Effective Hybrid Teaching-learning-based Optimization Algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints

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.

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.

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.

基于混合TLBO-DE算法的图像去噪卷积神经网络

针对用于图像去噪的卷积神经网络(Convolutional Neural Networks,CNN)的超参数与结构难以确定的问题,本文提出了一种基于混合教与学优化算法(Teaching-Learning-Based Optimization,TLBO)和差分进化算法(Differential Evolution,DE)的CNN网络并应用于图像去噪。首先,建立了CNN超参数与结构优化的数学模型;其次,提出了一种混合TLBO-DE算法,并将其用于去噪CNN超参数与结构的优化。在该混合TLBO-DE算法的进化前期,种群以较大的概率采用DE算法的进化机制进行进化,从而提高种群多样性;在进化后期,种群以较大的概率采用TLBO算法的“教”机制进行进化,从而提高算法收敛速度;最后,在公共的医学图像数据集上对所提方法进行测试。实验结果表明,与基于遗传算法、DE和TLBO等算法的CNN去噪方法相比,本文所提方法具有更好的优化性能和图像去噪性能。与目前去噪性能较好的块匹配滤波(Block-Matching and 3D filtering,BM3D)、去噪卷积神经网络(Denoising Convolutional Neural Network,DnCNN)方法相比,本文所提方法具有更好的去噪性能。

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.

改进TLBO的相关反馈图像检索方法

针对当前基于进化算法的相关反馈图像检索方法无法很好地结合用户偏好信息和设置参数过多的问题,提出一种基于改进教与学优化的相关反馈图像检索方法。根据图像检索问题的特定环境,对教与学优化算法进行了一系列改进:首先,结合最近邻分类法构造适应度函数的约束条件,使之更好地反映用户偏好信息;其次,通过在教阶段将相关图像集的中心图像作为教师以及在学阶段将相关图像作为学员学习的对象,使算法快速收敛到相关图像区域;最后,结合约束处理技术Deb准则进行学员的选择操作。将该算法与目前效果优异的3种基于进化算法的相关反馈技术在两套标准图像测试集上进行对比。结果表明,所提算法相较于另外3种算法具有明显的优势,能更好地结合用户偏好信息提高图像检索性能。

NTLBO算法优化ELM的SOC预测方法

为提高锂电池荷电状态(SOC)预测的精度,提出了新型教与学优化(NTLBO)算法优化极限学习机的SOC预测方法。首先,采用Logistics混沌对种群中精英个体进行优化以改善算法的全局优化性能;其次,采用改进的TLBO算法优化调整ELM模型的输入权值和隐含层阈值,构建NTLBO-ELM预测模型以提升模型的泛化能力。以某锰酸锂电池为研究对象对NTLBO-ELM模型进行测试验证并与其他3种模型相比较,结果表明提出的方法具有较小的预测误差和良好的泛化能力,验证了模型的有效性。

基于TLBO的工程结构表面缺陷图像边缘检测方法

非接触式、数字化的工程质量检测方法对于快速发现建筑结构表面缺陷,如裂缝、焊接缺陷等,降低工程质量检测的劳动强度具有重要的工程意义。本文在计算机数字图像处理技术基础上,根据建筑结构表面缺陷图像特征,提出基于TLBO算法的缺陷图像轮廓识别预处理方法,作为进一步缺陷特征判断的依据。本文在TLBO算法基础上,边缘像素点的搜索不需要设定任何算法参数,实现简单;提出基于的8个方向的灰度导数,建立图像边缘强度矩阵,将边缘点附近的小规模局部搜索和大量的全局搜索相结合,TLBO算法保证了所提出的边缘检测方法不会陷入局部边缘点,找到最重要的图像全局边缘特征;将TLBO算法应用于图像边缘检测,以工程质量检测中常见的钢结构焊缝检测为例加以验证和分析,证明了本文方法在缺陷图像轮廓识别预处理中的抗噪性和有效性。

改进TLBO算法优化灰色神经网络的ORP预测

在生物氧化提金预处理过程中,由于传统的氧化还原电位预测方法精度不高,该文提出一种改进教与学优化算法(ATLBO)优化灰色神经网络的预测方法。在ATLBO算法中,采用多种群协同学习策略,有效地提高了算法的收敛速度和寻优精度。同时,对各个子种群进行随机交叉操作,大大降低算法陷入局部最优的可能性。运用ATLBO算法优化灰色神经网络(GNN)的参数,并将最优解作为灰色神经网络的输入,对氧化还原电位进行预测。仿真结果表明,与其他预测方法相比,该预测模型能达到较好的预测精度,并且优于其他预测模型。

改进TLBO算法求解绿色零等待流水线调度问题

针对近年来严重的环境影响和越来越多的能量成本损耗所引发的绿色调度问题,提出了一种改进的基于"教"与"学"的优化算法,求解带序相关设置时间和释放时间的零等待流水线绿色调度问题,用于最小化能量成本。首先根据该调度问题的性质,设计了一种问题解的快速评价方法。其次在教师阶段,通过对成绩最差的学员或问题解进行Insert操作来提高种群的整体质量,同时提出一种自适应的教学因子,从而使算法的全局搜索能力得到改善。最后提出基于Insert邻域的策略来增强算法的局部搜索能力,有助于算法在全局和局部之间达到合理平衡。仿真实验和算法比较验证了该算法的有效性和鲁棒性。

一种新的结合奖励机制的ETLBO算法

通过对原ETLBO(elitist teaching-learning-based optimization)算法引入一种新的奖励机制,提出一种新的结合奖励机制的ETLBO-reward算法,并基于该算法提出一种简单自适应的精英个数算法RETLBO-reward,该算法保留了传统算法参数少、易实现、收敛快等优点,进一步提升了传统算法的收敛能力.对6个连续非线性优化问题的测试结果表明,这两种算法均具有良好的性能,求解效率较原ETLBO算法有明显提升.

基于IATLBO与稳态初值寻优的波动热工系统建模

针对人为选取热工系统稳态分量不准确,造成建模精度下降的问题,提出一种基于IATLBO(improved adaptive teaching-learning-based optimization algorithm)与稳态初值寻优的波动热工系统建模方法。方法将系统稳态分量与模型参数均看作未知量,依据智能优化算法寻优过程中所分配的系统稳态分量,动态的对系统输入输出数据进行稳态分量的剔除,从而有效的避免人为选取所产生的误差;寻优过程选取智能优化算法为教学优化算法,结合真实教学过程,对算法进行了改进,并应用标准测试函数进行仿真,验证了算法的高效性。对某波动热工系统进行建模,表明了上述方法的有效性。

改进的TLBO及其在自来水供水量预测中的应用

为准确预测城市自来水供水量,提出采用教与学优化算法(TLBO)优化的极限学习机预测方法。针对TLBO算法收敛精度低、易陷入局部最优的不足,提出一种改进的TLBO算法(ITLBO)。在ITLBO中,增加一个最差学生补习阶段,通过老师对该学生单独辅导或者采用一个反向学习策略快速提升学生成绩;在此基础上,采用一种干扰算子对老师进行扰动,增强种群跳出局部最优的动能;最后,将ITLBO算法用于优化调整极限学习机(ELM)模型的输入权值和隐层阈值参数,并构建ITLBO-ELM自来水供水量预测模型。将ITLBO-ELM模型用于上海市自来水供水量的预测实验,仿真结果表明该模型能够准确预测自来水供水量。

基于TLBO算法优化的球磨机FBEL控制方案研究

为了提高球磨机控制系统的稳定性和精度,首先将分数阶微积分引入大脑情感学习模型,并把误差信号的分数阶微积分的线性组合作为大脑情感学习模型的感官输入信号和奖励信号,构建一种优化的分数阶大脑情感学习模型参数的方法,然后采用教与学优化算法优化系统的各个参数,使得系统的各个参数更合理,提高了系统的精度,并通过仿真实验验证.仿真结果表明:与传统的一些算法相比较,该方法选取的参数精确度较高,能更快寻找到最优解,具有较好的鲁棒性.

基于WOA-TLBO算法的有源配电网故障区段定位

为提高有源配电网故障区段定位的精度和容错性能,构造了面向有源配电网的故障区段定位数学模型,在此基础上提出一种融合鲸鱼优化算法和教学优化算法的WOA-TLBO故障区段定位方法,充分利用了WOA的全局探索优势和TLBO的快速收敛优势。以含多个分布式电源的有源配电网为例进行了仿真测试,结果表明,所提故障区段定位方法能够在有源配电网发生短路故障时实现快速准确的区段定位,并具有良好的容错性。

基于HTLBO算法的舰载机机群机库维修任务调度

舰载机机库维修能力是制约舰载机机群可用度乃至出动、作战能力的关键要素,为提升舰载机机库维修效率并减轻传统人工制定维修计划的负担,研究了舰载机机群机库维修任务调度方法。首先,系统分析了舰载机机群机库维修任务调度的作业流程模式,基于维修资源约束,面向实际维修任务需求,提出了将舰载机机群波次可用度指标和机库维修人员负载均衡性指标作为分层优化目标,建立了舰载机机群机库维修任务调度模型。其次,将模型抽象为多技能资源受限项目调度问题,提出了基于资源约束推进的串行调度机制,并设计了混合教与学优化算法。最后,通过设计仿真案例和算法对比,验证了模型和算法的可行性与有效性,所提出的混合教与学算法具备更好的收敛速度和优化性能,有一定的工程借鉴意义。

改进TLBO优化人工鱼群算法实现FCM图像分割

FCM算法是一种比较流行的模糊聚类算法,将其运用到图像分割领域中,可以更加客观地对像素进行分类,达到较好的分割效果。但是传统的FCM算法存在对初始聚类中心较为依赖的问题,一般情况下,初始聚类中心是随机选取的,如果选择的不合适,则很容易导致算法得不到全局最优解。针对此问题,提出了改进方法,对人工鱼群算法(AFSA)进行改进,利用人工鱼群算法良好的全局搜索功能,同时融合改进的教学优化算法(TLBO),用于搜索FCM算法的初始聚类中心。通过实验证明,提出的算法不仅有效地改善了人工鱼群算法后期寻优速度慢、不精确的问题,并且与标准FCM算法和人工鱼群算法优化FCM算法相比,在迭代次数和运行时间方面,平均下降了14%~55%,分割正确率平均提高了7%~10%,从而在图像分割方面具有更快的分割速度和更优的分割效果。

Cost Effective Operating Strategy for Unit Commitment and Economic Dispatch of Thermal Power Plants with Cubic Cost Functions Using TLBO Algorithm

This paper deals with a Unit Commitment (UC) problem of a power plant aimed to find the optimal scheduling of the generating units involving cubic cost functions. The problem has non convex generator characteristics, which makes it very hard to handle the corresponding mathematical models. However, Teaching Learning Based Optimization (TLBO) has reached a high efficiency, in terms of solution accuracy and computing time for such non convex problems. Hence, TLBO is applied for scheduling of generators with higher order cost characteristics, and turns out to be computationally solvable. In particular, we represent a model that takes into account the accurate higher order generator cost functions along with ramp limits, and turns to be more general and efficient than those available in the literature. The behavior of the model is analyzed through proposed technique on modified IEEE-24 bus system.

基于ECTLBO-ELM模型的荷电状态估计

电池的荷电状态(SOC)表示电池的可用容量,是电池管理系统重要参数之一。以锂离子电池为例,准确的估计可以提高其性能。为了建立锂离子电池的精确计算模型,提出了一种基于增强混沌教与学优化算法(ECTLBO)优化极限学习机(ELM)的SOC估计模型(ECTLBO-ELM)。在ECTLBO-ELM模型中,一是利用增强混沌优化策略对班级中最优个体进行混沌搜索以增强TLBO算法的全局优化性能;二是采用改进的TLBO算法优化ELM的输入权值和隐含层偏差,提高其估计性能。利用某10AH的锰酸锂电池的三种不同倍率下的放电实验数据集对提出的算法进行测试,为了揭示所提方法的性能,将结果与标准ELM算法进行比较。结果表明,该方法能较好地估计SOC。