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Efficient Computation Offloading of IoT-Based Workflows Using Discrete Teaching Learning-Based Optimization 认领 引用 被引量：1
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作者 Mohamed K.Hussein Mohamed H.Mousa 《Computers, Materials & Continua》 SCIE EI 2022年第11期3685-3703,共19页
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
关键词 High-performance computing internet of things(IoT) mobile edge computing(MEC) workflows computation offloading teaching learning-based optimization
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An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem 认领 引用 被引量：11
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作者 Bingjie Li Guohua Wu +2 位作者 Yongming He Mingfeng Fan Witold Pedrycz 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第7期1115-1138,共24页
The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contribute... The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms. 展开更多
关键词 End-to-end approaches learning-based optimization(LBO)algorithms reinforcement learning step-by-step approaches vehicle routing problem(VRP)
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Noise-Averse and Profit-Desired Stochastic Multi-Product Disassembly Sequence Planning Problems Using Multi-Objective Group Teaching Optimization 认领 引用
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作者 Pei Liang Yaping Fu +2 位作者 Zhenyong Wu Kaizhou Gao Vedpal Arya 《Complex System Modeling and Simulation》 EI 2026年第1期1-23,共23页
Remanufacturing contributes to achieving economical,environmental,and social sustainability,and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from endof-life produc... Remanufacturing contributes to achieving economical,environmental,and social sustainability,and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from endof-life products.This research considers a multi-objective multi-product disassembly sequence planning problem under uncertain circumstances to realize a trade-off among economic,environmental,and social sustainability.Firstly,a multi-objective chance-constrained programming model is formulized to achieve maximal disassembly profit and minimal noise pollution while satisfying energy consumption requirements and obeying various complex product structures.Secondly,a multi-objective group teaching optimization algorithm combining a stochastic simulation approach is particularly devised to handle the problem.In the designed approach,problem-specific encoding and decoding methods are employed to represent and produce feasible solutions.The stochastic simulation approach is utilized to assess the feasibility and performance of the obtained solutions under uncertain environments.Rank and crowding distance approaches are introduced to realize ability grouping,namely,dividing the population into two groups.Precedence preserving crossover and mutation operators are separately utilized on the two groups to achieve population evolution,and an adaptive local search method is developed to enhance exploitation.Thirdly,comparison experiments on some real-world test problems with different scales are carried out.Through dissecting the experimental results with three performance metrics,it can be observed that the devised approach outperforms its competitors by 9.39%-10.00%,11.37%-59.86%,and 2.36%-7.73%regarding performance,respectively.The experimental results demonstrate the efficiency and excellence of the devised approach in providing high-quality disassembly schemes for managers and engineers. 展开更多
关键词 multi-product disassembly disassembly sequence planning sustainable development noise aversion multiobjective group teaching optimization algorithm
A novel improved teaching and learning-based-optimization algorithm and its application in a large-scale inventory control system 认领 引用
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作者 Zhixiang Chen 《International Journal of Intelligent Computing and Cybernetics》 EI 2023年第3期443-501,共59页
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
关键词 Teaching and learning-based optimization Group-individual multi-mode cooperation Performance-based group teaching Teacher self-learning Team learning
Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem 认领 引用 被引量：5
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作者 Nianbo Kang Zhonghua Miao +2 位作者 Quan-Ke Pan Weimin Li M.Fatih Tasgetiren 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第5期1249-1265,共17页
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 i... 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. 展开更多
关键词 genetic algorithm heuristic algorithm Multi-Weeding Robot Task Assignment(MWRTA) teaching optimization algorithm
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改进教学优化算法求解执行器配置与生产调度协同优化问题 认领 引用
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作者 谭伟华 吴亮红 +1 位作者 李哲 袁小芳 《控制理论与应用》 EI CAS CSCD 北大核心 2026年第4期865-873,共9页
在实际柔性作业车间中,执行器配置与生产调度的高效协同有利于提高生产决策的全局性,从而提升车间的柔性加工能力.针对执行器配置与生产调度的多目标协同优化问题,以最小化综合生产成本和完工时间为优化目标,构建了混合整数规划模型,使... 在实际柔性作业车间中,执行器配置与生产调度的高效协同有利于提高生产决策的全局性,从而提升车间的柔性加工能力.针对执行器配置与生产调度的多目标协同优化问题,以最小化综合生产成本和完工时间为优化目标,构建了混合整数规划模型,使得小规模问题通过可以Gurobi精确求解.本文提出了一种两阶段离散教学优化算法,设计了学习强度自适应调整方法和改进关键工序移动策略,以提升算法的效率和多目标平衡搜索能力.通过仿真实验,分析验证了所提协同优化方法的优越性和所提算法的有效性. 展开更多
关键词 柔性作业车间调度 资源配置 多目标优化 教学优化算法
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基于IFA-BP神经网络模型的变电站碳排放预测 认领 引用
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作者 王巍 李智威 +5 位作者 张赵阳 张洪 周蠡 王振 黄放 王灿 《广西师范大学学报（自然科学版）》 CAS 北大核心 2026年第2期103-114,共12页
针对现有变电站碳排放量预测模型存在考虑指标较少、数据更新慢等问题,本文提出一种基于改进萤火虫算法(improved firefly algorithm,IFA)优化反向传播(back propagation,BP)神经网络的变电站碳排放预测模型。首先,针对萤火虫算法(firef... 针对现有变电站碳排放量预测模型存在考虑指标较少、数据更新慢等问题,本文提出一种基于改进萤火虫算法(improved firefly algorithm,IFA)优化反向传播(back propagation,BP)神经网络的变电站碳排放预测模型。首先,针对萤火虫算法(firefly algorithm,FA)收敛速度过慢以及易陷入局部最优等问题,引入教与学因子,修改萤火虫位置更新过程,以提高群体适应度。其次,引入IFA算法对BP神经网络模型进行超参数寻优,并构建IFA-BP神经网络预测模型。然后,基于CRITIC法筛选预测模型输入层的关键碳排放指标。最后,利用训练集数据训练预测模型,基于训练好的模型对变电站的碳排放量进行预测。仿真结果表明,相较于3种对比方案,本文IFA-BP神经网络预测模型分别在均方根误差(root mean square error,RMSE)上降低59.61%、15.77%和26.65%,在决定系数(coefficient of determination,R2)上提高5.66%、1.46%和1.15%,充分验证了本文所提变电站碳排放预测模型的可行性与优越性。 展开更多
关键词 碳排放 变电站 改进萤火虫算法 BP神经网络 教与学因子
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面向多星协同任务规划的自适应教学优化算法 认领 引用
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作者 刘严 刘国华 +1 位作者 温治江 胡海鹰 《中国空间科学技术（中英文）》 CSCD 北大核心 2026年第1期73-82,共10页
针对低轨大规模星座协同观测任务规划中动态适应性不足的问题,提出一种自适应教学优化算法。在教学优化算法的教与学框架下通过引入自适应机制和混合学习策略,采用时变教学因子和精英导向机制优化教阶段,采用混合学习策略改进学阶段,动... 针对低轨大规模星座协同观测任务规划中动态适应性不足的问题,提出一种自适应教学优化算法。在教学优化算法的教与学框架下通过引入自适应机制和混合学习策略,采用时变教学因子和精英导向机制优化教阶段,采用混合学习策略改进学阶段,动态平衡全局探索与局部探索能力。通过仿真验证,自适应教学优化算法在任务完成率和运行时间上均优于改进遗传算法和改进差分教学优化算法,在大规模高复杂度多星协同任务场景下相对基线算法任务完成率可提升6%与16%,适用于高维离散优化问题。算法在任务完成率、运行效率及鲁棒性上具有综合优势,可应用于低轨星座协同观测任务。 展开更多
关键词 敏捷卫星 任务规划 多点目标 对地观测 在轨规划 教学优化算法
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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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Timetabling optimization of classrooms and self-study rooms in university teaching buildings based on the building controls virtual test bed platform considering energy efficiency 认领 引用 被引量：4
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作者 Yanfeng Liu Hui Ming +2 位作者 Xi Luo Liang Hu Yongkai Sun 《Building Simulation》 SCIE EI CSCD 2023年第2期263-277,共15页
The energy consumption of a teaching building can be effectively reduced by timetable optimization.However,in most studies that explore methods to reduce building energy consumption by course timetable optimization,se... The energy consumption of a teaching building can be effectively reduced by timetable optimization.However,in most studies that explore methods to reduce building energy consumption by course timetable optimization,self-study activities are not considered.In this study,an MATLAB-EnergyPlus joint simulation model was constructed based on the Building Controls Virtual Test Bed platform to reduce building energy consumption by optimizing the course schedule and opening strategy of self-study rooms in a holistic way.The following results were obtained by taking a university in Xi’an as an example:(1)The energy saving percentages obtained by timetabling optimization during the heating season examination week,heating season non-examination week,cooling season examination week,and cooling season non-examination week are 35%,29.4%,13.4%,and 13.4%,respectively.(2)Regarding the temporal arrangement,most courses are scheduled in the morning during the cooling season and afternoon during the heating season.Regarding the spatial arrangement,most courses are arranged in the central section of the middle floors of the building.(3)During the heating season,the additional building energy consumption incurred by the opening of self-study rooms decreases when duty heating temperature increases. 展开更多
关键词 timetabling optimization university teaching buildings energy efficiency Building Controls Virtual Test Bed platform genetic algorithm
A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine 认领 引用 被引量：9
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作者 孙泽文 顾幸生 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第8期1779-1788,共10页
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 wor... 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. 展开更多
关键词 hybrid estimation of distribution algorithm teaching learning based optimization strategy hybrid flow shop unrelated parallel machine scheduling
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面向新质人才培养的生成式AI智慧课程教学模式研究——以“人工智能算法优化”课程为例 认领 引用
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作者 方怡静 刘金平 刘晨亮 《湖南工程学院学报（社会科学版）》 2026年第2期105-112,共8页
针对人工智能专业课程中理论教学与实践应用衔接不足、学习支持方式单一及评价反馈滞后等问题,以“人工智能算法优化”课程为载体,构建生成式AI赋能的智慧课程教学模式。该模式围绕“以用促学、以创促学、以评促学”的教学理念,形成涵... 针对人工智能专业课程中理论教学与实践应用衔接不足、学习支持方式单一及评价反馈滞后等问题,以“人工智能算法优化”课程为载体,构建生成式AI赋能的智慧课程教学模式。该模式围绕“以用促学、以创促学、以评促学”的教学理念,形成涵盖设计理念、“教—学—评”一体化框架、实施路径与运行机制的整体方案。通过引入人机协同学习支持和过程数据驱动评价,推动课程教学由知识传递向能力生成转型,构建的教学框架可为人工智能类课程及新工科课程教学改革提供参考。 展开更多
关键词 生成式人工智能 新质人才培养 智慧课程 人工智能算法优化 教学模式改革
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“算法设计与分析”课程体系构建与教学模式创新研究 认领 引用
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作者 韩军 邓婷 +1 位作者 林学练 王雪飞 《教育教学论坛》 2026年第12期1-4,共4页
针对“算法设计与分析”课程的教学体系进行了系统优化,旨在提升学生的算法设计、理论分析和工程实践能力。传统的“算法设计与分析”课程教学以理论推导为主,实践训练不足,难以满足现代计算机科学教育的需求。为此,针对“算法设计与分... 针对“算法设计与分析”课程的教学体系进行了系统优化,旨在提升学生的算法设计、理论分析和工程实践能力。传统的“算法设计与分析”课程教学以理论推导为主,实践训练不足,难以满足现代计算机科学教育的需求。为此,针对“算法设计与分析”课程构建了一套系统性、实践性和前沿性兼备的教学体系。结合实验驱动、项目制学习、在线评测等多元教学方法,优化知识体系与考核方式,确保学生能够在实际应用中高效设计和优化算法。该教学改革方案有效提升了学生的理论掌握、工程应用和算法创新等能力,为现代计算机教育提供了新的思路和实践路径。 展开更多
关键词 算法设计与分析 课程体系优化 前沿算法 工程应用
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多角度需求下新媒体教学资源交互式双向调度 认领 引用
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作者 李晗晗 熊忠辉 《江苏理工学院学报》 2026年第2期59-66,112,共8页
在以静态、单向为主的传统新媒体教学资源调度模式中,系统端可能因并发需求影响而出现资源负载过重的情况,导致调度的资源难以有效满足用户的需求。针对这一问题,提出一种基于多角度需求下的新媒体教学资源调度方法,将资源调度需求与系... 在以静态、单向为主的传统新媒体教学资源调度模式中,系统端可能因并发需求影响而出现资源负载过重的情况,导致调度的资源难以有效满足用户的需求。针对这一问题,提出一种基于多角度需求下的新媒体教学资源调度方法,将资源调度需求与系统方和用户方(资源需求方)深度关联,构建以最大化新媒体教学资源调度效益为目标的优化函数。创新性地将教学资源的交互式双向调度问题视为组合优化问题,结合遗传算法对该优化问题进行求解。具体做法是将系统方和用户方的资源分配方案编码为一条染色体,并引入线性缩放操作以防止算法过早收敛,最终筛选出综合资源调度效能最高的个体作为最优解,获得满足组合优化目标的调度结果,实现资源的交互式双向调度。实验结果表明:所设计的方法产生的综合调度效能始终保持较高水平,效能最低值为88.32%;在不同轮次迭代中,响应时延最大值为0.36 s,丢包率最大值为0.18%,显示出较强的资源利用能力与系统可扩展性。 展开更多
关键词 新媒体教学资源 遗传算法 组合优化 双向调度
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基于教学优化算法的压力容器结构优化研究 认领 引用
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作者 左遥远 王令杰 《价值工程》 2026年第14期152-154,共3页
压力容器轻量化设计对降低成本与能耗至关重要。针对该问题多约束、非线性的特点,提出一种基于教学优化算法的结构优化方法。以筒体和封头总质量最小为目标,建立包含应力、稳定性等的约束模型。通过算法的“教”与“学”机制,高效寻求... 压力容器轻量化设计对降低成本与能耗至关重要。针对该问题多约束、非线性的特点,提出一种基于教学优化算法的结构优化方法。以筒体和封头总质量最小为目标,建立包含应力、稳定性等的约束模型。通过算法的“教”与“学”机制,高效寻求最优解。与遗传算法、粒子群算法的对比结果表明,该方法在求解精度和收敛稳定性方面均具优势,能为压力容器轻量化设计提供有效方案。 展开更多
关键词 压力容器结构优化 教学优化算法 轻量化设计
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基于项目驱动的《最优化算法》课程教学改革与实践——以“智能配送系统优化设计”为例 认领 引用
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作者 田增娴 何冠霖 《教育教学研究前沿》 2026年第2期108-110,共3页
《最优化算法》是计算机科学与运筹学领域的核心课程,传统教学普遍存在理论抽象、实践薄弱、知识碎片化等问题。为提升学生综合应用与创新能力,本研究基于项目驱动学习(Project-Based Learning,PBL)与成果导向教育(Outcome-Based Educat... 《最优化算法》是计算机科学与运筹学领域的核心课程,传统教学普遍存在理论抽象、实践薄弱、知识碎片化等问题。为提升学生综合应用与创新能力,本研究基于项目驱动学习(Project-Based Learning,PBL)与成果导向教育(Outcome-Based Education,OBE)理念,设计了以“智能配送系统优化设计”为载体的贯穿式教学项目。该项目以城市快递“最后一公里”配送为真实场景,将线性规划、整数规划、进化算法及多目标优化等核心内容系统整合。通过“建模—求解—分析—优化”的递进式任务,学生完成从算法理解到系统实现的完整过程。教学实践结果表明,该教学模式显著提升了学生的问题建模、算法实现与系统思维能力,为最优化类课程教学改革提供了可推广的范式。 展开更多
关键词 项目驱动学习(PBL) OBE 教学改革 《最优化算法》 智能配送系统
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基于Apriori算法与OBE理念的课程成绩关联分析及教学优化研究——以保险学专业为例 认领 引用
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作者 苏琪 阿布都瓦力·艾百 孙昕 《办公自动化》 2026年第12期36-39,共4页
成果导向教育(OBE)强调以学生学习成果为导向进行教学设计,而课程成绩关联分析可为OBE实施提供数据支持。随着教育大数据技术的快速发展,数据驱动的教学决策正成为高等教育改革的重要方向。本研究利用Apriori算法对新疆某财经类高校保... 成果导向教育(OBE)强调以学生学习成果为导向进行教学设计,而课程成绩关联分析可为OBE实施提供数据支持。随着教育大数据技术的快速发展,数据驱动的教学决策正成为高等教育改革的重要方向。本研究利用Apriori算法对新疆某财经类高校保险专业培养方案中6门专业核心课程的共计140名学生的课程成绩数据,进行关联规则挖掘和数据分析研究。通过构建"数据挖掘-教学诊断-优化实施"的闭环模型,结合OBE(Outcome Based Education)教育理念,运用教育测量学理论与机器学习方法交叉验证,从课程体系重构、教学策略优化两个方面提出OBE理念下的保险专业培养方案改进建议。研究创新性地建立课程关联强度量化指标体系,为保险学专业OBE理念下的培养方案制定提供数据参考。 展开更多
关键词 Apriori算法 OBE理念 保险学专业 成绩关联分析 教学优化 教育数据挖掘
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基于深度学习的个性化教学推荐算法优化 认领 引用
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作者 黄佳莉 《移动信息》 2026年第6期277-279,共3页
针对个性化教学推荐中存在的推荐精度不高、冷启动处理不足以及学习行为特征挖掘不充分等问题,文中提出了一种基于深度学习的推荐算法优化方法。该方法构建了教学推荐任务建模框架,引入了深度神经网络及注意力机制,实现了多维特征的融合... 针对个性化教学推荐中存在的推荐精度不高、冷启动处理不足以及学习行为特征挖掘不充分等问题,文中提出了一种基于深度学习的推荐算法优化方法。该方法构建了教学推荐任务建模框架,引入了深度神经网络及注意力机制,实现了多维特征的融合,从而提升了学生学习偏好的动态建模能力。实验结果表明,所提方法有效增强了个性化推荐的适应性与教学支持效果。 展开更多
关键词 深度学习 个性化教学 推荐算法优化
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基于深度BPR+算法的不完全信息博弈环境下教学策略优化研究 认领 引用
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作者 吕杰 《成都工业学院学报》 2026年第1期104-112,共9页
针对传统教育模式中策略优化效率低下和缺乏个性化学习推荐的挑战,提出一种基于深度BPR+算法的教学策略优化方法,旨在提升不完全信息博弈环境下的教育质量。通过构建不完全信息博弈模型,并将其与深度BPR+算法集成,所提出的模型能够有效... 针对传统教育模式中策略优化效率低下和缺乏个性化学习推荐的挑战,提出一种基于深度BPR+算法的教学策略优化方法,旨在提升不完全信息博弈环境下的教育质量。通过构建不完全信息博弈模型,并将其与深度BPR+算法集成,所提出的模型能够有效减轻信息不完整对博弈设置的影响。实验结果表明,深度BPR+算法在多项关键指标上显著优于传统方法:策略优化准确率达到85%,推荐覆盖率为92%,准确率、召回率和F1分别为87%、80%、0.835。此外,个性化推荐准确率、学生反馈满意度和用户黏性分别达到90%、95%、92%。所提出的模型在改善教学成果、培养学生自主性和推进个性化教学方法方面具有显著优势,为教育领域的质量提升提供了新的理论和实践支持。 展开更多
关键词 深度BPR+算法 非完全信息博弈 教学策略优化 个性化学习建议 教育质量提升
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数字孪生技术在数字化教学实验模拟与实践教学创新中的应用探索 认领 引用 被引量：2
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作者 姜齐艳 吕丹 《自动化与仪器仪表》 2025年第11期197-200,205,共4页
为提高智能制造实验教学过程中的安全性和创新性,提出设计一个基于数字孪生技术的智能制造实验教学平台。首先,利用数字孪生技术将智能制造生产线映射至虚拟空间,构建智能制造数字孪生实验教学平台;然后在平台数字孪生层中引入改进ABC-... 为提高智能制造实验教学过程中的安全性和创新性,提出设计一个基于数字孪生技术的智能制造实验教学平台。首先,利用数字孪生技术将智能制造生产线映射至虚拟空间,构建智能制造数字孪生实验教学平台;然后在平台数字孪生层中引入改进ABC-PID控制模型,通过该模型实现环境动态变化控制;最后利用虚拟空间仿真运行与物理空间实践操作相结合,得到两个阶段的实验教学模式。实验结果表明,本模型的控制精度为98.76%,控制时长仅为1.42 s,明显优于传统的BOA-PID控制模型和PSO-PID控制模型。模型应用可知,搭建的智能制造数字孪生实验教学平台可实现数字化教学实验模拟,智能制造实验教学的安全性和创新性显著提升,实验教学质量和效率进一步提高,数字化实验模拟教学成本明显下降,满足实际应用需求。 展开更多
关键词 数字孪生技术 实验教学 PID控制 ABC优化算法 虚拟仿真
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