Teaching Learning Based Optimization Algorithm Research Articles

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Optimizing the series cascade control structure for nonminimum phase system regulation

AbstractThis work elucidates the control of integrating a nonminimum phase system via a series cascade scheme with fractional‐order P.I. (Proportional–Integral) plus D (Derivative) controller. The traditional Internal Model Control (IMC) is adopted for inner loop controller design. The feedback D controller is synthesized with the outer loop process model, showing the proposed work's universality. The outer loop controller is suggested in the IMC framework after the accountability of fractional‐filter and inverse response compensator. This combination is revealed to enhance performance without compromising robustness. The Riemann sheet principle is explored to compute the stability of the suggested controller. The sensitivity analysis has asserted the robustness. More importantly, the optimal value of controller settings is achieved via the Teaching Learning Based Optimization (TLBO) algorithm. This TLBO algorithm uses an objective function that minimizes Integral Square Error. Two illustrative problems are utilized to examine the recommended control structure's virtue.

DeepFore: A Deep Reinforcement Learning Approach for Power Forecasting in Renewable Energy Systems

An open network known as the “energy internet” links every component of the whole energy supply chains, from the generations. Due to their ability to mimic regional flow dynamics that have an impact on wind farm production, regional meteorological models are increasingly being used as a general tool for wind resource forecasting. In this study, higher vertical and horizontal resolutions WRF (weather research and forecasting) paradigm simulation are used to anticipate and validate production for a genuine onshore wind farm. This paper proposed a DeepFore which is a power forecasting system for hybrid renewable energy systems. Initially, the dataset is generated by the hybrid system. This data is preprocessed to improve the quality of the data by incorporating, filtering and outlier detection techniques. Then, this enriched data is fed into K++ means clustering algorithm to separate the normal data from faulty data. With the normal and original data, Teaching-Learning based optimization algorithm attempts to realize the optimal features which are important for forecasting. Finally, Deep SARSA which is deep reinforcement learning algorithm is incorporated to determine the power generated by the hybrid system. Better winds energy prediction estimations enable more efficient utilization of the produced electricity, according to computational models.

Optimizing SNR for indoor visible light communication by using TLBO algorithm

This article explores the selection of Communication Light-Emitting Diodes (C-LEDs) in a multiuser indoor visible light communication system. It is aimed to obtain uninterrupted and quality communication by selecting appropriate communication LEDs depending on the location of the users. In indoor visible light communication, the data from a group of C-LEDs reach the receiver through multiple transmission paths directly or as a result of reflections. Inter-symbol interference occurs due to these multiple transmission paths. As a result, the signal-to-noise ratio and the communication quality decrease. In this study, to guarantee a low bit error rate and high enough signal-to-noise ratio values for all users, a Teaching-Learning Based Optimization algorithm based on a valid rate threshold was used for selecting communication LEDs for the first time. The simulation results showed that the Teaching-Learning Based Optimization algorithm is suitable to improve the signal-to-noise ratio. Also, the time required to select the communication LEDs was shortened by determining the appropriate valid rate threshold value. When the Teaching-Learning Based Optimization algorithm was compared with other metaheuristic optimization algorithms used in the literature, it was seen that a better signal-to-noise ratio was obtained with lower iteration and population number.

Optimizing Chemical Reaction Mechanisms: Evaluating Parameter-Free Metaheuristic Algorithms and Gradient-Based Optimization

ABSTRACT In the present work, we compare the performance of four different metaheuristic algorithms in reaction mechanism optimization problems. Mechanism optimization is an essential step to develop detailed and skeletal reaction mechanisms. Stochastic metaheuristic algorithms that mimic biological evolution, such as the genetic algorithm (GA), are often used for this purpose. While this technique has proven effective over a broad class of problems, it requires the specification of many hyperparameters or user-defined inputs, such as the crossover rate, mutation rate, offspring selection criterion, among others. In the present work, we show that extensive knowledge about the underlying system is needed to ensure efficient performance from the GA. Particle swarm optimization (PSO) is another metaheuristic technique that has gained popularity in the chemical kinetics community. However, it also suffers from sensitive dependence on the input parameters. The performance of two parameter-free algorithms, the Teaching-Learning-Based Optimization (TLBO) and the Artificial Bee Colony (ABC) algorithm are compared against GA and PSO. The TLBO and ABC algorithms have not been tested previously in reaction mechanism optimization problems to the best knowledge of the authors. We show that TLBO provides comparable or superior performance than other optimization strategies in terms of fitness value, computational time, recovery of the original coefficients, and the prediction of the species mole fractions. Additionally, as the method is hyperparameter-free, the sensitive dependence on user-defined input is eliminated. In the last section of the study, the performance of a gradient-based optimization algorithm (Adam) is compared against the metaheuristic algorithms. The performance of Adam is sensitive to the learning rate, which is a hyperparameter for the algorithm. The effectiveness of TLBO method for the optimization of reaction mechanisms is established.

Boosting constrained teaching–learning based optimization algorithm through group discussion

Teaching–learning based optimization algorithm (TLBO) is an efficient population-based nature-inspired meta-heuristic designed initially to solve unconstrained optimization problems. TLBO being a swam intelligence based algorithm is renowned for fast convergence. This characteristic, however, occasionally causes it to converge too soon to a non-globally optimal solution. Group discussion techniques are used in educational institutions all over the world to help participants to exchange ideas and information and to improve their skills. This work imitates the outlined strategy, therefore innovating TLBO. Here, a group of virtual students is selected corresponding to each student in the learner phase of TLBO to produce diversity in the population and to balance the exploration and exploitation capabilities of it, hence reducing the likelihood of early convergence. GTLBO stands for the new algorithm that incorporates group discussion. It is essential to incorporate some constraint handling techniques (CHTs) into the TLBO framework in order to handle constrained optimization problems (COPs). The most recent, effective, and enhanced version of the widely used CHT stochastic ranking (SR) is called hybrid stochastic ranking (HSR). HSR is added to the GTLBO structure as a selection operator for managing COPs; the resulting setup is referred to as HSR-GTLBO. Additionally, the suggested algorithm’s group size for discussion is sensitive, resulting in a number of constrained variants. The efficacy of these variations is assessed with the suit CEC 2017’s constrained problems. In conformity with the parameters settlement, comparison, and ranking criteria of CEC 2017, the simulation results of the proposed versions are compared and ranked using the most advanced state-of-the-art algorithms. As a result, in the competition, the newly developed HSR-GTLBO variants placed first, second, and third.

Inventory model for green products with payment strategy, selling price and green level dependent demand using teaching learning based optimization algorithm

There has been a lot of research on pricing and lot-sizing practices for different payment methods; however, the majority has focused on the buyer’s perspective. While accepting buyers’ credit conditions positively impacts sales, requesting advance payments from purchasers tends to have a negative effect. Additionally, requiring a down payment has been found to generate interest revenue for the supplier without introducing default risk. However, extending the credit period, along with offering delayed payment options, has the potential to increase sales volume, albeit with an elevated risk of defaults. Taking these payment schemes into account, this study investigates and compares the per-unit profit for sellers across three distinct payment methods: advance payment, cash payment, and credit payment. The consumption rate of the product varies non-linearly not only with the time duration of different payment options but also with the price and the level of greenness of the product. The utmost objective of this work is to determine the optimal duration associated with payment schemes, selling price, green level, and replenishment period to maximize the seller’s profit. The Teaching Learning Based Optimization Algorithm (TLBOA) is applied to address and solve three numerical examples, each corresponding to a distinct scenario of the considered payment schemes. Sensitivity analyses confirm that the seller’s profit is markedly influenced by the environmental sustainability level of the product. Furthermore, the seller’s profitability is more significantly affected by the selling price index compared to the indices of the payment scheme duration and the green level in the demand structure.

A comprehensive review on the effect of turbulence promoters on heat transfer augmentation of solar air heater and the evaluation of thermo-hydraulic performance using metaheuristic optimization algorithms

AbstractSolar air heaters are characterized by poor thermal performance due to limited heat transfer capability of air, thereby necessitating the need for design modifications. Among a different system performance augmentation technique, turbulence promotors are widely used owing to its effectiveness. Based on design parameters such as geometry, size, pitch and arrangement of turbulence promoters, varying levels of heat transfer increment with the pressure drop penalty is achievable. This led to the development of new designs which could offer on optimum thermo-hydraulic performance for a wide range of Reynolds number. Such research invariably requires a thorough insight of data related to various design parameters and optimal thermal–hydraulic performance range. This article provides a detailed overview of various turbulence promotor designs and their optimal thermal–hydraulic performance ranges compiled from a wide spectrum of experimental and numerical studies. Apart from outlining the general flow characteristics of each turbulator design, this study also evaluates different metaheuristic optimization algorithm such as bonobo optimization (BO), particle swarm optimization and teaching–learning-based optimization algorithm for enhancing the thermal–hydraulic performance parameter (THPP). The study shows that the BO algorithm does not exhibit local trapping due to its self-adapting nature of the optimized parameters which makes it a promising choice for THPP optimization studies in air heater applications. The extensive review also shows that the arrangement pattern of rib turbulator plays a key role in thermo-hydraulic performance augmentation. Based on the BO optimization analysis, the range of THPP is determined for the optimized geometry of turbulence promoters. In the pool of rib design, transverse prism rib, multi-V-rib, multi-V-shaped rib with staggered rib, sinewave-shaped rib with gap and S-shaped ribs exhibits an optimal THPP range of 2.05–3.32, 2.43–2.94, 3.00–3.61, 1.58–3.40 and 2.05–3.74, respectively. Other turbulence promotor designs such as winglet vortex generator, dimple protrusion in arc shape and multi-V-baffles exhibits optimal THPP range of 1.95–2.2, 2.44–3.68 and 1.75–2.01, respectively. At the end, the study proposes key research gaps such as the use of combined ribs and vortex generators and discrete fin arrays of different geometry as future scope of research. Graphical abstract

A novel human-inspirited collectivism teaching–learning-based optimization algorithm with multi-mode group-individual cooperation strategies

A novel human-inspirited collectivism teaching–learning-based optimization algorithm with multi-mode group-individual cooperation strategies

Integration method of large-scale photovoltaic system in distribution network based on improved multi-objective TLBO algorithm

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the “teaching” process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity.

Welding investigation of Hastelloy C-276 and parametric optimization using integrated MRA-TLBO algorithm during A-TIG process

A-TIG welding is a modified form of GTAW process that has become popular due to its ability to create high-quality welded joints with minimal cracks and distortion. This technique is commonly used to join various metals, including stainless steel, aluminum, titanium, and nickel alloys like Hastelloy C-276, which is used in fabrication of complex components across marine, nuclear, aerospace, and chemical industries. In this investigation, A-TIG welding of Hastelloy C-276 was studied using three parameters - welding current, welding speed, and gas flow rate - at three distinct levels. The impact of these parameters were analyzed on two responses, namely penetration depth and width of weld. Macrostructural investigation, sensitivity analysis, and parametric studies revealed that welding current (I) had the most influence, followed by speed of welding (S) and flow rate of gas(G). Full quadratic multiple regression analysis-based models were developed for both responses, which were found to be suitable with a higher coefficient of determination and an average percentage error of 2.165% and 2.624%, respectively. Additionally, a teaching learning based optimization algorithm was integrated with these models to identify the most effective A-TIG parameter combination for higher penetration depth and lower weld width. The MRA-TLBO integrated approach resulted in an optimal parametric combination of I (170A), S (180 mm min−1), and G (11.37 l min−1) that resulted in a penetration depth and weld width of 4.503 mm and 4.684 mm, respectively. Validation at this optimal setting showed an improvement of 3.365% and 1.284% for weld width and penetration depth, respectively, suggesting the robustness of the developed methodology.

F-TLBO-ID: Fuzzy fed teaching learning based optimisation algorithm to predict the number of [formula omitted]-barriers for intrusion detection

Ensuring fast and efficient Intrusion Detection and Prevention (IDP) at international borders is crucial for maintaining security and safeguarding nations. In this study, we propose an innovative approach that harnesses the power of machine learning and Wireless Sensor Networks (WSNs) to achieve faster and more accurate IDP. Our novel Fuzzy fed Teaching Learning Based Optimisation regression algorithm (F-TLBO-ID) revolutionises the prediction of the required number of k-barriers for rapid IDP. To develop and validate our approach, we synthetically generated pertinent features using Monte-Carlo simulations. These features encompass essential parameters such as the concerned region’s area, effective transmission range, effective sensing range, number of sensor nodes, and the fading parameter. Training the F-TLBO-ID algorithm with these features yielded exceptional results, accurately predicting the required number of k-barriers with an impressive correlation coefficient (R = 0.99), minimal Root Mean Square Error (RMSE = 11.32), and negligible bias (−3.66). To benchmark the performance of our F-TLBO-ID algorithm, we conducted comprehensive comparisons with fine-tuned benchmark algorithms, including AutoML, GPR, GRNN, RF, RNN, SVM, and ANN. Additionally, we evaluated the algorithm against 11 different variants of nature-inspired algorithms. Remarkably, our F-TLBO-ID algorithm outperformed all these methods in terms of accuracy, firmly establishing its superiority. Finally, we validated the performance of the F-TLBO-ID algorithm using publicly available datasets. The results were highly satisfactory, exhibiting a strong correlation coefficient (R = 0.84), acceptable RMSE (36.24), and minimal bias (−7.17). This study offers a robust and reliable algorithm to predict the required barriers for fast IDP, surpassing the accuracy of existing benchmark algorithms. By implementing our proposed algorithm, the efficiency of IDP systems at international borders can be significantly improved, ultimately enhancing security and facilitating smooth border operations.

Optimized planning framework of solar photovoltaic based generation with EV charging station in a rural distribution network considering uncertainties

To address the adverse impacts due to rapid growth of electric vehicles (EVs), a robust planning framework is developed in this paper for optimal deployment of EV charging stations and solar energy resources in the distribution network. Uncertainty modeling of EV is done using probability density function considering stochastic parameters extracted from real National Household Travel Survey (NHTS)datasheet. Considering solar irradiance as the uncertainty parameter, a practical Photovoltaic (PV) model is developed using beta probability function. To solve the problem of optimal allocation of EV charging stations and PV in the distribution network, proposed Teaching Learning Based Optimization algorithm is used. The problem is formulated to minimize the power loss reduction index and the voltage deviation index while considering system constraints. Here this proposed approach is tested to Indian 28 bus rural distribution network and standard IEEE 69 bus system in MATLAB. Also to assess the efficiency of the proposed technique, it is compared with three different algorithms, i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) in terms of convergence characteristics and computational time. The system indices, i.e., voltage profile, line loss, voltage stability and the penetration level of EV charging station are improved after simultaneously optimally deploying EV charging station and PV units both in rural and standard 69 bus distribution networks. Different case studies were conducted and it was observed that deployment of EV charging station in the network leads to deterioration of voltage profile, voltage stability and line loss. The simulation outcome further reveals that the addition of PV panels concurrently with EV charging stations enhances the system performances and the penetration level of EV charging station in the network.

Two Feedback PID Controllers Tuned with Teaching–Learning-Based Optimization Algorithm for Ball and Beam System

The proportional integral derivative (PID) controller continues to be the most popular and widely used in the industry despite the development of various artificial intelligence-based controllers due to its simplicity and ease of use. Setting PID parameters, especially in non-linear systems, is still a significant problem. This study presents a cascade PID tuning technique based on the teaching–learning-based optimization (TLBO) algorithm. The proposed method is tested for the position control of a non-linear ball and beam system. A comparative analysis of the proposed method is done with the conventional tuning method and particle swarm optimization-tuned cascade PID controller. The optimization was carried out using integral time absolute error, integral time error, and integral square error as objective functions. It was observed that the evolutionary algorithm-based controller tuned by TLBO gives a much better response regarding rise time, settling time, and overshoot. To test the effectiveness and validity of the proposed controller robust analysis of the proposed controller is carried out with a step disturbance applied at t = 3 s. The comparative study proves that TLBO-tuned response is better than other controllers. Sensitivity analysis of the proposed controller is also performed by varying system parameters.

A Multi-UAV Indoor Air Real-Time Detection and Gas Source Localization Method Based on Improved Teaching–Learning-Based Optimization

This paper presents an improved Teaching–Learning-Based Optimization Algorithm for controlling a UAV swarm to achieve real-time monitoring of indoor air quality and possible pollution source location search. The improvements to the Teaching–Learning-Based Optimization Algorithm focus on the following four points: proposing a variable teaching factor to improve search speed and accuracy, establishing an individual capability value model to integrate real-world UAV status with the algorithm, increasing the number of teachers and providing updated formulas to improve the algorithm’s convergence speed, and proposing a grouping strategy to achieve real-time monitoring of indoor air quality and pollution source search within a unified algorithm framework. At the end of the article, possible legal challenges for deploying this algorithm are also discussed.

TLBO-trained ANN-based Shunt Active Power Filter for Mitigation of Current Harmonics

The increased utilization of nonlinear devices is resulting in damage to power distribution infrastructure by introducing harmonics into power system networks, which in turn causes distortion in voltage and current signals. A novel solution called Shunt Active Power Filter (SAPF) has been developed to address this issue using power electronics. This study aims to provide a method that is efficient and cost-effective for lowering harmonics and improving power quality in distribution infrastructure. The proposed method combines the Teaching learning-based optimization (TLBO) technique with an Artificial Neural Network Controller (TLBO-ANN) in conjunction with SAPF. The primary objective of the TLBO-ANN algorithms in SAPF is to minimise total harmonic distortion (THD) for maximum system efficiency. Initially, Gain values (Ki, Kp) for a regular Proportional-Integral controller are optimised with the Particle Swarm Optimisation (PSO) technique. Those optimized parameters obtained from the PSO-tuned PI controller serve as input and target datasets for training the ANN controller. Subsequently, the TLBO algorithm is utilized to further refine the ANN controller by finding the optimal weight and bias values. Using MATLAB/SIMULINK software, we compare the performance of the proposed algorithm to that of the PSO-tuned PI controller and traditional PI controller. The findings from the simulation suggest that a SAPF utilizing a TLBO-trained ANN controller could improve THD in the supplying current while maintaining harmonics within IEEE-519 accepting levels.

Distributed generator and capacitor‐embedded reconfiguration in three‐phase unbalanced distribution systems using teaching learning‐based optimization

AbstractIn the transmission and distribution networks, both active and reactive power play significant roles. While active power does the beneficial work, reactive power maintains the voltage that necessitates management from a system reliability perspective. The voltage variation from the nominal range may result in unintentional operation and early component failure. To maximize the amount of real power that can be transported over the power‐transmitting media, the system must also control reactive power flow. In a three‐phase unbalanced distribution system reinforced with distributed generator units (DGUs) and shunt capacitor units (SCUs), this paper suggests a teaching learning‐based optimization (TLBO) approach to be applied to combinatorial problems for simultaneous reduction of real power loss and net reactive power flow, enhance voltage stability index (VSI) and minimize aggregated voltage deviation index. A multiobjective formulation based on TLBO has been implemented to choose the ideal sizes and placements of DGUs and SCUs in large distribution networks. The case studies have been carried out on an IEEE 33‐bus standard system and a large 123‐bus unbalanced system. The analysis of the IEEE 33‐ and 123‐bus test systems reveals the economic efficiency of the suggested TLBO algorithm. Notably, by applying DG and capacitor simultaneously in the 33‐bus system, a significant reduction of 59.35%, 55.05%, and 50.76% (phases a, b, and c), and for the 123‐bus system, a significant reduction of 43.65%, 19%, and 48.65% (phases a, b, and c) in total active power losses have been achieved. The results suggest that the proposed method renders significant improvement in voltage stability and reduces power losses concerning the base case for both test systems.

RETRACTED: Optimum operation of energy hub by considering renewable resources by considering risk tolerance and risk taking with Teaching–learning-based optimization

Amidst growing concerns over climate change, the conventional energy landscape faces critical challenges associated with fossil fuel usage. In response, this study explores alternative approaches by investigating an energy hub system leveraging wind turbine-generated power. This hub integrates seamlessly with electricity and natural gas grids, offering a comprehensive energy supply encompassing electricity, natural gas, and thermal loads. The research strategy encompasses a multi-pronged approach. A Time-of-Use (TOU) load management program optimizes the distribution of electrical and thermal loads. To address the unpredictability of wind speed, the information gap decision theory (IGDT) is introduced to manage risk and deviations effectively. The central objective involves optimizing the energy hub's operations, focusing on minimizing operational costs and emissions. To address this intricate challenge, the study employs the Teaching–Learning-Based Optimization (TLBO) method. This advanced technique concurrently optimizes cost and environmental impact, exemplifying its efficacy in the energy hub context. The research culminates in demonstrating the TLBO algorithm's swift convergence, highlighting its aptitude for addressing complex cost and emission optimization dynamics. In sum, this study offers innovative insights into sustainable energy systems, where economic and environmental considerations converge harmoniously.

Advances in teaching–learning-based optimization algorithm: A comprehensive survey(ICIC2022)

Teaching-learning-based optimization (TLBO) algorithm which imitates the teaching–learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution processes as utilized by a standard evolutionary algorithm. Unlike traditional evolutionary algorithms and swarm intelligent algorithms, the iterative computation process of teaching–learning-based optimization is divided into two phases and each phase executes iterative learning operation. In this paper, we present a comprehensive survey on the recent advances in TLBO. A review of the current literature reveals intriguing challenges and suggests potential future research directions.

Enhanced Economic Load Dispatch by Teaching–Learning-Based Optimization (TLBO) on Thermal Units: A Comparative Study with Different Plug-in Electric Vehicle (PEV) Charging Strategies

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching–Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm was utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization was investigated, and different charging profiles and probability distributions were defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources were introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios.