TLBO Algorithm Research Articles

Hybrid wind power forecasting based on extreme learning machine and improved TLBO algorithm

Wind power forecasting (WPF) plays an important role in the planning, efficient operation, and security maintenance of power systems. A large number of hybrid models have been applied to WPF in the past two decades. Due to the rapid development of swarm intelligence algorithms, there is great potential for forecasting performance improvements by combining them with basic data-driven models for parameter optimization. In this study, a hybrid WPF method is proposed, which combines an extreme learning machine (ELM) and improved teaching-learning-based optimization (iTLBO), and incorporates a recursive feature elimination (RFE) method for feature selection. For WPF, appropriate feature combination is recognized from original input data using the RFE method, which helps facilitate understanding of the data pattern and defy the curse of dimensionality. To enhance the convergence speed and learning ability of the basic TLBO, four improvements are performed, and the obtained iTLBO algorithm is applied to optimize the parameters of the ELM model. Case study data came from a wind farm in Yunnan, China. The ERMSE, EMAE, and MAPE values of the proposed hybrid method are all lower than those of the comparison methods. The results demonstrate the superior forecasting performance that makes the hybrid method more applicable in real WPF applications.

Research on Geographic Location Prediction Algorithm Based on Improved Teaching and Learning Optimization ELM

With the rapid development of vehicle-mounted communication technology, GPS data is an effective method to predict the current road vehicle track based on vehicle-mounted data. GPS-oriented vehicle-mounted data position prediction method is currently a hot research work and an effective method to realize intelligent transportation. In this paper, an improvement scheme is proposed based on the problem of falling into local optimization existing in the basic algorithm of teaching and learning optimization algorithm. An interference operator is used to disturb teachers to enhance the kinetic energy of the population to jump out of local optimization. By comparing the performance of GA, PSO, TLBO and ITLBO algorithms with four test functions, the results show that ITLBO has efficient optimization effect and generalization ability. Finally, the ITLBO-ELM algorithm has the best prediction effect by comparing the vehicle GPS data and comparing the experimental algorithms.

A multi-response optimal design of bistable compliant mechanism using efficient approach of desirability, fuzzy logic, ANFIS and LAPO algorithm

Compliant mechanisms are promising candidates in precision engineering, soft robotics, space, and bioengineering due to their advantages of free friction, free lubricant, no backlash, monolithic structure, and minimal assembly. However, designing and analyzing of compliant mechanisms are facing the high complexity due to a coupling of kinematic and mechanical behavior in comparison to rigid-body mechanisms. Especially, considering a multi-objective optimization design for compliant mechanisms, the problem is more complicated. Thus, this paper presents a new efficient hybrid methodology for solving the multi-objective optimization design. A hybridization is developed through a combination of finite element method, statistical technique, desirability function approach, fuzzy logic system, adaptive neuro-fuzzy inference system (ANFIS), and Lightning attachment procedure optimization (LAPO). A bistable compliant mechanism is investigated as an application example of the proposed method. First, design variables of the mechanism are determined, and then central composite design is employed to construct a numerically experimental matrix. Though using analysis of variance and Taguchi approach, the design variables are refined to make new populations. Subsequently, desirability values of two performances of the mechanism are computed, and the results are transferred into the fuzzy logic system. The output of fuzzy logic system is considered as single combined objective function. By developing the ANFIS model, the relation between the refined design variables and the output of fuzzy logic system is established. Finally, LAPO algorithm is adopted for solving the multi-objective optimization problem for the mechanism. Three numerical examples are investigated to validate the performance efficiency of the proposed method. The results demonstrate that the proposed method is more efficient than Taguchi-based fuzzy logic. Besides, through Wilcoxon signed rank test and Friedman test, it reveals that the performances of proposed approach are superior to those of the Jaya algorithm and TLBO algorithm. The results of this article can be extended for other complex compliant mechanisms as well as optimization problems with multiple objective functions and more complex constraints.

ANFIS Modeling with ICA, BBO, TLBO, and IWO Optimization Algorithms and Sensitivity Analysis for Predicting Daily Reference Evapotranspiration

AbstractEvapotranspiration (ET) is an important factor in water resource management. This research investigated the performance of four optimization algorithms to hybridize adaptive network-based f...

The Load Forecasting Method Based on Adaptive Neural Network and TLBO Algorithm

Load forecasting is of great significance for the arrangement and optimization of the distribution network scheduling. This paper proposes a load forecasting method based on an improved TLBO optimized adaptive neural network. Firstly, the ‘teaching’ phase in the basic TLBO algorithm is improved, and the average value of all search individuals is changed while adopting adaptive teaching factors, so that the performance of TLBO in the entire search space can be adaptively improved. Then, the ‘learning’ phase of the TLBO is improved. The Gaussian mutation operator is introduced in the learning phase to maintain the diversity of the population and avoid the TLBO algorithm’s premature convergence and local optimization. Finally, using the improved TLBO algorithm to optimize the adaptive neural network forecast model. In the end, the actual load data of Tianjin Power Grid was used to verify the accuracy of the simulation results.

Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem. This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored. IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM). Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms.

HYBRID APPROACH FOR HUMAN FACIAL EXPRESSION DETECTION THROUGH TLBO AND PFEF

This paper introduces facial expression detection method which is based on facial’s selected feature and optimized those selected features. The study says that human face generally faced generally consist of skin color, texture shape and size of face in this paper we study skin color and texture of human face .This process consist two steps for the same. In first known as detection of expression which uses PFEF (partial feature extension function) and in second, for optimization we used TLBO algorithm is basically a population base searching technics. Also uses soft computation technics because we cannot actual and accurate for human related activity. Varieties of technic are used for the same purpose this as per use hybrid approach to get better result.

Clustering Algorithm of Density Difference Optimized by Mixed Teaching and Learning

Density peak clustering (DPC) algorithm is to find clustering centers by calculating the local density and distance of data points based on the distance between data points and the cutoff distance (dc) set manually. Generally, the attribute calculation between data points is simply obtained by Euclidean distance. However, when the density distribution of data points in data sets is uneven, there are high-density and low-density points, and the dc value is set artificially and randomly, this will seriously affect the clustering results of DPC algorithm. For this reason, a clustering algorithm which combines teaching and learning optimization algorithm and density gap is proposed (NSTLBO-DGDPC). First, in order to consider the influence of data point attributes and neighborhoods, the density difference distance is introduced to replace the Euclidean distance of the original algorithm. Secondly, because manual selection of clustering centers may produce incorrect clustering results, the standard deviation of high-density distance is used to determine the clustering centers of clustering algorithm. Finally, using the teaching and learning optimization algorithm (TLBO) to find the optimal value, in order to avoid the algorithm falling into local optimum. When the population density reaches a certain threshold, the niche selection strategy is introduced to discharge the similarity value, and then the nonlinear decreasing strategy is used to update the students in the teaching stage and the learning stage to obtain the optimal dc solution. In this paper, the accuracy and convergence of the improved TLBO algorithm (NSTLBO) are verified by ten benchmark functions. Simulation experiments show that the NSTLBO algorithm has better performance. Clustering algorithm integrating teaching and learning optimization algorithm and density gap proposed in this paper are validated by using eight synthetic data sets and eight real data sets. The simulation results show that the algorithm has better clustering quality and effect.

A Hybrid Computational Method of Desirability, Fuzzy Logic, ANFIS, and LAPO Algorithm for Multiobjective Optimization Design of Scott Russell Compliant Mechanism

Flexure-based compliant mechanisms are increasingly promising in precision engineering, robotics, and other applications, thanks to the excellent advantages of no friction, no backlash, no wear, and minimal assembly. However, their design and analysis are still challenging due to the coupling of kinematic-mechanical behaviors with large nonlinear deflections in comparison to their rigid-body counterparts. Optimal design is an important aspect in the field of compliant mechanisms and has attracted much attention during the last decades. Especially, when considering a multiobjective optimization design for compliant mechanisms, the problem is becoming more complicated. Thus, this article presents a new efficient hybrid computational method to resolve multiobjective optimization design of compliant mechanisms. A Scott Russell compliant mechanism is employed as the design example and to show the advantages of the proposed optimizing method. The proposed method is developed by hybridizing the desirability function approach, fuzzy logic system, adaptive neuro-fuzzy inference system (ANFIS), and lightning attachment procedure optimization (LAPO). First of all, a 3D finite element model is created and central composite design is employed to build a numerical matrix. First, design variables are refined by using analysis of variance and Taguchi approach in terms of considerably eliminating space of design variables. Subsequently, desirability values of two objective functions are determined and transferred into the fuzzy logic system. The output of the fuzzy logic system is considered as a single combined objective function. Next, modeling for fuzzy output is established via developing the ANFIS model. At last, the LAPO algorithm is adopted for solving the multiobjective optimization problem for the mechanism. Three numerical examples are investigated to validate the feasibility and the effectiveness of performance efficiency of the proposed methodology. The results find that the proposed methodology is more efficient than traditional Taguchi-based fuzzy logic. Besides, the performance efficiency of the proposed approach outperforms the Jaya algorithm and TLBO algorithm through the Wilcoxon signed rank test and Friedman test. The results of this article give a useful approach for complex optimization problems.

Optimum design of reinforced concrete counterfort retaining walls using TLBO, Jaya algorithm

In this study, the optimum design of a reinforced concrete counterfort retaining wall for minimum cost is investigated by using TLBO and Jaya algorithms. There are 17 design variables in the problem of reinforced concrete counterfort retaining wall created in the study. These design variables are related to the wall geometry and steel reinforcement in various parts of the wall. There are a number of charts used in the technical literature to determine the cross-sectional effects of the stem and heel slabs of the wall. However, it is seen that these charts have been prepared at certain aspect ratios of the plates. In this case, Open Application Programming Interface of SAP2000 Software (called SAP2000-API) is used within the algorithm to realize a more realistic structural analysis. Thus, the desired structural analysis can be performed by applying external loads on the wall model prepared in SAP2000 Software through MATLAB Software where the algorithm is encoded. The desired cross-sectional effects are taken from this Software and used in the algorithm. The problem has a total of 46 constraints which consist of checks of sliding, overturning, and bearing capacity of the wall, reinforced concrete cross-section controls, and conditions related to size and reinforcement. The performance of the algorithms according to the findings from the optimum design performed on a numerical application is evaluated with standard statistical data and Wilcoxon test. According to Wilcoxon test, it is also observed that TLBO algorithm is more successful than Jaya algorithm for this problem.

The Optimal Power Flow Solution by Optimal Location of STATCOM Device using AHP Method

Optimum power flow is a useful tool for planning and operating the electrical system and maintains the economy and safety of the modern electrical system. Teaching, Learning-based Algorithm is one of the new Metaheuristic algorithms which can influence both teachers and students by expediting the interaction among them in sharing the necessary knowledge. The proposed TLBO is designed here to solve the problem of an optimum power flow with STATCOM FACTS device. The optimal location of STATCOM FACTS device on the weak bus is obtained by Analytical Hierarchy Process (AHP) method. The main objective of this study is to reduce fuel cost of generation, reduce active and reactive power loss, improve voltage deviation and enhance voltage stability index within the given control variable constraints. The proposed TLBO algorithm with STATCOM device is evaluated on the standard IEEE-57 bus system. From the simulation result, it shows that the Teaching Learning-based algorithm gives the optimal solution as compared to the recent algorithm mentioned in the literature with some IEEE-57 bus test system.

Experimental investigation and parametric optimization for minimizing surface roughness during WEDM of Ti6Al4V alloy using modified TLBO algorithm

Wire-cut electrical discharge machining (WEDM) is a widely used non-contact machining process used to cut difficult-to-machine materials such as titanium, nickel and inconel alloys. WEDM is famous for producing components in diverse engineering applications, including automotive, marine, space, medical sectors with the highest accuracy and consistency. In this paper, experimental investigation on surface roughness (SR) of Ti6Al4V alloy employing WEDM process is presented using Taguchi L16 experimental plan by varying four process variables, viz., pulse on time (Ton), pulse off time (Toff), current (I) and wire speed (WS) at four different levels. The novelty of the present work is minimization of surface roughness (SR) in WEDM of Ti6Al4V alloy employing reusable wire technology during experimental work and optimizes the problem with modified teaching–learning-based optimization (M-TLBO) algorithm. A novel technique for fitness curve fitting is also illustrated to obtain global optima for minimization of SR. Results showed that SR decreased by 2.65% at optimal parameter setting Ton = 13 µs, Toff = 10 µs, I = 1 A and WS = 850 rpm and global optima is obtained as 3.749 µm. Pulse on time (44.06%) and current (28.69%) are recognized as the most dominant process variables affecting SR followed by pulse off time (15.80%) and wire speed (7.47%). Surface morphology study of machined Ti alloy using SEM images showed that smoother surface is obtained at lower settings of pulse on time and current. The proposed M-TLBO algorithm is found to be highly accurate and consistent during several runs conducted with mean fitness value and standard deviation recorded as 3.751 µm and 0.006417, respectively.

An effective hybrid approach of desirability, fuzzy logic, ANFIS and LAPO algorithm for optimizing compliant mechanism

This paper proposes an efficient hybrid approach for solving multi-objective optimization design of a compliant mechanism. The approach is developed by integrating desirability function approach, fuzzy logic system, adaptive neuro-fuzzy inference system, and Lightning attachment procedure optimization. Box–Behnken design is used to form a numerically experimental matrix. First, a refinement of design variables is conducted through analysis of variance and Taguchi approach in terms of considerably eliminating space of design variables and computation efforts. Next, desirability of two objective functions is computed and transferred into the fuzzy logic system. The output of fuzzy logic system is regarded as single combined objective function. Subsequently, a modeling for fuzzy output is developed via adaptive neuro-fuzzy inference system. Then, LAPO algorithm is adopted for solving the optimization problem. By investigating three different numerical examples, performance of the proposed approach is validated. Numerical results revealed that the proposed approach has a computational accuracy better than that of Taguchi-based fuzzy logic reasoning. Finally, case study 1 is chosen as an optimal solution for the mechanism. Furthermore, the effectiveness of proposed approach is greater than that of the Jaya algorithm and TLBO algorithm through Wilcoxon signed rank test and Friedman test. The proposed approach can be used for related engineering fields.

Stability Enhancement of Power System with UPFC Using Hybrid TLBO Algorithm

Power sector's complexity has been increasing due to rising demand—distributed generation and deregulation have greatly increased the complexity of the power system. Flexible Alternating Current Transmission System (FACTS) devices improve the quality of power by increasing the power transfer capability. This paper proposes an optimal power flow analysis using a Modified Teaching Learning Based Optimization (MTLBO) algorithm followed by an optimal placement of UPFC in the system. The proposed analysis has been validated and implemented on an IEEE 30 bus system.

A Survey of Application and Classification on Teaching-Learning-Based Optimization Algorithm

Teaching-Learning-based Optimization is an optimization technique which does not require any algorithm-specific parameters and is popular for its less computational cost and high consistency. Therefore, it has achieved great success application by the researchers in various disciplines of engineering. It works on the philosophy of teaching and learning which is used to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Recently the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities and the performance. However, there is less surveys on TLBO algorithm recent advances and its application. In this paper, the successful researches of TLBO algorithm of the past decade are surveyed. Firstly, the available intelligent optimization algorithms were reviewed. Then the application fields of TLBO and the improved TLBO were discussed and analyzed. Furthermore, some representative TLBO methods were classified into three main groups: 1) Improvement of teaching process; and 2) Fusion with Other Optimization Methods; and 3) Weight Methods and Others. Finally, our viewpoints were shared on the open issues and challenges in TLBO as well as research trends in the future.

Feature Selection Using Improved Teaching Learning Based Algorithm on Chronic Kidney Disease Dataset

Abstract Feature selection plays an important role in almost any data mining application especially in medical data mining to solve the problem of ‘curse of dimensionality’ and provide early diagnosis with relevant features and high accuracy. Innumerable feature selection methods have been presented in state-of-arts literature to tackle the problems of high dimensional data. Many evolutionary and swarm intelligence algorithms find solutions based on algorithm-specific control parameters. However, it is a challenging task to identify the optimal feature subset using a feature selection algorithm that is not dependent on the controlling parameters of an algorithm that is specific to a particular problem in hand. Hence, the present research work is based on the working principle of the original TLBO algorithm which does not require any algorithm-specific parameters. The proposed research work is known as Improved Teacher Learner Based Optimization (ITLBO) algorithm which aims to select the best feature subset based on Chebyshev distance formula in the evaluation of the fitness function and common control parameters viz., population size and number of generations to find the optimal feature subset for early diagnosis of chronic diseases. The proposed feature selection technique was applied to Chronic Kidney Disease (CKD) dataset and has achieved a significant feature reduction of 36% compared to the feature reduction of 25 % obtained by applying the original TLBO algorithm. The derived optimal feature subset obtained from TLBO algorithm and feature subset obtained from ITLBO algorithm is validated by evaluating the accuracy of Support Vector Machine (SVM), Convolution Neural Networks (CNN) and Gradient Boosting classification algorithms. Experimental results reveal that there is an overall improvement of classification accuracy for the three algorithms for the derived feature subset from the proposed feature selection algorithm compared to the original TLBO algorithm.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Varna-based optimization: a novel method for capacitated controller placement problem in SDN

Recently, software defined networking (SDN) is a promising paradigm shift that decouples the control plane from the data plane. It can centrally monitor and control the network through softwarization, i.e., controller. Multiple controllers are a necessity of current SDN based WAN. Placing multiple controllers in an optimum way is known as controller placement problem (CPP). Earlier, solutions of CPP only concentrated on propagation latency but overlooked the capacity of controllers and the dynamic load on switches, which is a significant factor in real networks. In this paper, we develop a novel optimization algorithm named varna-based optimization (VBO) and use it to solve CPP. To the best of our knowledge, this is the first attempt to minimize the total average latency of SDN along with the implementation of TLBO and Jaya algorithms to solve CPP for all twelve possible scenarios. Our experimental results show that TLBO outperforms PSO, and VBO outperforms TLBO and Jaya algorithms in all scenarios for all topologies.

Design optimization of a compressor transition S-shaped duct using a teaching–learning-based optimization algorithm

A high bypass turbofan aero-engine delivers compressed air from the low-pressure to the high-pressure compressor through a compressor transition duct. Weight and design space limitations impel to its S-shaped design. Despite that, the compressor transition duct has to guide the flow very carefully to the high-pressure compressor without disturbances and flow separations. Hence, the present paper is devoted to elaborate on the application of the proposed heuristic optimization technique known as multi-objective teaching–learning-based optimization (MO-TLBO) algorithm in the area of S-shaped transition compressor duct. The present algorithm is applied for obtaining the optimum design of the S-shaped duct while minimizing the total pressure loss coefficient and non-uniformity index at the S-shaped duct outlet. These objectives are also optimized independently and as a multi-objective consideration as well. Because of the conflicting nature of the objective functions, a Pareto-optimal curve is also presented for a successful trade-off between the objectives. Pareto-optimal curve gives flexibility to the designer for selecting the best set of the parameters based on the objectives. Successful execution of the proposed MO-TLBO algorithm is examined by comparing the optimum results acquired by the multi-objective genetic algorithm and comparisons divulge that the TLBO algorithm can be successfully practiced to obtain the optimized design of compressor transition S-shaped duct. Optimized duct shows the reduction in total pressure loss and non-uniformity by 28.80% and 36.67%, respectively, despite the reduction of 14.74% in overall length.

The DABC and TLBO Algorithms for Solve Job Shop Scheduling Problem

The DABC and TLBO Algorithms for Solve Job Shop Scheduling Problem