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Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer 认领 引用 被引量：22
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作者 Wei Chen Xi Chen +2 位作者 Jianbing Peng Mahdi Panahi Saro Lee 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第1期93-107,共15页
As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been ... As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency. 展开更多
关键词 Landslide susceptibility Step-wise weight assessment ratio analysis Adaptive neuro-fuzzy fuzzy inference system Teaching-learning-based optimization Satin bowerbird optimizer
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Effective Hybrid Teaching-learning-based Optimization Algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints 认领 引用 被引量：8
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作者 TANG Qiuhua LI Zixiang +2 位作者 ZHANG Liping FLOUDAS C A CAO Xiaojun 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2015年第5期1067-1079,共13页
Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In ... Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS. 展开更多
关键词 two-sided assembly line balancing teaching-learning-based optimization algorithm variable neighborhood search positional constraints zoning constraints synchronism constraints
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An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage 认领 引用 被引量：2
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作者 Deming Lei Surui Duan +1 位作者 Mingbo Li Jing Wang 《Computers, Materials & Continua》 SCIE EI 2024年第4期47-63,共17页
Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid ... Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP. 展开更多
关键词 Hybrid flow shop scheduling reentrant bottleneck stage teaching-learning-based optimization
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Improved Teaching-Learning-Based Optimization Algorithm for Modeling NOX Emissions of a Boiler 认领 引用 被引量：1
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作者 Xia Li Peifeng Niu +1 位作者 Jianping Liu Qing Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2018年第10期29-57,共29页
An improved teaching-learning-based optimization(I-TLBO)algorithm is proposed to adjust the parameters of extreme learning machine with parallel layer perception(PELM),and a well-generalized I-TLBO-PELM model is obtai... An improved teaching-learning-based optimization(I-TLBO)algorithm is proposed to adjust the parameters of extreme learning machine with parallel layer perception(PELM),and a well-generalized I-TLBO-PELM model is obtained to build the model of NOX emissions of a boiler.In the I-TLBO algorithm,there are four major highlights.Firstly,a quantum initialized population by using the qubits on Bloch sphere replaces a randomly initialized population.Secondly,two kinds of angles in Bloch sphere are generated by using cube chaos mapping.Thirdly,an adaptive control parameter is added into the teacher phase to speed up the convergent speed.And then,according to actual teaching-learning phenomenon of a classroom,students learn some knowledge not only by their teacher and classmates,but also by themselves.Therefore,a self-study strategy by using Gauss mutation is introduced after the learning phase to improve the exploration ability.Finally,we test the performance of the I-TLBO-PELM model.The experiment results show that the proposed model has better regression precision and generalization ability than eight other models. 展开更多
关键词 Bloch sphere qubits self-learning improved teaching-learning-based optimization(I-TLBO)algorithm
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Single Solution Optimization Mechanism of Teaching-Learning-Based Optimization with Weighted Probability Exploration for Parameter Estimation of Photovoltaic Models 认领 引用 被引量：1
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作者 Jinge Shi Yi Chen +2 位作者 Zhennao Cai Ali Asghar Heidari Huiling Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第5期2619-2645,共27页
This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)models.The objective is to address challenges related to the detection and maintenanc... This article presents a novel optimization approach called RSWTLBO for accurately identifying unknown parameters in photovoltaic(PV)models.The objective is to address challenges related to the detection and maintenance of PV systems and the improvement of conversion efficiency.RSWTLBO combines adaptive parameter w,Single Solution Optimization Mechanism(SSOM),and Weight Probability Exploration Strategy(WPES)to enhance the optimization ability of TLBO.The algorithm achieves a balance between exploitation and exploration throughout the iteration process.The SSOM allows for local exploration around a single solution,improving solution quality and eliminating inferior solutions.The WPES enables comprehensive exploration of the solution space,avoiding the problem of getting trapped in local optima.The algo-rithm is evaluated by comparing it with 10 other competitive algorithms on various PV models.The results demonstrate that RSWTLBO consistently achieves the lowest Root Mean Square Errors on single diode models,double diode models,and PV module models.It also exhibits robust performance under varying irradiation and temperature conditions.The study concludes that RSWTLBO is a practical and effective algorithm for identifying unknown parameters in PV models. 展开更多
关键词 Teaching-learning-based optimization Single solution optimization Solar energy Photovoltaic models Weighted probability exploration
Weighted Teaching-Learning-Based Optimization for Global Function Optimization 认领 引用 被引量：9
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作者 Suresh Chandra Satapathy Anima Naik K. Parvathi 《Applied Mathematics》 2013年第3期429-439,共11页
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
关键词 Function Optimization TLBO Evolutionary Computation
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Harnessing TLBO-Enhanced Cheetah Optimizer for Optimal Feature Selection in Cancer Data 认领 引用
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作者 Bibhuprasad Sahu Amrutanshu Panigrahi +5 位作者 Abhilash Pati Ashis Kumar Pati Janmejaya Mishra Naim Ahmad Salman Arafath Mohammed Saurav Mallik 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期1029-1054,共26页
Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradi... Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradientinformation, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimizationalgorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets.Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks bybalancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitationability, often converging prematurely or getting trapped in local optima, particularly when applied to discrete featureselection tasks. Previous studies reported that BTLBO yields lower classification accuracy and higher feature subsetvariance compared to other hybrid methods in benchmark tests, motivating the development of hybrid approaches.This study proposes a novel hybrid algorithm, BTLBO-Cheetah Optimizer (BTLBO-CO), which integrates the globalexploration strength of BTLBO with the local exploitation efficiency of the Cheetah Optimization (CO) algorithm. Theobjective is to enhance the feature selection process for cancer classification tasks involving high-dimensional data. Theproposed BTLBO-CO algorithm was evaluated on six benchmark cancer datasets: 11 tumors (T), Lung Cancer (LUC),Leukemia (LEU), Small Round Blue Cell Tumor or SRBCT (SR), Diffuse Large B-cell Lymphoma or DLBCL (DL), andProstate Tumor (PT).The results demonstrate superior classification accuracy across all six datasets, achieving 93.71%,96.12%, 98.13%, 97.11%, 98.44%, and 98.84%, respectively.These results validate the effectiveness of the hybrid approachin addressing diverse feature selection challenges using a Support Vector Machine (SVM) classifier. 展开更多
关键词 Cancer classification hybrid model teaching-learning-based optimization cheetah optimizer feature selection
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基于太赫兹技术与TLBO算法的汽车电镀涂层厚度检测 认领 引用
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作者 侯作云 《电镀与精饰》 CAS 北大核心 2026年第5期95-104,共10页
针对汽车电镀涂层厚度检测中工业噪声干扰与实时性难以协同的难题,提出太赫兹动态核宽滤波与群体智能优化神经网络融合架构。通过噪声能量驱动的高斯核宽自适应调节机制动态适配工业噪声强度,结合主成分分析压缩时频特征矩阵以消除计算... 针对汽车电镀涂层厚度检测中工业噪声干扰与实时性难以协同的难题,提出太赫兹动态核宽滤波与群体智能优化神经网络融合架构。通过噪声能量驱动的高斯核宽自适应调节机制动态适配工业噪声强度,结合主成分分析压缩时频特征矩阵以消除计算冗余。采用双阶段教与学优化策略,教学阶段由精英个体引导动态教学强度,学习阶段通过适应度差异控制协作更新。此外,研究设计的教与学优化Elman网络,利用隐层状态反馈建模厚度时序依赖特性,显著提升检测鲁棒性。实验表明:该方法在5类基体上实现了0.65μm平均绝对误差与52.0 dB峰值信噪比,特征提取延迟11.5 ms,工业误检率均值为1.9%;单次检测能耗23.3 mJ,内存占用峰值9.8 MB,边缘部署能耗波动标准差为1.5 mJ。该架构将镀层混叠工况检测性能提升了13%,为多材质复杂曲面镀层提供高精度厚度检测方案。 展开更多
关键词 太赫兹 教与学优化算法(TLBO) Elman神经网络 无损检测
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An Experimental Investigation into the Amalgamated Al2O3-40% TiO2 Atmospheric Plasma Spray Coating Process on EN24 Substrate and Parameter Optimization Using TLBO 认领 引用
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作者 Thankam Sreekumar Rajesh Ravipudi Venkata Rao 《Journal of Materials Science and Chemical Engineering》 2016年第6期51-65,共15页
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 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. 展开更多
关键词 Atmospheric Plasma Spray (APS) EN24 Design of Experiments (DOE) Teaching Learning Based Optimization (TLBO) Analytic Hierarchy Process (AHP) Al2O3-40% TiO2
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Parameter Optimization of Amalgamated Al2O3-40% TiO2 Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm 认领 引用
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作者 Thankam Sreekumar Rajesh Ravipudi Venkata Rao 《Journal of Surface Engineered Materials and Advanced Technology》 2016年第3期89-105,共17页
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 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. 展开更多
关键词 Atmospheric Plasma Spray (APS) Coating SS304 Steel Teaching Learning Based Optimization (TLBO) Design of Experiments (DoE) Analytic Hierarchy Process (AHP) Al2O2-40% TiO3
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A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems 认领 引用 被引量：4
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作者 Meiji Cui Li Li +3 位作者 MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第11期1952-1966,共15页
This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informat... This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction tool.The search operation conducted in this low space facilitates the population with fast convergence towards the optima.To strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process.Also,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed.The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200.As indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer.Compared with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization. 展开更多
关键词 Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization
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Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs 认领 引用 被引量：2
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作者 N Tamilarasan SB Lenin +1 位作者 P Mukunthan NC Sendhilkumar 《China Communications》 SCIE CSCD 2024年第9期159-178,共20页
In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending netw... In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches. 展开更多
关键词 Adaptive Grasshopper Optimization Algorithm(AGOA) Cluster Head(CH) network lifetime Teaching-Learning-based Optimization Algorithm(TLOA) Wireless Sensor Networks(WSNs)
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Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm 认领 引用 被引量：3
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作者 D.Vidyabharathi V.Mohanraj 《Intelligent Automation & Soft Computing》 SCIE 2023年第6期2559-2573,共15页
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
关键词 Deep learning deep neural network(DNN) learning rates(LR) recurrent neural network(RNN) cyclical learning rate(CLR) hyperbolic tangent decay(HTD) toggle between hyperbolic tangent decay and triangular mode with restarts(T-HTR) teaching learning based optimization(TLBO)
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基于TLBO算法的储能容量优化配置方法 认领 引用 被引量：1
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作者 孙慧颖 李月乔 刘自发 《太阳能学报》 EI CAS CSCD 北大核心 2025年第9期333-341,共9页
提出一种基于教与学优化算法(TLBO)的储能容量优化配置方法。在考虑多因素对光伏出力影响的前提下,构建双层储能容量优化配置模型。上层以储能全寿命周期成本最小为目标函数,利用TLBO算法求解;下层以运行收益最大为目标函数,采用Gurobi... 提出一种基于教与学优化算法(TLBO)的储能容量优化配置方法。在考虑多因素对光伏出力影响的前提下,构建双层储能容量优化配置模型。上层以储能全寿命周期成本最小为目标函数,利用TLBO算法求解;下层以运行收益最大为目标函数,采用Gurobi求解器求解最优日运行策略。最后以大庆某实际光伏电站为例进行仿真,结果表明该方法的有效性。 展开更多
关键词 光伏发电 储能 优化 教与学算法(TLBO)
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基于TLBO-LIBSVM的联合收割机振动筛螺栓故障诊断 认领 引用 被引量：2
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作者 李鹏程 顾新阳 +2 位作者 梁亚权 章浩 唐忠 《农机化研究》 北大核心 2025年第5期28-33,42,共6页
联合收割机振动筛工作时的瞬时冲击与交变载荷易导致振动筛螺栓结构发生失效。为解决联合收割机振动筛螺栓故障诊断问题,提出了一种基于多元特征融合TLBO-LIBSVM的振动筛螺栓失效故障诊断方法,通过提取特征矩阵,分别将时域特征、频域特... 联合收割机振动筛工作时的瞬时冲击与交变载荷易导致振动筛螺栓结构发生失效。为解决联合收割机振动筛螺栓故障诊断问题,提出了一种基于多元特征融合TLBO-LIBSVM的振动筛螺栓失效故障诊断方法,通过提取特征矩阵,分别将时域特征、频域特征、WOA-VMD能量熵特征组合归一化得到多元融合高维特征矩阵,导入经验参数LIBSVM模型,得到的成功率分别为64.44%、74.44%、81.11%、90%。结果表明:随着特征矩阵维数不断增加,失效特征信息不断完善,识别成功率不断提升,也验证了联合收割机振动筛螺栓频域特征敏感性高于时域特征。通过运用TLBO算法对LIBSVM模型超参数进行优化,得到最佳参数组合下的识别成功率为98.89%,完成了联合收割机振动筛螺栓失效故障的高精度识别,可为联合收割机振动筛螺栓故障的精确诊断提供参考。 展开更多
关键词 振动筛螺栓 变分模态分解 鲸鱼优化算法 支持向量机模型 教与学优化算法 故障诊断
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An improved teaching-learning-based optimization for extreme learning machine in floating photovoltaic power forecasting 认领 引用
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作者 Mohd Redzuan Ahmad Nor Farizan Zakaria +1 位作者 Mohd Shawal Jadin Mohd Herwan Sulaiman 《Clean Energy》 EI CSCD 2025年第6期150-173,共24页
Floating photovoltaic systems provide better land use and higher energy output through water cooling effects,but accurate power forecasting remains challenging due to complex environmental factors and measurement erro... Floating photovoltaic systems provide better land use and higher energy output through water cooling effects,but accurate power forecasting remains challenging due to complex environmental factors and measurement errors.This study presents an improved teaching-learning-based optimization algorithm with extreme learning machine for floating photovoltaic power forecasting.The method uses an adaptive teaching factor that adjusts the balance between exploration and exploitation during optimization,replacing fixed teaching factors with continuous,iteration-based adjustment.The research evaluated the approach using comprehensive real data from a floating photovoltaic installation at Universiti Malaysia Pahang Al-Sultan Abdullah,Malaysia.The proposed method achieved superior forecasting accuracy compared to benchmark algorithms including standard teaching-learningbased optimization with extreme learning machine,manta rays foraging optimization with extreme learning machine,moth flame optimization with extreme learning machine,ant colony optimization with extreme learning machine and salp swarm algorithm with extreme learning machine.The improved teaching-learning-based optimization approach demonstrated a root mean squared error of 7.81 kW and coefficient of determination of 0.9386,outperforming all comparison methods with statistically significant improvements.The algorithm showed faster convergence,enhanced stability,and superior computational efficiency while maintaining accuracy suitable for real-time grid integration applications.Phase current measurements were identified as the most important predictors for floating photovoltaic power forecasting.The system achieved high prediction accuracy with most forecasts falling within acceptable error tolerance,making the proposed approach a reliable solution for floating photovoltaic power forecasting that supports grid integration and renewable energy deployment.The methodology addresses unique characteristics of aquatic solar installations while providing practical implementation viability for operational floating photovoltaic systems. 展开更多
关键词 floating photovoltaic systems extreme learning machine improved teaching-Learning-Based optimization renewable energy forecasting power output prediction machine learning optimization
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求解绿色批加工调度问题的多层教学优化算法 认领 引用
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作者 郑友莲 崔樱堃 +1 位作者 雷德明 王静 《计算机应用》 CSCD 北大核心 2026年第6期1922-1930,共9页
针对纺织工厂染色车间里考虑重染工序的绿色并行批处理机(BPM)调度问题,提出一种多层教学优化(MTLBO)算法,以最小化最大完成时间、总能耗和总加权提前/拖期成本。首先,运用启发式规则生成初始种群提升初始解质量;其次,采用多层结构将种... 针对纺织工厂染色车间里考虑重染工序的绿色并行批处理机(BPM)调度问题,提出一种多层教学优化(MTLBO)算法,以最小化最大完成时间、总能耗和总加权提前/拖期成本。首先,运用启发式规则生成初始种群提升初始解质量;其次,采用多层结构将种群划分为教师组、精英班和普通班这3层,并设计高效的层间通信机制,促进信息共享与知识传承;最后,为了增强种群探索能力,防止算法陷入局部最优,引入一种基于概率模型的多样性增强算子替换停滞解。基于工业数据生成测试实例评估MTLBO的性能,并将它与自适应混合蛙跳算法(ASFLA)、多目标人工蜂群(MOABC)算法、模糊遗传算法(FGA)和非支配排序遗传算法Ⅱ(NSGA-Ⅱ)等算法进行比较。实验结果表明,MTLBO的非劣解集的支配关系平均提高81.92%,覆盖度指标平均提高97.58%,且在收敛性指标平均减低99.66%,以上验证了MTLBO在优化调度指标上的更强寻优能力和更高稳定性,为实际生产决策提供了兼具鲁棒性与优化效能的调度方案。 展开更多
关键词 绿色调度问题 批处理机 重染工序 教学优化算法 概率模型
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基于TLBO-LOIRE的回采工作面瓦斯涌出量预测 认领 引用 被引量：10
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作者 胡坤 王素珍 +1 位作者 韩盛 王爽 《应用基础与工程科学学报》 EI CSCD 北大核心 2017年第5期1048-1056,共9页
瓦斯涌出量是瓦斯防治与管理、矿井通风系统设计的重要基础数据,准确地预测瓦斯涌出量对于煤矿安全生产有着极其重要的指导意义与应用价值.但工作面瓦斯涌出规律复杂,在检测、数据采集过程中不可避免地会混入异常噪声,直接影响着瓦斯预... 瓦斯涌出量是瓦斯防治与管理、矿井通风系统设计的重要基础数据,准确地预测瓦斯涌出量对于煤矿安全生产有着极其重要的指导意义与应用价值.但工作面瓦斯涌出规律复杂,在检测、数据采集过程中不可避免地会混入异常噪声,直接影响着瓦斯预测的准确性.本文采用l1正则化异常值隔离与回归方法(LOIRE)对煤矿回采工作面瓦斯涌出量及其相关影响因素的统计样本数据库进行计算分析,隔离样本的异常噪声干扰,利用教与学算法(TLBO)优化回归参数,建立了回采工作面瓦斯涌出量的优化预测模型,并对煤矿现场数据进行分析预测,结果表明3个回采工作面的瓦斯涌出量预测误差分别为3.04%、0.33%和2.36%,平均相对误差仅为2.36%.TLBO-LOIRE优化预测方法,预测准确性高,能够满足井下瓦斯防治的工程需要,对其它工程领域的数据预测同样适用. 展开更多
关键词 瓦斯涌出量 预测 TLBO LOIRE 参数优化
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基于改进TLBO算法的刮板输送机伸缩机尾PID控制系统优化 认领 引用 被引量：7
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作者 胡坤 张长建 +1 位作者 王爽 韩盛 《中南大学学报（自然科学版）》 EI CAS CSCD 北大核心 2017年第1期106-111,共6页
为了提高刮板输送机伸缩机尾控制系统的工作性能,将一种新的群智能优化算法,即教学与学习算法(TLBO)应用于机尾PID控制器的参数优化中,并提出新的自适应教学因子计算方法,其利用完整学习阶段前、后学生群体成绩的变化来决定教学因子的... 为了提高刮板输送机伸缩机尾控制系统的工作性能,将一种新的群智能优化算法,即教学与学习算法(TLBO)应用于机尾PID控制器的参数优化中,并提出新的自适应教学因子计算方法,其利用完整学习阶段前、后学生群体成绩的变化来决定教学因子的取值。研究结果表明:改进后的TLBO算法的精度及稳定性均比原TLBO算法的优。在建立刮板输送机伸缩机尾控制系统模型的基础上,利用改进的TLBO方法进行PID参数整定,并引入超调量控制指标对适应度函数再次完善,二次优化后的刮板输送机伸缩机尾控制系统具有良好控制品质和鲁棒性。 展开更多
关键词 刮板输送机 伸缩机尾 TLBO 教学因子 PID参数优化
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基于TLBO算法的不确定性条件下复杂产品协同设计的可靠性拓扑优化 认领 引用 被引量：1
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作者 Zhaoxi Hong Xiangyu Jiang +2 位作者 冯毅雄 Qinyu Tian 谭建荣 《Engineering》 SCIE EI CAS CSCD 2023年第3期71-81,共11页
复杂产品的拓扑优化设计可以显著节省材料和节能,有效地降低惯性力和机械振动。本研究以一种大吨位液压机作为典型的复杂产品,用于阐述该优化方法。本文提出了一种基于可靠性与优化解耦模型和基于教学学习的优化(TLBO)算法的可靠性拓扑... 复杂产品的拓扑优化设计可以显著节省材料和节能,有效地降低惯性力和机械振动。本研究以一种大吨位液压机作为典型的复杂产品,用于阐述该优化方法。本文提出了一种基于可靠性与优化解耦模型和基于教学学习的优化(TLBO)算法的可靠性拓扑优化方法。将由板结构形成的支撑物作为拓扑优化对象,重量轻、稳定性好。将不确定性下的可靠性优化和结构拓扑优化协同处理。首先,利用有限差分法将优化问题中的不确定性参数修正为确定性参数。然后,将不确定性可靠性分析和拓扑优化的复杂嵌套解耦。最后,利用TLBO算法求解解耦模型,该算法参数少,求解速度快。TLBO算法采用了自适应教学因子,在初始阶段实现了更快的收敛速度,并在后期进行了更精细的搜索。本文给出了一个液压机基板结构的数值实例,说明了该方法的有效性。 展开更多
关键词 Plates structure Reliability Collaborative topology optimization Teaching-learning-based optimization algorithm Uncertainty Collaborative design for product life cycle
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