Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, impr...Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.展开更多
广域电力系统稳定器(Wide Area Power System Stabilizer,WAPSS)对电力系统的区间低频振荡能够起到良好的阻尼作用。同时,WAPSS参数的协调优化设计能够避免因增大某一振荡模式的阻尼而造成其他模式阻尼恶化的问题,提出一种两阶段设计的W...广域电力系统稳定器(Wide Area Power System Stabilizer,WAPSS)对电力系统的区间低频振荡能够起到良好的阻尼作用。同时,WAPSS参数的协调优化设计能够避免因增大某一振荡模式的阻尼而造成其他模式阻尼恶化的问题,提出一种两阶段设计的WAPSS参数协调优化方法。第一阶段基于留数相位补偿原理设计WAPSS超前滞后环节的参数。第二阶段,将整定后的超前滞后环节参数代入WAPSS传递函数以减少决策变量,再以提高低频振荡模式和近虚轴模式的阻尼为多优化目标,利用基于精英替换策略的改进教与学算法(Teaching-Learning-Based Optimization,TLBO)对WAPSS的增益参数进行优化。通过将超前滞后环节参数和增益参数两阶段协调优化,不仅减少了每次迭代计算时间,而且达到了提高电力系统阻尼的目的。最后通过两区四机的仿真算例验证了该方法的有效性。展开更多
Purpose Load frequency control(LFC)in today’s modern power system is getting complex,due to intermittency in the output power of renewable energy sources along with substantial changes in the system parameters and lo...Purpose Load frequency control(LFC)in today’s modern power system is getting complex,due to intermittency in the output power of renewable energy sources along with substantial changes in the system parameters and loads.To address this problem,this paper proposes an adaptive fractional order(FO)-fuzzy-PID controller for LFC of a renewable penetrated power system.Design/methodology/approach To examine the performance of the proposed adaptive FO-fuzzy-PID controller,four different types of controllers that includes optimal proportional-integral-derivative(PID)controller,optimal fractional order(FO)-PID controller,optimal fuzzy PID controller,optimal FO-fuzzy PID controller are compared with the proposed approach.The dynamic response of the system relies upon the parameters of these controllers,which are optimized by using teaching-learning based optimization(TLBO)algorithm.The simulations are carried out using MATLAB/SIMULINK software.Findings The simulation outcomes reveal the supremacy of the proposed approach in dynamic performance improvement(in terms of settling time,overshoot and error reduction)over other controllers in the literature under different scenarios.Originality/value In this paper,an adaptive FO-fuzzy-PID controller is proposed for LFC of a renewable penetrated power system.The main contribution of this work is,a maiden application has been made to tune all the possible parameters of fuzzy controller and FO-PID controller simultaneously to handle the uncertainties caused by renewable sources,load and parametric variations.展开更多
Finding the optimal number of clusters has remained to be a challenging problem in data mining research community. Several approaches have been suggested which include evolutionary computation techniques like genetic ...Finding the optimal number of clusters has remained to be a challenging problem in data mining research community. Several approaches have been suggested which include evolutionary computation techniques like genetic algorithm, particle swarm optimization, differential evolution etc. for addressing this issue. Many variants of the hybridization of these approaches also have been tried by researchers. However, the number of optimal clusters and the computational efficiency has still remained open for further research. In this paper, a new optimization technique known as “Teaching-Learning-Based Optimization” (TLBO) is implemented for automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified rather it determines the optimal number of partitions of the data “on the run”. The new AUTO-TLBO algorithms are evaluated on benchmark datasets (collected from UCI machine repository) and performance comparisons are made with some well-known clustering algorithms. Results show that AUTO-TLBO clustering techniques have much potential in terms of comparative results and time of computations.展开更多
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
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
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
In this paper,a multistring-multilevel inverter(M-MLI)for renewable-energy-source applications has been proposed with reduced switch count and harmonics along with single-switch fault analysis for various levels.It re...In this paper,a multistring-multilevel inverter(M-MLI)for renewable-energy-source applications has been proposed with reduced switch count and harmonics along with single-switch fault analysis for various levels.It requires only‘m+1’power switches for‘m’voltage levels.The proposed work achieves the fine-tuning of switching angles using a metaheuristic technique,i.e.the teaching-learning-based optimization algorithm(TLBOA),to mitigate the total harmonic distortion(THD)of the M-MLI.Furthermore,the proposed TLBOA has been compared with conventional modulation techniques such as equal phase(EP),half-equal phase(HEP),near-level control(NLC)and Newton-Raphson(NR)to verify the effectiveness of TLBOA for various voltage levels in terms of%voltage-THD(%V-THD),computational time and methodology.By fine-tuning the switching angles,the%V-THD is improved significantly when compared with EP,HEP,NLC and NR modulation techniques.For an 11-level single-phase M-MLI,the%V-THD using TLBOA at 0.91 modulation index(MI)is 5.051%.The lower-order harmonics,i.e.5,7,11 and 13,are eliminated to improve the power quality.Furthermore,MLIs are often prone to failure,resulting in waveform distortion.The extreme reduction in power quality impacts the load and significant damage is likely.The location of the open-circuit fault to be identified becomes more tedious under the faulty conditions with increased switch counts and voltage levels since the mathematical modelling fails to address the scenario in less computational time.Hence,the machine-learning approach,i.e.support vector machine(SVM)with Bayesian optimization,has been discussed to locate the faulty switch.Finally,the proposed M-MLI configuration has been modelled,simulated and validated using MATLAB®and Simulink®.The results of the M-MLI configuration have been verified for 7,9 and 11 levels using TLBOA along with fault analysis using the SVM approach.展开更多
Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest can...Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.展开更多
文摘Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.
文摘广域电力系统稳定器(Wide Area Power System Stabilizer,WAPSS)对电力系统的区间低频振荡能够起到良好的阻尼作用。同时,WAPSS参数的协调优化设计能够避免因增大某一振荡模式的阻尼而造成其他模式阻尼恶化的问题,提出一种两阶段设计的WAPSS参数协调优化方法。第一阶段基于留数相位补偿原理设计WAPSS超前滞后环节的参数。第二阶段,将整定后的超前滞后环节参数代入WAPSS传递函数以减少决策变量,再以提高低频振荡模式和近虚轴模式的阻尼为多优化目标,利用基于精英替换策略的改进教与学算法(Teaching-Learning-Based Optimization,TLBO)对WAPSS的增益参数进行优化。通过将超前滞后环节参数和增益参数两阶段协调优化,不仅减少了每次迭代计算时间,而且达到了提高电力系统阻尼的目的。最后通过两区四机的仿真算例验证了该方法的有效性。
文摘Purpose Load frequency control(LFC)in today’s modern power system is getting complex,due to intermittency in the output power of renewable energy sources along with substantial changes in the system parameters and loads.To address this problem,this paper proposes an adaptive fractional order(FO)-fuzzy-PID controller for LFC of a renewable penetrated power system.Design/methodology/approach To examine the performance of the proposed adaptive FO-fuzzy-PID controller,four different types of controllers that includes optimal proportional-integral-derivative(PID)controller,optimal fractional order(FO)-PID controller,optimal fuzzy PID controller,optimal FO-fuzzy PID controller are compared with the proposed approach.The dynamic response of the system relies upon the parameters of these controllers,which are optimized by using teaching-learning based optimization(TLBO)algorithm.The simulations are carried out using MATLAB/SIMULINK software.Findings The simulation outcomes reveal the supremacy of the proposed approach in dynamic performance improvement(in terms of settling time,overshoot and error reduction)over other controllers in the literature under different scenarios.Originality/value In this paper,an adaptive FO-fuzzy-PID controller is proposed for LFC of a renewable penetrated power system.The main contribution of this work is,a maiden application has been made to tune all the possible parameters of fuzzy controller and FO-PID controller simultaneously to handle the uncertainties caused by renewable sources,load and parametric variations.
文摘Finding the optimal number of clusters has remained to be a challenging problem in data mining research community. Several approaches have been suggested which include evolutionary computation techniques like genetic algorithm, particle swarm optimization, differential evolution etc. for addressing this issue. Many variants of the hybridization of these approaches also have been tried by researchers. However, the number of optimal clusters and the computational efficiency has still remained open for further research. In this paper, a new optimization technique known as “Teaching-Learning-Based Optimization” (TLBO) is implemented for automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified rather it determines the optimal number of partitions of the data “on the run”. The new AUTO-TLBO algorithms are evaluated on benchmark datasets (collected from UCI machine repository) and performance comparisons are made with some well-known clustering algorithms. Results show that AUTO-TLBO clustering techniques have much potential in terms of comparative results and time of computations.
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
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>
文摘In this paper,a multistring-multilevel inverter(M-MLI)for renewable-energy-source applications has been proposed with reduced switch count and harmonics along with single-switch fault analysis for various levels.It requires only‘m+1’power switches for‘m’voltage levels.The proposed work achieves the fine-tuning of switching angles using a metaheuristic technique,i.e.the teaching-learning-based optimization algorithm(TLBOA),to mitigate the total harmonic distortion(THD)of the M-MLI.Furthermore,the proposed TLBOA has been compared with conventional modulation techniques such as equal phase(EP),half-equal phase(HEP),near-level control(NLC)and Newton-Raphson(NR)to verify the effectiveness of TLBOA for various voltage levels in terms of%voltage-THD(%V-THD),computational time and methodology.By fine-tuning the switching angles,the%V-THD is improved significantly when compared with EP,HEP,NLC and NR modulation techniques.For an 11-level single-phase M-MLI,the%V-THD using TLBOA at 0.91 modulation index(MI)is 5.051%.The lower-order harmonics,i.e.5,7,11 and 13,are eliminated to improve the power quality.Furthermore,MLIs are often prone to failure,resulting in waveform distortion.The extreme reduction in power quality impacts the load and significant damage is likely.The location of the open-circuit fault to be identified becomes more tedious under the faulty conditions with increased switch counts and voltage levels since the mathematical modelling fails to address the scenario in less computational time.Hence,the machine-learning approach,i.e.support vector machine(SVM)with Bayesian optimization,has been discussed to locate the faulty switch.Finally,the proposed M-MLI configuration has been modelled,simulated and validated using MATLAB®and Simulink®.The results of the M-MLI configuration have been verified for 7,9 and 11 levels using TLBOA along with fault analysis using the SVM approach.
文摘Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.
