This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of c...This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of clustering and classification.In the first phase,the clustering(k-means and FCM)algorithms were employed independently and the clustering accuracy was evaluated using different computationalmeasures.During the second phase,the non-clustered data obtained from the first phase were preprocessed with TLBO.TLBO was performed using k-means(TLBO-KM)and FCM(TLBO-FCM)(TLBO-KM/FCM)algorithms.The objective function was determined by considering both minimization and maximization criteria.Non-clustered data obtained from the first phase were further utilized and fed as input for threshold optimization.Five benchmark datasets were considered from theUniversity of California,Irvine(UCI)Machine Learning Repository for comparative study and experimentation.These are breast cancer Wisconsin(BCW),Pima Indians Diabetes,Heart-Statlog,Hepatitis,and Cleveland Heart Disease datasets.The combined average accuracy obtained collectively is approximately 99.4%in case of TLBO-KM and 98.6%in case of TLBOFCM.This approach is also capable of finding the dominating attributes.The findings indicate that TLBO-KM/FCM,considering different computational measures,perform well on the non-clustered data where k-means and FCM,if employed independently,fail to provide significant results.Evaluating different feature sets,the TLBO-KM/FCM and SVM(GS)clearly outperformed all other classifiers in terms of sensitivity,specificity and accuracy.TLBOKM/FCM attained the highest average sensitivity(98.7%),highest average specificity(98.4%)and highest average accuracy(99.4%)for 10-fold cross validation with different test data.展开更多
The teaching–learning-based optimisation (TLBO) algorithm is a population-based metaheuristic inspired on the teaching–learning process observed in a classroom. It has been successfully used in a wide range of appli...The teaching–learning-based optimisation (TLBO) algorithm is a population-based metaheuristic inspired on the teaching–learning process observed in a classroom. It has been successfully used in a wide range of applications. In this study, the authors present a variant version of TLBO. In the proposed version, different weights are assigned to students during the student phase, with higher weights being assigned to students with better solutions. Three different approaches to assign weights are investigated. Numerical experiments with benchmark instances of the flow-shop and the job-shop scheduling problems are carried out to investigate the performance of the proposed approaches. They compare the proposed approaches with the original TLBO algorithm and with two variants of TLBOs proposed in the literature in terms of solution quality, convergence speed and simulation time. The results obtained by the application of a Friedman statistical test showed that the proposed approaches outperformed the original version of TLBO in terms of convergence, with no significant losses in the average makespan. The additional simulation time required by the proposed approaches is small. The best performance was achieved with the approach of assigning a fixed weight to half the students with the best solutions and assigning zero to other students.展开更多
The prediction of crop yield is one of the important factor and also challenging,to predict the future crop yield based on various criteria’s.Many advanced technologies are incorporated in the agricultural processes,...The prediction of crop yield is one of the important factor and also challenging,to predict the future crop yield based on various criteria’s.Many advanced technologies are incorporated in the agricultural processes,which enhances the crop yield production efficiency.The process of predicting the crop yield can be done by taking agriculture data,which helps to analyze and make important decisions before and during cultivation.This paper focuses on the prediction of crop yield,where two models of machine learning are developed for this work.One is Modified Convolutional Neural Network(MCNN),and the other model is TLBO(Teacher Learning Based Optimization)-a Genetic algorithm which reduces the input size of data.In this work,some spatial information used for analysis is the Normalized Difference Vegetation Index,Standard Precipitation Index and Vegetation Condition Index.TLBO finds some best feature value set in the data that represents the specific yield of the crop.So,these selected feature valued set is passed in the Error Back Propagation Neural Network for learning.Here,the training was done in such a way that all set of features were utilized in pair with their yield value as output.For increasing the reliability of the work whole experiment was done on a real dataset from Madhya Pradesh region of country India.The result shows that the proposed model has overcome various evaluation parameters on different scales as compared to previous approaches adopted by researchers.展开更多
Surface coating is a critical procedure in the case of maintenance engineering. Ceramic coating of the wear areas is of the best practice which substantially enhances the Mean Time between Failure (MTBF). EN24 is a co...Surface coating is a critical procedure in the case of maintenance engineering. Ceramic coating of the wear areas is of the best practice which substantially enhances the Mean Time between Failure (MTBF). EN24 is a commercial grade alloy which is used for various industrial applications like sleeves, nuts, bolts, shafts, etc. EN24 is having comparatively low corrosion resistance, and ceramic coating of the wear and corroding areas of such parts is a best followed practice which highly improves the frequent failures. The coating quality mainly depends on the coating thickness, surface roughness and coating hardness which finally decides the operability. This paper describes an experimental investigation to effectively optimize the Atmospheric Plasma Spray process input parameters of Al<sub>2</sub>O<sub>3</sub>-40% TiO<sub>2</sub> coatings to get the best quality of coating on EN24 alloy steel substrate. The experiments are conducted with an Orthogonal Array (OA) design of experiments (DoE). In the current experiment, critical input parameters are considered and some of the vital output parameters are monitored accordingly and separate mathematical models are generated using regression analysis. The Analytic Hierarchy Process (AHP) method is used to generate weights for the individual objective functions and based on that, a combined objective function is made. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is practically utilized to the combined objective function to optimize the values of input parameters to get the best output parameters. Confirmation tests are also conducted and their output results are compared with predicted values obtained through mathematical models. The dominating effects of Al<sub>2</sub>O<sub>3</sub>-40% TiO<sub>2</sub> spray parameters on output parameters: surface roughness, coating thickness and coating hardness are discussed in detail. It is concluded that the input parameters variation directly affects the characteristics of output parameters and any number of input as well as output parameters can be easily optimized using the current approach.展开更多
SS304 is a commercial grade stainless steel which is used for various engineering applications like shafts, guides, jigs, fixtures, etc. Ceramic coating of the wear areas of such parts is a regular practice which sign...SS304 is a commercial grade stainless steel which is used for various engineering applications like shafts, guides, jigs, fixtures, etc. Ceramic coating of the wear areas of such parts is a regular practice which significantly enhances the Mean Time Between Failure (MTBF). The final coating quality depends mainly on the coating thickness, surface roughness and hardness which ultimately decides the life. This paper presents an experimental study to effectively optimize the Atmospheric Plasma Spray (APS) process input parameters of Al<sub>2</sub>O<sub>3</sub>-40% TiO2 ceramic coatings to get the best quality of coating on commercial SS304 substrate. The experiments are conducted with a three-level L<sub>18</sub> Orthogonal Array (OA) Design of Experiments (DoE). Critical input parameters considered are: spray nozzle distance, substrate rotating speed, current of the arc, carrier gas flow and coating powder flow rate. The surface roughness, coating thickness and hardness are considered as the output parameters. Mathematical models are generated using regression analysis for individual output parameters. The Analytic Hierarchy Process (AHP) method is applied to generate weights for the individual objective functions and a combined objective function is generated. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is applied to the combined objective function to optimize the values of input parameters to get the best output parameters and confirmation tests are conducted based on that. The significant effects of spray parameters on surface roughness, coating thickness and coating hardness are studied in detail.展开更多
<span style="font-family:Verdana;">In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Tr...<span style="font-family:Verdana;">In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimiz</span><span style="font-family:Verdana;">ing the congestion cost, this work suggests a hybrid optimization based on</span><span style="font-family:Verdana;"> two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.</span>展开更多
文摘This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of clustering and classification.In the first phase,the clustering(k-means and FCM)algorithms were employed independently and the clustering accuracy was evaluated using different computationalmeasures.During the second phase,the non-clustered data obtained from the first phase were preprocessed with TLBO.TLBO was performed using k-means(TLBO-KM)and FCM(TLBO-FCM)(TLBO-KM/FCM)algorithms.The objective function was determined by considering both minimization and maximization criteria.Non-clustered data obtained from the first phase were further utilized and fed as input for threshold optimization.Five benchmark datasets were considered from theUniversity of California,Irvine(UCI)Machine Learning Repository for comparative study and experimentation.These are breast cancer Wisconsin(BCW),Pima Indians Diabetes,Heart-Statlog,Hepatitis,and Cleveland Heart Disease datasets.The combined average accuracy obtained collectively is approximately 99.4%in case of TLBO-KM and 98.6%in case of TLBOFCM.This approach is also capable of finding the dominating attributes.The findings indicate that TLBO-KM/FCM,considering different computational measures,perform well on the non-clustered data where k-means and FCM,if employed independently,fail to provide significant results.Evaluating different feature sets,the TLBO-KM/FCM and SVM(GS)clearly outperformed all other classifiers in terms of sensitivity,specificity and accuracy.TLBOKM/FCM attained the highest average sensitivity(98.7%),highest average specificity(98.4%)and highest average accuracy(99.4%)for 10-fold cross validation with different test data.
文摘The teaching–learning-based optimisation (TLBO) algorithm is a population-based metaheuristic inspired on the teaching–learning process observed in a classroom. It has been successfully used in a wide range of applications. In this study, the authors present a variant version of TLBO. In the proposed version, different weights are assigned to students during the student phase, with higher weights being assigned to students with better solutions. Three different approaches to assign weights are investigated. Numerical experiments with benchmark instances of the flow-shop and the job-shop scheduling problems are carried out to investigate the performance of the proposed approaches. They compare the proposed approaches with the original TLBO algorithm and with two variants of TLBOs proposed in the literature in terms of solution quality, convergence speed and simulation time. The results obtained by the application of a Friedman statistical test showed that the proposed approaches outperformed the original version of TLBO in terms of convergence, with no significant losses in the average makespan. The additional simulation time required by the proposed approaches is small. The best performance was achieved with the approach of assigning a fixed weight to half the students with the best solutions and assigning zero to other students.
文摘The prediction of crop yield is one of the important factor and also challenging,to predict the future crop yield based on various criteria’s.Many advanced technologies are incorporated in the agricultural processes,which enhances the crop yield production efficiency.The process of predicting the crop yield can be done by taking agriculture data,which helps to analyze and make important decisions before and during cultivation.This paper focuses on the prediction of crop yield,where two models of machine learning are developed for this work.One is Modified Convolutional Neural Network(MCNN),and the other model is TLBO(Teacher Learning Based Optimization)-a Genetic algorithm which reduces the input size of data.In this work,some spatial information used for analysis is the Normalized Difference Vegetation Index,Standard Precipitation Index and Vegetation Condition Index.TLBO finds some best feature value set in the data that represents the specific yield of the crop.So,these selected feature valued set is passed in the Error Back Propagation Neural Network for learning.Here,the training was done in such a way that all set of features were utilized in pair with their yield value as output.For increasing the reliability of the work whole experiment was done on a real dataset from Madhya Pradesh region of country India.The result shows that the proposed model has overcome various evaluation parameters on different scales as compared to previous approaches adopted by researchers.
文摘Surface coating is a critical procedure in the case of maintenance engineering. Ceramic coating of the wear areas is of the best practice which substantially enhances the Mean Time between Failure (MTBF). EN24 is a commercial grade alloy which is used for various industrial applications like sleeves, nuts, bolts, shafts, etc. EN24 is having comparatively low corrosion resistance, and ceramic coating of the wear and corroding areas of such parts is a best followed practice which highly improves the frequent failures. The coating quality mainly depends on the coating thickness, surface roughness and coating hardness which finally decides the operability. This paper describes an experimental investigation to effectively optimize the Atmospheric Plasma Spray process input parameters of Al<sub>2</sub>O<sub>3</sub>-40% TiO<sub>2</sub> coatings to get the best quality of coating on EN24 alloy steel substrate. The experiments are conducted with an Orthogonal Array (OA) design of experiments (DoE). In the current experiment, critical input parameters are considered and some of the vital output parameters are monitored accordingly and separate mathematical models are generated using regression analysis. The Analytic Hierarchy Process (AHP) method is used to generate weights for the individual objective functions and based on that, a combined objective function is made. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is practically utilized to the combined objective function to optimize the values of input parameters to get the best output parameters. Confirmation tests are also conducted and their output results are compared with predicted values obtained through mathematical models. The dominating effects of Al<sub>2</sub>O<sub>3</sub>-40% TiO<sub>2</sub> spray parameters on output parameters: surface roughness, coating thickness and coating hardness are discussed in detail. It is concluded that the input parameters variation directly affects the characteristics of output parameters and any number of input as well as output parameters can be easily optimized using the current approach.
文摘SS304 is a commercial grade stainless steel which is used for various engineering applications like shafts, guides, jigs, fixtures, etc. Ceramic coating of the wear areas of such parts is a regular practice which significantly enhances the Mean Time Between Failure (MTBF). The final coating quality depends mainly on the coating thickness, surface roughness and hardness which ultimately decides the life. This paper presents an experimental study to effectively optimize the Atmospheric Plasma Spray (APS) process input parameters of Al<sub>2</sub>O<sub>3</sub>-40% TiO2 ceramic coatings to get the best quality of coating on commercial SS304 substrate. The experiments are conducted with a three-level L<sub>18</sub> Orthogonal Array (OA) Design of Experiments (DoE). Critical input parameters considered are: spray nozzle distance, substrate rotating speed, current of the arc, carrier gas flow and coating powder flow rate. The surface roughness, coating thickness and hardness are considered as the output parameters. Mathematical models are generated using regression analysis for individual output parameters. The Analytic Hierarchy Process (AHP) method is applied to generate weights for the individual objective functions and a combined objective function is generated. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is applied to the combined objective function to optimize the values of input parameters to get the best output parameters and confirmation tests are conducted based on that. The significant effects of spray parameters on surface roughness, coating thickness and coating hardness are studied in detail.
文摘<span style="font-family:Verdana;">In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimiz</span><span style="font-family:Verdana;">ing the congestion cost, this work suggests a hybrid optimization based on</span><span style="font-family:Verdana;"> two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.</span>
