height at a set of Chebyshev collocation points to reduce the terminal error of states, and the values of control variables at other heights are obtained via interpolation. In the simulation studies, the ITLBO-based method exhibits better performance in terms of optimizing the index when compared to the other two algorithms. Furthermore, the effects of the distribution and number of collocation points on the results are analyzed.

An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security

Due to the drastic rise in multimedia content, digital images have become a major carrier of data. Generally, images are communicated or archived via wireless communication changes, and the significance of data security gets increased. In order to accomplish security, encryption is an effective technique which is used to encrypt the images using secret keys in such a way that it is not readable by the hacker. In this view, this study focuses on the design of Teaching and Learning based Optimization (TLBO) with Multi-Key Homomorphic Encryption (MHE) technique, called MHE-TLBO algorithm. The goal of the MHE-TLBO algorithm is to optimally select multiple keys using TLBO algorithm for encryption and decryption processes. In addition, the MHE-TLBO algorithm has derived a fitness function involving peak signal to noise ratio (PSNR) and thereby ensures the superior quality of the reconstructed image. For validating the security performance of the MHE-TLBO algorithm, a comprehensive result analysis is made and the simulation results ensured the betterment of the MHE-TLBO algorithm interms of different aspects.

Bus body manufacturing system via FEMA and fuzzy logic controller

Failure mode and effect analysis has been generally applied to investigate the potential failures in systems, products, etc. Here, the bus body structure fabricating process is taken to assess the potential failure of a product and its effects. The failure mode of the bus body structure is analyzed based on the “Risk Priority Number (RPN)” which is the criteria to decide the risk priorities of the failure modes. Usually, the evaluation of RPN is based on the risk factors like “Severity(S), Occurrence (O), and Detection (D)”. To improve the failure mode analysis and ranking of bus body structure, a simulation technique i.e., fuzzy logic (FL) is proposed where the optimal RPN is achieved. With the goal of optimizing the generated rules based on the failure modes, hybrid teaching and learning-based optimization (TLBO) algorithm is presented with three distinctive metaheuristics updating behaviors. The optimal outcome demonstrates that the attained error rate between the output of desired and predicted values are firmly equivalent to zero. The proposed FL with hybrid TLBO achieves promising results in terms of the failure modes determination and risk prioritization.

RETRACTED ARTICLE: An optimal artificial neural network based big data application for heart disease diagnosis and classification model

At present days, world is facing several issues like irregular distribution of medicinal resources, new chronic diseases, and the raising operating cost. The way of combining recent technologies into the medical system will helps to significantly resolve the problems. This study introduces a big health application system based on optimal artificial neural network (OANN) for heart disease diagnosis, which is considered as a deadliest disease in all over the globe. The proposed OANN includes a set of two main processes namely, distance based misclassified instance removal (DBMIR) and teaching and learning based optimization (TLBO) algorithm for ANN, called (TLBO-ANN). The proposed model is developed using a Big Data framework like Apache Spark. The presented OANN model operates on two phases, namely offline prediction and online prediction. During the offline prediction stage, the benchmark heart disease dataset will be used to train a model and performs testing. Similarly, at the online prediction stage, the real time data will be streamed into Apache Spark model and the filtered data will be diagnosed by the use of trained model to obtain the prediction results. The performance of the presented OANN model has been tested using a benchmark heart disease dataset from UCI repository. A comprehensive experimental result analysis clearly verified the better outcome of the OANN model over the compared methods. The proposed method is found to be an effective tool to analyze big data based heart disease prediction model to satisfy the need of increasing number of heart patients.

Improvement of Small Signal Stability of SMIB System with Optimized Power System Stabilizer

As electrical power system is a complex system, there are more chances of stability issues may arise. One of the stability issues is Low Frequency Oscillations (LFOs) which makes the system unstable. As these oscillations are having low frequency i.e. large time constant with slowly increasing magnitude, they are referred to small signal stability. The main reason of these oscillations is due to lack of sufficient damping torque. Automatic Voltage Regulator (AVR) action in generator is providing sufficient synchronizing torque for system stability. This is possible with high gain and low time constant AVR which results in reduction of damping torque. Power System Stabilizer (PSS) is used together with AVR for providing necessary damping torque to minimize the LFOs. For effective damping, the PSS performance is improved by optimizing its parameters. In this paper, Single Machine Infinite Bus (SMIB) system is considered for studying the effect of LFOs. The SMIB system is simulated for a step disturbance in reference voltage and the results are carried out for different optimizing techniques Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching and Learning based Optimization (TLBO).

Object detection using Metaheuristic algorithm for volley ball sports application

Object Detection has been a great challenge over the years. The reason behind is that, it is applied for numerous real time applications like vision based control, traffic control, video surveillance, sports analysis, etc. But, object detection in a video sequence is a highly challenging task. It has various problems like occlusion, fast moving objects, shadow, poor lighting, color contrast and other static background objects. This reason brought the object detection to be a thrust research area in the field of image processing. In the previous researches the conventional methods of object detection like Frame Difference, Mixture of Gaussian (MoG), Optical Flow etc., still have the above problems. Hence the research focuses on a different approach in object detection using Metaheuristic algorithm for the video sequence of volley ball player in the practice session. In this research three Metaheuristic Algorithms, namely Firefly, Teaching and Learning Based Optimization (TLBO) and Cuckoo Search Algorithm are used. These algorithms are evaluated and compared with the parameters like accuracy, precision, and recall. The result shows Cuckoo Search Algorithm is best suited to object detection especially in this application.