Teaching Learning Based Optimization Method Research Articles

Utilizing the ANFIS and teaching-learning-based optimization (TLBO) methods to predict the maximum depth of scouring at culvert outlets

ABSTRACT Excessive scouring at culvert outlets is a widespread issue that often leads to culvert failure. At present, there is an absence of a universally applicable model for accurately predicting the occurrence of local scouring at culvert outlets. In this paper, a comparison is made between the performance of existing empirical equations and the results obtained from two advanced methods: adaptive neuro-fuzzy inference system (ANFIS) and the teaching-learning-based optimization (TLBO). This paper presents the development of the TLBO method, based on the ANFIS approach, for simulating scouring at culvert outlets. The algorithms for supervised training are applied by utilizing data collected from published studies. The results illustrate that the ANFIS–TLBO model successfully predicts scouring depth at culvert outlets with higher accuracy in comparison to existing empirical formulas. Furthermore, the model exhibits a wider range of applicability, accommodating diverse conditions. By integrating the ANFIS approach and TLBO optimization, the model surpasses the limitations of traditional empirical equations, providing engineers with a more reliable tool for predicting scour depth at culvert outlets. Furthermore, the proposed model has the largest accuracy compared to the previously developed empirical models. Ultimately, it can be concluded that the hybrid ANFIS–TLBO is a robust approach for scour depth prediction downstream of culverts.

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.

An improved teaching learning based optimization method to enrich the flight control of a helicopter system

An improved teaching learning based optimization method to enrich the flight control of a helicopter system

Robust optimization of the design of monopropellant propulsion control systems using an advanced teaching-learning-based optimization method

This research proposes a novel approach for the robust optimization of the design of hydrogen peroxide propulsion control systems using the efficient and advanced Teaching-Learning-Based Optimization (TLBO) method. This study adopts a robust design optimization (RDO) formulation that considers both epistemic and aleatory uncertainties, including sparse points and interval data, and uses the Johnson distribution family for uncertainty representation. The maximum likelihood estimation method is applied to determine the distribution parameters, also considering interval data with a nested optimization technique. A novel advanced TLBO method with high accuracy and convergence rate is employed to optimization of this robust design approach. The method’s originality and advancement come from two categories of modifications to the original framework: the structure of the teaching and learning phases and the initialization and search approach. The efficacy and applicability of the proposed Ad-TLBO, respectively, were evaluated using benchmark problems from the CEC2020 competition and three real-world engineering problems, with a comparison to some recently published and the CEC competition’s top-ranked algorithms. The results and statistical analyses of the Quade test, Wilcoxon signed-rank test, and Friedman test show that the proposed Ad-TLBO method outperforms the other algorithms. The proposed optimization method is eventually applied to design a monopropellant propulsion system as the control actuator of a satellite orbital transfer system. It is found that the proposed advanced TLBO is effective in handling uncertainty in real design problems and improves both the convergence rate and accuracy of the optimization process.

Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method

In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network’s operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 × 106 for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20; 16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs’ optimal placement at bus numbers 2, 18, 30; 60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm.

OPTIMAL ENERGY CONSUMPTION AND COST PERFORMANCE SOLUTION WITH DELAY CONSTRAINTS ON FOG COMPUTING

Cloud computing plays an essential role in development of the Internet of Things, which provides data processing and storage services. Fog computing is the evolution of cloud computing, which helps provide solutions to cloud computing challenges such as latency, location awareness, and real-time mobility support. Fog computing fills the gap between the cloud and IoT devices within the close vicinity of IoT devices. So, computation, networking, storage, data management, and decision making occur along the path between the cloud and IoT devices. The automatic and intelligent management of fog node resources and achieving an effective scheduling policy in the computing model is a necessary requirement and will lead to the improvement of the overall performance of fog computing. Some optimization problems are modeled by mixed-integer nonlinear programming (MINLP). In this paper, a model, i.e. an MINLP optimization problem on fog computing, is designed. Our model has two goals: to increase Cost Performance as well as to reduce energy consumption. Cost Performance is the price, users are charged as benefit/revenue. In other words Cost Performance is defined as the ratio of the average data rate of each user to its cost. Then the exact mathematical method with the GAMS program was used to prove its logical process. In the next step, we solved the model with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing+GA (SA+GA), Teaching–Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), and random method. According to the TOPSIS comparison, the SA+GA method with a value of 0.23 is the best compared to other methods. Then GWO, GA, TLBO, PSO, and GOA methods are better, respectively.

SURFACE MORPHOLOGY AND MACHINABILITY ASPECTS IN WIRE-EDM OF INCOLOY-800 SUPERALLOY: AN EXPERIMENTAL EVALUATION AND MICROSTRUCTURE ANALYSIS

This study investigated various performance measures — material removal rate (MRR), surface roughness (SR), and dimensional deviation (DD) — in wire cut electrical discharge machining of the Incoloy-800 superalloy under the effect of purposefully varied process inputs. Superalloys comprise a bright category of alloys that possess excellent mechanical strength at elevated temperatures, superior surface stability, magnificent creep resistance, and corrosion and oxidation resistance. Analysis of variance was performed to reveal influential and crucial inputs for machining measures; results were interpreted and modeled statistically. After conducting the experimental runs, the novel attempt has further been made to conduct the multi-response optimization with the desirability method and the teaching-learning-based optimization (TLBO) algorithm to develop the best process setting for optimizing all considered measures jointly. Furthermore, machined sample’s microstructure analysis was conducted with the scanning electron microscopy (SEM) analysis. The optimized parametric setting was obtained as follows: pulse on time: 2 [Formula: see text]s, pulse off time: 4 [Formula: see text]s, peak current: 2 A, and wire feed rate: 5 m/min. Obtained experimental values for MRR, SR, and DD were 0.936 mm/min, 3.797 [Formula: see text]m, and 0.084 mm, respectively, with the combined desirability value of 0.601. Optimization values of MRR, SR, and DD through the TLBO method varied from predicted values by 3.01%, 6.86%, and 6.32%, respectively.

Microlens array through induction-aided hot embossing: fabrication, optimization, and characterization

ABSTRACT In the present work, an induction-aided hot embossing (IHE) setup was developed in-house, to reduce the time required for embossing. Using the setup, a micro-lens array was successfully fabricated over PMMA substrate using laser engraved mold in less than half the time required compared to traditional hot embossing. The operating parameters were optimized through composite desirability (CD) method and teaching learning-based optimization (TLBO) to enhance the replication accuracy of the embossed microlens array. IHE experiments were designed as per rotatable central composite design (R-CCD). Embossing temperature (Te), embossing pressure, embossing time, de-embossing temperature were selected as control parameters, and deviation in diameter of the embossed microlens array was chosen as performance factor. A first-order linear model was employed for mathematical modeling. ANOVA result reveals that the developed model is adequate and Te has an impact of 44.63% on the replication accuracy. It was observed that the deviation in diameter decreases from 27.12 μm to 23.63 μm as Te increases from 120ᵒC to 130ᵒC. Final optimization result reveals that TLBO performs better than the CD. Deviation in diameter predicted by TLBO method at optimum condition is 15.83 μm which closely matches the experimental findings at optimum condition, that is, 16.57 μm. Abbreviations HE: Hot embossing; PMMA: Polymethyl methacrylate; ROC: Radius of curvature; RSM: Response surface methodology; TLBO: Teaching learning-based optimization; CD: Composite desirability; ANOVA: Analysis of variance; To: room temperature; Tg: Glass transition temperature; Te: Embossing temperature; Pe: embossing pressure; te: embossing time; Tde: deembossing temperature; tc: Cycle time; μ: Micro; Ffrictional: Frictional force; RIE: Reactive ion etching; EDM: Electric discharge machining; : sag height; IP: Input; OP: Output; IHE: Induction-aided hot embossing; CHE: Conventional hot embossing; WEDM: Wire electric discharge machining

Finite element method coupled with TLBO for shape control optimization of piezoelectric bimorph in COMSOL Multiphysics

Finite element methods (FEMs) are more advantageous for analyzing complex geometry and structures than analytical methods. Local search optimization techniques are suitable for the unimodal problem because final result depends on the starting point. On the other hand, to optimize the parameters of multi-minima/maxima problems, global optimization-based FEM is used. Unfortunately, global optimization solvers are not present in, COMSOL Multiphysics, a versatile tool for solving varieties of problems using FEM. Teaching–learning-based optimization (TLBO) is a global optimization technique and does not require any algorithm-specific parameter. In this paper, FEM is coupled with TLBO algorithms in COMSOL Multiphysics for solving the global optimization problem. The TLBO algorithm is implemented in COMSOL Multiphysics using the JAVA application programming interface and tested with the standard benchmark functions. The solutions of the standard benchmark problem in COMSOL Multiphysics are in close agreement with the results presented in literature. Furthermore, the optimization procedure thus established is used for the optimization of actuator voltage for piezoelectric bimorphs to achieve the desired shapes. The FEM-based TLBO method is compared with two optimization methods present in COMSOL Multiphysics for a shape control problem; (i) method of moving asymptotes (MMA) and (ii) Bound Optimization BY Quadratic Approximation (BOBYQA). The root mean square error shows that the FEM-based TLBO algorithm converges to a global minimum and gives the same result (19.3 nm) at multiple runs, whereas MMA and BOBYQA trapped in local minimum and gave different results for different starting points.

Development of Integrated Renewable Energy System Based on Optimal Operational Strategy and Sizing for an Un-Electrified Remote Area

Providing electricity access to remote rural areas plays a crucial role in the socio-economic development of the rural community. The use of renewable energy sources (RES) for electrification of remote rural areas has been increased in the past two decades. The integrated renewable energy system (IRES), which embeds two or more RES, is paid great attention to satisfy the energy needs of the rural areas. In the present study, the IRES model is developed using solar, micro-hydro, biomass, biogas, and wind energy sources to meet the electricity demand of the seven un-electrified clusters of hamlets of Limkheda taluka in Dahod district of Gujarat state in India. The proposed IRES model is optimized for the lowest total net present cost (TNPC) and cost of energy (COE) of the system using artificial bee colony (ABC), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) algorithms. The optimal operational strategy has been developed to minimize the operating cost of the IRES and the optimum sizing of the system. An optimal power scheduling method with a linear programming problem (LPP) approach has been carried out in the MATLAB environment. The proposed power scheduling method effectively reduces the TNPC and COE of the IRES. Finally, the results recorded in the paper confirm that the proposed configuration together with the TLBO method offers the lowest TNPC of INR 9.5951 million and TAC of INR 1.1274 million at the estimated COE of INR 5.68 per kWh, which is comparable with respect to standardized ABC and GWO.

An experimental investigation and optimization of energy consumption and surface defects in wire cut electric discharge machining

Optimizing the energy consumption and minimization of surface defects in wire cut electric discharge machining (WEDM) at the experimental design effectively saves energy and improves quality of machining. The present study is aimed to reduce energy consumption and improve machine performance related to kerf width, metal removal rate and surface quality in WEDM of Al-Si metal matrix composite. Current, pulse on time, pulse off time, voltage and wire tension are considered as controllable parameters and optimized using graph theory and utility concept (GTUC) and teaching learning based optimization (TLBO) algorithm considering criteria for the performance characteristics. It is observed that 26% of the additional energy is consumed in GTUC method while 40% less energy is consumed in TLBO method; the kerf width is found to be 8.4% more in GTUC and 2.8% less in TLBO respectively; the MRR is found to be 40.2% and 43.2% less in GTUC and TLBO methods respectively; 72.8% of the excess surface roughness is found in GTUC method while 1.2% less surface roughness is found in TLBO method. Effect of process parameters on the performance characteristics is also analyzed and the current is found to be dominant parameter. At TLBO optimized working condition, surface defects around kerf are found minimum.

A multiclass SVM classifier with teaching learning based feature subset selection for enzyme subclass classification

The functional characterization of proteins aims to understand life at macroscopic and microscopic levels, thus having wide and extensive applications in biological and pharmaceutical research. The classical methods used for the functional characterization of proteins rely on the sequence similarity approach i.e. identifying a similar protein whose functions are known. However, the performance of these methods reduces in the event of the sequences exhibiting less similarity. Hence, to tide over this impediment, many computational function prediction methods that do not use the sequence similarity approach were developed. This paper focuses on a Support Vector Machine (SVM) based method which classifies a protein at three levels. At the first level, a protein is identified and earmarked as an enzyme or non-enzyme. In the case of it being an enzyme, its functional class, and subclass are predicted in one step by taking 7 EC classes with 63 of their subclasses. Each protein is initially represented by its 32 physicochemical properties. Then a multiclass SVM (MSVM) classifier, that solves an n class classification problem with log2n binary classifiers, along with a Modified Teaching Learning Based Optimization method (MTLBO) to identify the significant features for classification, is designed to predict the functional class, and subclass of an enzyme in one step. The recall of the proposed MSVM-MTLBO using only 25 features ranges from 92.97% to 98.14% for class label prediction and 60% to 98.25% for subclass label prediction, which is significantly better than the existing methods for class and subclass classification. Moreover, the proposed method predicts the subclass labels with less number of binary classifiers than a standard multiclass SVM.

Jaya ve Öğretme-Öğrenme Tabanlı Optimizasyon Algoritmalarını Kullanarak Meteorolojik Faktörler ve Çeşitli Hava Kirleticileri ile Ozon Etkileşimlerinin Modellenmesi

Ozone (O3), nitrogen oxides (NOx) and carbon monoxide (CO) concentrations and some meteorological parameters measured hourly have been analyzed to examine the interaction patters between O3 and NOx, CO, air temperature, wind speed, relative humidity, and air pressure by taking into account the diurnal variations of them at urban site (Akçaabat ) in Trabzon. Variations of O3 levels have been modeled via Jaya and Teaching-Learning Based Optimization (TLBO) algorithms considering the effects of certain parameters (NOx and CO concentration, air temperature, wind speed, relative humidity, and air pressure) called as the independent variables. The accuracy of Jaya and TLBO methods has been determined and these methods have been carried out with four different functions: quadratic, exponential, linear and power. Some statistical indices have been applied to evaluate the performance of these models. In conclusion, it is shown that Jaya and TLBO algorithms can be used in the optimization of the regression function coefficients in modelling some air pollutants interactions and the best-fit equation for each parameter is obtained from the quadratic function.

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching–learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.

School-based optimization for performance-based optimum seismic design of steel frames

The performance-based optimum seismic design of steel frames is one of the most complicated and computationally demanding structural optimization problems. Metaheuristic optimization methods have been successfully used for solving engineering design problems over the last three decades. A very recently developed metaheuristic method called school-based optimization (SBO) will be utilized in the performance-based optimum seismic design of steel frames for the first time in this study. The SBO actually is an improved/enhanced version of teaching–learning-based optimization (TLBO), which mimics the teaching and learning process in a class where learners interact with the teacher and between themselves. Ad hoc strategies are adopted in order to minimize the computational cost of SBO results. The objective of the optimization problem is to minimize the weight of steel frames under interstory drift and strength constraints. Three steel frames previously designed by different metaheuristic methods including particle swarm optimization, improved quantum particle swarm optimization, firefly and modified firefly algorithms, teaching–learning-based optimization, and JAYA algorithm are used as benchmark optimization examples to verify the efficiency and robustness of the present SBO algorithm. Optimization results are compared with those of other state-of-the-art metaheuristic algorithms in terms of minimum structural weight, convergence speed, and several statistical parameters. Remarkably, in all test problems, SBO finds lighter designs with less computational effort than the TLBO and other methods available in metaheuristic optimization literature.

Multi-objective Optimization of Electrical Discharge Machining of Nimonic 75 using Teaching Learning Based Optimization (TLBO) Algorithm

Superalloys have been widely used in aircraft, space and power plant industry because of their high performance, resistance to corrosion and oxidation at elevated temperature. Processing of superalloys by conventional machining process becomes difficult due to high tool wear and poor quality. In present paper, machining of Nimonic 75 (Ni-based Superalloy) is carried out using Electrical Discharge Machining (EDM) process. Experiments were carried out using Taguchi’s L18 (2^1×3^5) orthogonal array. The multi-objective optimization of performance measures namely material removal rate (MRR) and surface roughness (SR) have been carried out using Teaching Learning Based Optimization (TLBO) algorithm. Initially empirical models of MRR and SR were generated using Regression Method. The optimal parametric combination for copper tool electrode as peak current 6 amp, gap voltage 50 volt, pulse on time 200 µs, pulse off time 90 µs and lift time 2 sec and for brass tool electrode as peak current 12 amp, gap voltage 40 volt, pulse on time 120 µs, pulse off time 15 µs and lift time 2 sec are recommended according to TLBO algorithm. Confirmatory tests were conducted to validate the results obtained by TLBO method.

Thyristor Controlled Series Capacitor based on TLBO

Thyristor Controlled Series Capacitor is main of the FACTS controller is an impedance compensation. The voltage power system should dependably in the reasonable range of ensure stability. A powerful technique of optimization is known as ‘Teaching–Learning-Based Optimization’. The New approach depends upon the impact of teacher on the output of learners in the class. Teaching–Learning-Based Optimization (TLBO) algorithm is contrasted with various optimization algorithms; the results explain the best performance of TLBO method. Simulations will be carried out using MATLAB programming to check the execution of Thyristor Controlled Series Capacitor.

Optimization of wear parameters using Taguchi grey relational analysis and ANN-TLBO algorithm for silicon nitride filled AA6063 matrix composites

The current research work aims to synthesis AA6063-Si3N4 composites by stir casting technique and analyzing their wear behavior using optimization techniques. The selected composition of the proposed composites are AA6063, AA6063-5 wt% Si3N4 and AA6063-10 wt% Si3N4. The Taguchi grey relational analysis (TGA) was used to analyze the wear performance of the synthesized composites. Further, using the measured experimental data, an artificial neural network (ANN) model was developed to predict the wear rate and teaching learning based optimization (TLBO) algorithm was implemented to obtain the optimized parameters value with the objective of minimizing the wear rate. In the existing work, the multiple linear regression model (MLRM) was developed using the measured values of both dependent (response) and independent (process parameter) variables and these models are used in the optimization technique to optimize the process parameters. From the TGA technique a minimum wear rate of 0.0002 mm3 min−1 was obtained for 10% wt reinforcement, 29.43N load, 3 m s−1 is sliding velocity and 1500 mm sliding distance and from the ANN model and TLBO algorithm, it is probable to attain 0.000183 mm3 min−1 wear rate for the process parameter values of 9.892% of wt reinforcement, 9.837N of load, 2.936 m s−1 sliding velocity and 1130.7 mm sliding distance. As compared to TGA method, an amount of 8.4% improvement in wear rate is achieved by implementing ANN–TLBO method.

Multi-objective optimization of roundness, cylindricity and areal surface roughness of Inconel 825 using TLBO method in wire electrical discharge turning (WEDT) process

Wire electrical discharge turning (WEDT) is a non-conventional machining process, which was developed to machine cylindrical components on difficult-to-machine, but electrically conductive materials with high precision. In the present work, roundness, cylindricity and areal surface roughness of the parts created by WEDT process are investigated. Inconel 825 was the material chosen for the present study. The main objective of this study is to identify the optimum process parameters required to obtain improved geometry of the turned components with respect to roundness, cylindricity and areal surface roughness. The effects of machining parameters in WEDT process on the output responses are studied using experiments based on Box–Behnken design. ANOVA has been performed to study the effect of process parameters on the output responses. A novel optimization approach is used to identify the optimum parameters for each response. Teaching learning-based optimization (TLBO) is used to optimize the process, considering the three output responses in order to improve the quality of the turned products. Validation experiments were conducted, and the predicted results were verified. It is observed that the roundness, cylindricity and areal surface roughness of components machined with WEDT process obtained from TLBO technique and validation experiments are in close agreement. The surface morphology of the samples having maximum and minimum Sa value was studied using SEM micrographs. Finally, a microelectrode of 185 µm diameter and 4 mm length was successfully fabricated with multiple cut strategy using the WEDT setup in order to demonstrate the process capability.

A novel hybrid method based on teaching–learning algorithm and leader progression model for evaluating the lightning performance of launch sites and experimental tests

In this article, a numerical optimization method has been used to study the lightning effect on satellite launch sites. In the conventional direct method, the leader progression model along with charge simulation method is employed for all the lightning leader tip positions and all the possible lightning current values on the equipment. This time-consuming calculation set is reduced in the developed method by using an optimization algorithm. For this purpose, the specifications of a real air termination system have been used for simulation and a system in a laboratory scales has been used for both purposes of simulation and practical tests. Teaching–learning-based optimization (TLBO) algorithm has been employed as an intelligent method to find the number of lightning strikes to the equipment, to detect critical areas on the structure surface and to reduce the execution time. Also, some experimental tests conducted in a high-voltage laboratory are referred on the small-scale system to investigate the conditions of leaders inception and propagation. An appropriate adaptation between the simulation results of TLBO and conventional direct method verifies the application of this method to investigate the performance of other air termination systems.