Teaching Learning Based Optimization Technique Research Articles

Optimization of Cost-Based Hybrid Flowshop Scheduling Using Teaching-Learning-Based Optimization Algorithm

A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.

Modeling of flexible AC transmission system devices and fuzzy controller for automatic generation control of electric vehicle-injected power system

The automatic generation control (AGC) is a paradigm topic of interest that is continuously evolving for adequate power generation control in the power system. In this proposed study, a fuzzy adapted PID (FPID) controller is assigned for the AGC of the hybrid power system in various electrical risks and hazards. The hybrid power system includes traditional thermal and hydroelectric facilities along with gas and nuclear power plants. Two FACTS components, the superconducting magnetic energy storage (SMES) and the interline power flow controller (IPFC), have been included in to the system to improve stability of the system. The proposed FPID controller is developed to its full potential using the newly developed teaching learning-based optimization (TLBO) method. The TLBO technique's capability has been compared against a few common evolutionary strategies in order to demonstrate its superiority. It also shows how FPID controllers outperform traditional PID controllers. Investigations are also done on how IPFC and SMES might improve system stability performance. Owing to the outcome study, it is revealed that suggested TLBO-FPID, IPFC and SMES are most effective tool to advance the AGC mechanism. It is revealed from the outcomes that anticipated FPID controller reduces the settling time of area1 frequency (ΔF1) by 135.72 %, 428.58 % and 471.42 % over TLBO;PID, DE;PID and PSO;PI respectively. Finally, the proposed mechanisms are discussed with employing in electric vehicle integrated three-area distributed energy source-based power network to justify the efficacy of the proposed approaches.

Mathematical Modelling Of Municipal Solid Waste Management In Spherical Fuzzy Environment

A Spherical fuzzy model induced with teaching learning based optimization technique is developed for supporting the municipal solid waste management under fuzzy environment. Spherical fuzzy set’s ability to capture imprecise and contradictory information results in a substantial contribution to decision-making issues. Thus, we introduce SFLPP in a spherical fuzzy environ-ment in this article, which entails maximization of truthiness and minimization of indeterminacy and falsity membership functions.In present era TLBO is gaining the popularity of being less complex and only two algorithmic parameters based algorithm. This study introduced a mathematical model to include all of the major components of municipal solid waste management. To deal with uncertainty, the mathematical model of municipal solid waste management is defined using a spherical fuzzy environment.The goal of this research is to determine the current state of waste management in the Dinanagar area of Punjab, India. Finally,the mathematical model is in possession of long-term waste management in the study area, Dinanagar city in Punjab, India. The findings of comparing the suggested model to the current framework show that the new model provides better solutions in terms of sustainability.

Evaluation of response characteristics using sensitivity analysis and TLBO technique of powder mixed wire EDM of Ti6Al4V alloy

The development of Powder Mixed Wire Electric Discharge Machining (PMWEDM) aims to enhance both precise dimension and surface quality, making it a more efficient method for the cost-effective production of precise dies and tools compared to conventional Wire Electric Discharge Machining (WEDM. This approach effectively eliminates the need for secondary operations. The present study is focused on the parametric influence of surfactant with powder mixed WEDM process parameters during die corner cutting on Ti6Al4V material in the presence of surfactant and powder with dielectric. A sensitivity analysis was conducted to determine the significant influence of machining parameters and powder properties in conjunction with the dielectric. To empirically explore these variables, a central composite full factorial design based on response surface methodology was utilized. The design involved varying the pulse on-time (Ton) within the range of 30–90 µs, pulse off-time (Toff) within 3–11 µs, gap voltage (GV) within 40–80 V, and powder concentration (PC) within 2–10 g/L. The mathematical model was developed to predict the responses such as corner inaccuracy (CI) and surface roughness (SR) using RSM. The results conclude that the sensitiveness of powder concentration is highest. Apart from this, positive sensitiveness towards surface roughness is determined for three inputs: pulse on time, pulse off time, and powder concentration. In comparison, powder concentration shows positive sensitiveness towards corner inaccuracy. An artificial intelligence technique namely, Teaching learning-based optimization (TLBO) algorithm has been used to determine the best output. The minimum output for both responses, i.e. corner inaccuracy of 12982.67 µm2 and surface roughness of 1.199 µm, was obtained using TLBO technique. Further improvement for surface finish and corner accuracy in addition powder (3 g/L) with dielectric in WEDM process is found to be a value of 50.77% and 23.01%, respectively, compared with conventional WEDM process. SEM was employed to observe and analyze the topographical changes that occurred during powder mixed and without powder mixed in WEDM process.

Performance evaluation and extraction of global power maxima under shading scenarios and load Variation: An experimental study

Due to environmental degradation, depletion of fossil resources and contaminants in the air, energy structure reform and development of sustainable environment friendly are approaching in the past few years, in which several renewable energy technologies such as solar, wind, bio and hydropower as efficient alternative energy have drawn considerable attention. Solar Photovoltaic (SPV) systems, in particular, have huge benefits for society and the economy as an essential means of extracting and utilization. However, these systems suffer from extracting maximum power from variable irradiance due to shading scenarios and variable load. As a result of PSCs, many LMPPs and GMPPs exist on their power voltage (P-V) characteristics. Thus, MPTT techniques are implemented in these systems to resolve these issues. Several researchers have conducted substantial research and discussion on these techniques to improve SPV system performances, but the practical applicability of MPPT approaches faces multiple obstacles. This study evaluates the performances of three such MPPT metaheuristic approaches, i.e., umbrella optimization technique (UOT), particle swarm optimization (PSO) and teaching learning-based optimization (TLBO) experimentally in real-time environment on small hardware under PSCs and load variation scenarios. Each technique is tested on a standalone SPV system incorporating an Inverse Single-Ended Primary Inductor Converter (SEPIC) and Luo DC-DC converter separately on various parameters of SPV systems such as tracking time, tracking efficiency, output power, output current and computational complexity, etc. With inverse SEPIC DC converter, UOT boosts GMPP up to 3.8% to 3.9% while the tracking time is reduced or faster by 20.11% to 27.7% compared to PSO & TLBO techniques. It also shows 98.6% average tracking efficiency under considered PSCs. On the other hand, UOT achieves 4.22% to 5.5% higher GMPP and tracking time is reduced by 38.38% to 57.87% with the Luo DC converter compared to PSO & TLBO techniques. UOT shows 98.19% average tracking efficiency under considered PSCs with this converter. Moreover, UOT can also maintain 3.80% to 6.48% high output current in the SPV system under varying load conditions, with stabilization time reduced to 34.06% to 35.80% compared to PSO & TLBO techniques. The further practical applicability of the study is tested on a 300 W standalone SPV system. On this system, UOT again shows its better tracking performance by boosting 3.5% to 3.64% GMPP as well as the system output current. It also takes 30.86% less tracking time with 98.56% average tracking efficiency compared to PSO and TLBO. Experimental validation of all three metaheuristic approaches on low and high-rated systems confirms the supremacy of UOT over PSO & TLBO techniques in terms of tracking time, tracking efficiency, output power and current under different working scenarios.

Techno-Economic and Environmental Based Approach for Planning of SDG and DSTATCOM with Impact of Network Reconfiguration using APSO and TLBO

A Multi Objective based Fitness Function (MOFF) is proposed for the optimum planning of multiple Solar Distributed Generation (SDG) and DSTATCOM with radial distribution network (RDN) reconfiguration impact for techno-economic and environmental benefit improvement. The Adaptive-Particle Swarm Optimization (APSO) and Teaching-Learning Based Optimization techniques (TLBO) are employed to accomplish this work. In the proposed MOFF, the Active Power Loss (APLoss), Reactive Power Loss (RPLoss), System Voltage Deviation (SVD), Fault-Current Level-of-Line (FCLLine), and System Service Reliability (SSR) are considered. The economic-benefit measures along with Environmental Emissions Components (EEC) impact have also been considered in light of various system costs such as Fixed Capital Recovery Cost (FCRCost), Energy Loss Cost (ELCost) and Energy Not Supplied Cost (ENSCost). The novelty in the MOFF is the simultaneous consideration of FCLLine with APLoss, RPLoss, SVD, and SSR along with EEC impact calculation. The IEEE 69 and 118 bus RDN is considered with three case studies to demonstrate the proposed methodology's usefulness. The result analysis reveals that better performances can be obtained based on the considered MOFF in terms of environment-friendly techno-economic perspective, consistency, convergence, and computation time using TLBO rather than APSO.

Prediction of rock drillability using gray wolf optimization and teaching–learning-based optimization techniques

Prediction of rock drillability using gray wolf optimization and teaching–learning-based optimization techniques

Processing of Triangular Profile on Inconel-825 Superalloy by Wire Electrical Discharge Machining and Corner Error Optimization Using Teaching Learning-Based Optimization Technique

Processing of Triangular Profile on Inconel-825 Superalloy by Wire Electrical Discharge Machining and Corner Error Optimization Using Teaching Learning-Based Optimization Technique

Terminal voltage prediction of Li-Ion batteries using Combined Neural Network and Teaching Learning Based Optimization algorithm

The static and dynamic model parameters are critical parameters for the accurate estimation of open-circuit voltage and the terminal voltage of a Lithium-Ion (Li-Ion) battery. This work mainly focuses on the investigation of an accurate method for predicting the open-circuit voltage and terminal voltage of Li-Ion batteries. This work proposes an Enhanced Neural Network and Teaching Learning-Based Optimization (ENN-TLBO) algorithm to predict the terminal voltage of a Li-Ion battery used in electric vehicle applications. In this work, the static model is obtained by using a neural network (NN) while the dynamic model parameters are predicted by using the Teaching Learning Based Optimization technique (TLBO). In the NN method, different network parameters are varied to predict the accurate State of Charge (SoC)-Open Circuit Voltage (OCV) relation of the battery. The proposed static models are validated by the performance metrics such as RMSE and R2, the best static model is identified by low Root Mean Square Error (RMSE) and high R2 value of the model. In recent times many optimization algorithms are reported in the literature to predict the dynamic model, which requires certain algorithm-specific tuning parameters that affects the performance of the algorithm. The proposed TLBO algorithm is implemented with lesser tuning parameters to predict the hysteresis constant of the dynamic model. The RMSE value of the predicted terminal voltage at different temperature profiles is calculated to validate the proposed method. The proposed method has several advantages such as fewer tuning parameters, simple to implement, and high accuracy.

Enhanced control of doubly fed induction generator based wind turbine system using soft computing assisted fractional order controller

This paper proposes teaching learning based optimization (TLBO) algorithm assisted fractional order controller for control of Rotor side converter (RSC) and Grid side converter (GSC) for efficient and optimal control of doubly fed induction machine based wind turbine (WT) system. The proposed fractional order proportional integral (FOPI) controller is tuned using Genetic algorithm (GA), particle swarm optimization (PSO) and TLBO techniques. The rotating reference frame with stator flux alignment dependent field oriented vector control scheme is accomplished for decoupled optimal power control. Proposed DFIG based WT system implemented and simulated using MATLAB/Simulink. Simulation results of proposed TLBO based fractional order control are compared with GA and PSO tuned FOPI controller. The simulation results obtained are expedient that TLBO tuned FOPI is more prominent in DC link voltage control and shown promising results as compared to GA and PSO tuned FOPI Controller in terms of the of the solution time required and time domain performance parameters.

Experimental Investigations and Effect of Nano-Powder-Mixed EDM Variables on Performance Measures of Nitinol SMA.

In the present study, the effect of alumina (Al2O3) nano-powder was investigated for the electrical discharge machining (EDM) of a Nitinol shape memory alloy (SMA). In addition to the nano-powder concentration, other parameters of pulse-on-time (Ton), pulse-off-time (Toff), and current were selected for the performance measures of the material removal rate (MRR), surface roughness (SR), and tool wear rate (TWR) of Nitinol SMA. The significance of the design variables on all the output measures was analyzed through an analysis of variance (ANOVA). The regression model term has significantly impacted the developed model terms for all the selected measures. In the case of individual variables, Al2O3 powder concentration (PC), Toff, and Ton had significantly impacted MRR, TWR, and SR measures, respectively. The influence of EDM variables were studied through main effect plots. The teaching–learning-based optimization (TLBO) technique was implemented to find an optimal parametric setting for attaining the desired levels of all the performance measures. Pursuant to this, the optimal parametric settings of current at 24 A, PC at 4 g/L, Toff at 10 µs, and Ton of 4 µs have shown optimal input parameters of 43.57 mg/min for MRR, 6.478 mg/min for TWR, and 3.73 µm for SR. These results from the TLBO technique were validated by performing the experiments at the optimal parametric settings of the EDM process. By considering the different user and application requirements, 40 Pareto points with unique solutions were generated. Lastly, scanning electron microscopy (SEM) performed the machined surface analysis. The authors consider this to be very beneficial in the nano-powder-mixed EDM process for appropriate manufacturing operations.

Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique

Nowadays, wind energy is receiving considerable attention due to its availability, low cost, and environment-friendly operation. Wind turbines are rarely placed individually but rather in the form of a wind farm with a group of several wind turbines. The purpose of this research is to perform studies on wind turbine farms in order to find the best distribution for wind turbines that maximizes the produced power, hence minimizing the wind farm area. Wind Farm Area Optimization (WFAO) is performed for optimal placement of wind turbines using elitist teaching–learning-based optimization (ETLBO) techniques. Three different scenarios of wind (first is fixed wind direction and constant speed, second is variable wind direction and constant speed, and third is variable wind direction and variable speed) are considered to find the optimal number of turbines and turbine positioning in a minimized squared land area that maximizes the power production while minimizing the total cost. Other research carried out in the past was to find the optimal placement of the wind turbines in a fixed squared land area of 2 km×2 km. In the present study, WFAO–ETLBO algorithm has been implemented to get the optimal land area for the placement of the same number of turbines used in the past research. For Case 1, there is a significant reduction in land area by approximately 30.75%, 45.25%, and 51.75% for each wind scenario, respectively. For Case 2, the reductions in land area for three different wind scenarios are respectively 30.75%, 7.2%, and 7.2%. For Case 3, there is a reduction of 7.2% in land area for each wind scenario. It has been observed that the results obtained by the WFAO–ETLBO algorithm with a significant reduction in the land area along with optimal placement of wind turbines are better than the results obtained from the wind turbines placement in the fixed land area of 2 km×2 km.

Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines

In this paper, the meta-heuristic multi-objective elitist teaching–learning based optimization technique is implemented for wind farm layout discrete optimization problem. The optimization of wind farm layout addresses the optimum siting among the wind turbines within the wind farm to accomplish economical, profitable, and technical features. The presented methodology is implemented with multi-objective optimization problem through different targets such as minimizing cost, power output maximization, and the saving of the number of turbines. These targets are investigated with some case studies of multi-objective optimization problems in three scenarios of wind (Scenario-I: fixed wind direction and constant speed, Scenario-II: variable wind direction and constant speed, and Scenario-III: variable wind direction and variable speed) for the optimal micro-siting of wind turbines in a given land area that maximizes the power production while minimizing the total cost. To check the effectiveness of the algorithm, firstly, the results obtained for the three different scenarios have been compared with past studies available in the literature. Secondly, the numbers of turbines have also been optimized by using teaching–learning based optimization. It has been observed that the proposed algorithm shows the optimal layouts along with the optimal number of turbines with minimum fitness evaluation. Finally, the concept of elitism has been introduced in the teaching–learning based optimization algorithm. It is proposed that if elitist-teaching–learning based optimization with elite size of 15% is used, computational expense can be significantly reduced. It can be concluded that that the results obtained by the proposed algorithm are more accurate and advantageous than others.

An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection

An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection

Optimization of structural health monitoring attributes under variable failure rate condition using teaching learning based optimization and multiple regression

The manufacturing industries prefer to use exponential equations for evaluating reliability, failure rate, etc., while assuming failure rate constant, but in real life failure rate is not always constant. This research work proposes a novel combination of Multiple Regression and Teaching Learning Based Optimization (TLBO). This work starts with regression modeling, to form an objective function from available data; secondly TLBO technique used, to obtain optimum values of structural health monitoring (SHM) attributes through cost minimization and finally, Weibull analysis to check the effect of optimum values of SHM attributes on β. The final results indicated that the β parameter reduces, which symbolized reduction in failure rate & SHM cost and, improvement in system reliability, thus validates the adopted methodology of combing multiple regression with TLBO, thus success of this work become the backbone for intelligent structures for SHMsystem.

Solving realistic reactive power market clearing problem of wind-thermal power system with system security

Abstract This paper proposes a security-constrained single and multi-objective optimization (MOO) based realistic security constrained-reactive power market clearing (SC-RPMC) mechanism in a hybrid power system by integrating the wind energy generators (WEGs) along with traditional thermal generating stations. Pre-contingency and post-contingency reactive power price clearing plans are developed. Different objective functions considered are the reactive power cost (RPC) minimization, voltage stability enhancement index (VSEI) minimization, system loss minimization (SLM), and the amount of load served maximization (LSM). These objectives of the SC-RPMC problem are solved in a single objective as well as multi-objective manner. The choice of objective functions for the MOO model depends on the load model and the operating condition of the system. For example, the SLM is an important objective function for the constant power load model, whereas the LSM is for the voltage-dependent/variable load model. The VSEI objective should be used only in near-critical loading conditions. The SLM/LSM objective is for all other operating conditions. The reason for using multiple objectives instead of a single objective and the rationale for the choice of the appropriate objectives for a given situation is explained. In this work, the teaching learning-based optimization (TLBO) algorithm is used for solving the proposed single objective-based SC-RPMC problem, and a non-dominated sorting-based TLBO technique is used for solving the multi-objective-based SC-RPMC problem. The fuzzy decision-making approach is applied for extracting the best-compromised solution. The validity and efficiency of the proposed market-clearing approach have been tested on IEEE 30 bus network.

Modeling and optimization of dead metal zone to reduce cutting forces in micro-milling of hardened AISI D2 steel

Improving machining performance with reduced power consumption is a big challenge for the manufacturer to reduce production cost. Since the dead metal zone (DMZ) directly affects the cutting forces, the present study aims to optimize the DMZ to reduce the cutting and thrust forces in the micro-milling of hardened AISI D2 steel using teaching–learning-based optimization technique (TLBO). Finite element model for DMZ geometry and mechanistic models for cutting and thrust forces are developed, integrated and estimated the cutting and thrust forces. The estimated forces are compared with experimental results and a good agreement found between them. In the next stage, process parameters (cutting speed and feed per tooth) and tool parameters (nose radius and rake angle) are optimized using TLBO technique to minimize DMZ geometry keeping the surface roughness (≤ 2 µm), tool wear (≤ 30 µm) and amplitude of cutter vibration (≤ 30 µm) as constraints. The optimal working condition is as follows: a spindle speed of 2225 rpm, a feed per tooth of 5.0 µm, and a nose radius of 7.6 µm and rake angle of 3.0°. Under the optimal working condition, side length of DMZ and DMZ angle is found as 13.8 mm and 5.74°, respectively, and the cutting and thrust forces are estimated as 3.27 and 2.37 N, respectively. These cutting and thrust forces are about 21.3–65.7 and 34.8–55.3%, respectively, less than the experimental results.

Reservoir bank slope stability prediction model based on BP neural network

Safety monitoring and stability analysis of high slopes are essential for construction of concrete dam in precipitous gorges or mountainous areas. The estimate of slope stability is a difficult engineering shortcoming with a number of variables. Thereafter, a hybrid model of Support Vector Regression (SVR) and Teaching–learning-based optimization technique (TLBO) is proposed to develop the predicting function. TLBO was used in obtaining the best SRV factors to improve the prediction accuracy. Few essential factors, such as the installation height of instruments, classification of rock masses, modulus of elasticity, the complete measuring time cycle, the excavation height of slope, the start measuring time, and the actual excavation height after measurement are considered as the input parameter, but the slope displacement is regarded as output. The outcomes showed SRV-TLBO a reliable hybrid accurate prediction of slope stability, then it was effectively used to the left abutment slope of Jinping I hydropower station located in Yalongjiang concrete dam reservoir as a novel method for this purpose.

Optimization of Wind Farm Area and Micro-Siting of Wind Turbines Using Teaching-Learning Based Optimization Technique

Optimization of Wind Farm Area and Micro-Siting of Wind Turbines Using Teaching-Learning Based Optimization Technique

Interactive teaching learning based optimization technique for multiple object tracking

In this paper, an Interactive Teaching Learning Based Optimization (In-TLBO) algorithm is proposed for tracking multiple objects with several challenges. The performance of the four other competitive approaches, such as the Mean Shift (MS), Particle Swarm Optimization (PSO), Sequential PSO (SPSO), and Adaptive Gaussian Particle Swarm Optimization (AGPSO) are investigated for comparison. The quantitative and qualitative analyses of these approaches have been performed to demonstrate their efficacy. The comparison of various performance measures includes the convergence rate, tracking accuracy, Mean Square Error (MSE) and coverage test. To assess the dominance of the proposed In-TLBO approach, Sign and Wilcoxon test are also performed. These two non-parametric tests reveal considerable advancement of the proposed Interactive TLBO (In-TLBO) over other four competitive approaches. In-TLBO shows significant improvement over the MS and PSO algorithms with a level of significance α = 0.05, and over SPSO, with a level of significance α = 0.1 by considering detection rate as winning parameter. The analyses of comparative results demonstrate that the proposed approach effectively tracks similar objects in the presence of many real time challenges.