Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

With the emergence of the artificial intelligence era,all kinds of robots are traditionally used in agricultural production.However,studies concerning the robot task assignment problem in the agriculture field,which is closely related to the cost and efficiency of a smart farm,are limited.Therefore,a Multi-Weeding Robot Task Assignment(MWRTA)problem is addressed in this paper to minimize the maximum completion time and residual herbicide.A mathematical model is set up,and a Multi-Objective Teaching-Learning-Based Optimization(MOTLBO)algorithm is presented to solve the problem.In the MOTLBO algorithm,a heuristicbased initialization comprising an improved Nawaz Enscore,and Ham(NEH)heuristic and maximum loadbased heuristic is used to generate an initial population with a high level of quality and diversity.An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule.A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm.Finally,a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature.Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.

Research on the Teaching Optimization Path in Children’s Creative Art Curriculum Under the Perspective of“Sanquan Education”

Children’s creative art curriculum is a professional course supported by art materials and techniques and facilitated through creative activities,aiming at cultivating students’ability to organize creative art activities using various art materials and techniques.In light of the evolving focus in China’s colleges and universities from“what to know”to“how to apply,”the pivotal question for educators is how to maximize the value of children’s creative art curriculum.Based on this,this article takes“Sanquan education”as the starting point,combines factual and theoretical arguments,and analyzes the teaching optimization path of children’s creative art curriculum under the perspective of Sanquan education,to improve the quality and efficiency of teaching.

Efficient Computation Offloading of IoT-Based Workflows Using Discrete Teaching Learning-Based Optimization

As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.

Research on the Optimization of Teaching System of International Economy and Trade

Taking the construction of new liberal arts as a background,this paper proposes that the international economy and trade major should optimize the teaching system and personnel training mechanism by adding the new cross-border e-commerce direction.This paper highlights the development path of high-quality international economy and trade professional training,and further ensures the first-class talent training and first-class professional construction specialty.

Research on The Method of Optimization of Higher Vocational English Teaching in The＂Internet Plus＇Era

With the continuous development of network technology, the related educational work is also in constant innovation,the Internet technology has been gradually used in teaching,which has increased the diversity of teaching,higher vocational English is no exception.Higher vocational education pays more attention to the cultivation of students＇ practical ability. English,as a language with strong application,needs to be adapted to the development of society in the process of learning.In the ＂Internet plus＂ era,in the process of higher vocational English teaching teachers should carry out a comprehensive innovation to teaching methods,combine education with the Internet, improve the adaptability of English teaching of times,improve the practical ability and application ability of students majoring in English,create good conditions for the improvement of the overall quality of English in Higher Vocational colleges.This paper focuses on the research of the method of optimization of higher vocational English teaching in the＂ Internet plus＂ era.

Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm

For training the present Neural Network(NN)models,the standard technique is to utilize decaying Learning Rates(LR).While the majority of these techniques commence with a large LR,they will decay multiple times over time.Decaying has been proved to enhance generalization as well as optimization.Other parameters,such as the network’s size,the number of hidden layers,drop-outs to avoid overfitting,batch size,and so on,are solely based on heuristics.This work has proposed Adaptive Teaching Learning Based(ATLB)Heuristic to identify the optimal hyperparameters for diverse networks.Here we consider three architec-tures Recurrent Neural Networks(RNN),Long Short Term Memory(LSTM),Bidirectional Long Short Term Memory(BiLSTM)of Deep Neural Networks for classification.The evaluation of the proposed ATLB is done through the various learning rate schedulers Cyclical Learning Rate(CLR),Hyperbolic Tangent Decay(HTD),and Toggle between Hyperbolic Tangent Decay and Triangular mode with Restarts(T-HTR)techniques.Experimental results have shown the performance improvement on the 20Newsgroup,Reuters Newswire and IMDB dataset.

Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide.This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions.The infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good performance.The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)algorithm.This networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs successfully.To avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F controller.The simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy controllers.Moreover,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language.Two common models are available:Machine Learning and lexicon-based approaches to address emotion classification problems.With this motivation,the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the Arabic language.The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

The Role of Teachers’Apperception in Herbart’s Theory for Chinese Teaching in Shenzhen Primary Schools of China

This paper deeply analyzes the practical application of Herbart’s educational concept in contemporary education and teaching,especially its guiding significance to the primary school Chinese classroom in Shenzhen area.Herbart proposed that teachers should play a key role in students’cognitive process,that is,help students better understand and master new knowledge by combining it with existing knowledge.The survey results show that although teachers have some understanding of Herbart’s educational philosophy,it is not widely used in the concrete teaching process.However,the study also shows that there is a positive relationship between Herbart’s theory and students’academic performance,indicating that it plays an important role in improving students’interest and engagement in learning.The research also reveals the differences between educational concepts and practices,as well as the uneven distribution of educational resources,and puts forward measures to alleviate these contradictions,such as strengthening teacher training,improving teaching methods,and emphasizing moral education.At the same time,the study also highlights the critical role of teachers in promoting knowledge integration,arousing learning enthusiasm,shaping students’moral character,and promoting personal and professional growth.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

A novel improved teaching and learning-based-optimization algorithm and its application in a large-scale inventory control system

Purpose–The purpose of this paper is to propose a novel improved teaching and learning-based algorithm(TLBO)to enhance its convergence ability and solution accuracy,making it more suitable for solving large-scale optimization issues.Design/methodology/approach–Utilizing multiple cooperation mechanisms in teaching and learning processes,an improved TBLO named CTLBO(collectivism teaching-learning-based optimization)is developed.This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes.Applying modularizationidea,based on the configuration structure of operators ofCTLBO,six variants ofCTLBOare constructed.Foridentifying the best configuration,30 general benchmark functions are tested.Then,three experiments using CEC2020(2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms.At last,a large-scale industrial engineering problem is taken as the application case.Findings–Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO.Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems.The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem,while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c,revealing that CTLBO and its variants can far outperform other algorithms.CTLBO is an excellent algorithm for solving large-scale complex optimization issues.Originality/value–The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism,self-learning mechanism in teaching and group teaching mechanism.CTLBO has important application value in solving large-scale optimization problems.

An Experimental Investigation into the Amalgamated Al2O3-40% TiO2 Atmospheric Plasma Spray Coating Process on EN24 Substrate and Parameter Optimization Using TLBO

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 Al2O3-40% TiO2 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 Al2O3-40% TiO2 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.

Parameter Optimization of Amalgamated Al2O3-40% TiO2 Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm

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 Al2O3-40% TiO2 ceramic coatings to get the best quality of coating on commercial SS304 substrate. The experiments are conducted with a three-level L18 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.

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements.These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints,such as the valve point effect,power balance and ramprate limits.The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times.In this paper,multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model.Self-learning teaching-learning based optimization(TLBO)is employed to solve the non-convex non-linear dispatch problems.Numerical results onwell-known benchmark functions,as well as test systems with different scales of generation units show the significance of the new scheduling method.

A novel OFS–TLBO–SVR hybrid model for optimal budget allocation of government schemes to maximize GVA at factor cost

Support Vector Regression (SVR) has already been proved to be one of the mostreferred and used machine learning technique in various fields. In this study, wehave addressed a predictive-cum-prescriptive analysis for finalizing fundallocations by the Government at center to the schemes under Central Plan andto the schemes under States and Union Territories Plan, with a goal to maximizeGross Value Added (GVA) at factor cost. Here, we have proposed a hybridmachine learning model comprising of OFS (Orthogonal Forward Selection),TLBO (Teaching Learning Based Optimization) and SVR for the prediction ofGVA at factor cost. In this model, referred as OFS–TLBO–SVR hybrid model,SVR is at the core of prediction mechanism, OFS is for identifying the relevantfeatures, and TLBO is to support in optimizing the free parameters of SVR andagain TLBO is used for optimizing the governable attributes of data.