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 task...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.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
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 ti...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.展开更多
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 opt...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.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
针对舱段主动隔振系统中作动器配置优化问题,给出一种优化模型和方法,通过数值计算进行方法验证。首先建立了多通道舱段主动隔振系统的动力学模型,然后将作动器配置优化转换为约束0-1非线性规划问题,以系统监测点响应为优化目标函数,作...针对舱段主动隔振系统中作动器配置优化问题,给出一种优化模型和方法,通过数值计算进行方法验证。首先建立了多通道舱段主动隔振系统的动力学模型,然后将作动器配置优化转换为约束0-1非线性规划问题,以系统监测点响应为优化目标函数,作动器启用状态为自变量,最后采用教与学优化(teaching and learning-based optimization,TLBO)算法寻找最优配置。仿真计算结果表明,对于不同的激励,多通道主动隔振系统的最优配置不同,即存在对应给定激励下抑制壳体振动与声辐射的最优配置。展开更多
文摘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.
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.
基金This research is funded by the National Natural Science Foundation of China(#71772191).
文摘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.
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>
文摘针对舱段主动隔振系统中作动器配置优化问题,给出一种优化模型和方法,通过数值计算进行方法验证。首先建立了多通道舱段主动隔振系统的动力学模型,然后将作动器配置优化转换为约束0-1非线性规划问题,以系统监测点响应为优化目标函数,作动器启用状态为自变量,最后采用教与学优化(teaching and learning-based optimization,TLBO)算法寻找最优配置。仿真计算结果表明,对于不同的激励,多通道主动隔振系统的最优配置不同,即存在对应给定激励下抑制壳体振动与声辐射的最优配置。
