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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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Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN 认领 引用
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作者 M.Manikandan S.Sakthivel V.Vivekanandhan 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3169-3185,共17页
Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node ... Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms.The efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors.Network management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of services.The topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process effectively.More solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still exists.This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks.The memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor networks.The proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc. 展开更多
关键词 Wireless sensor networks topology clustering memetic algorithm adaptive hill climbing algorithm network management energy consumption throughput
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AdaptiveMulti-Objective EnergyManagement Strategy Considering the Differentiated Demands of Distribution Networks with a High Proportion of New-Generation Sources and Loads 认领 引用 被引量：1
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作者 Huang Tan Haibo Yu +2 位作者 Tianyang Chen Hanjun Deng Yetong Hu 《Energy Engineering》 EI 2025年第5期1949-1973,共25页
With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have ev... With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy. 展开更多
关键词 High-proportion new-type source-loads multi-dimensional scenario classification clustering algorithms fuzzy logic control adaptive multi-objective energy management
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Hybrid Clustering Algorithms with GRASP to Construct an Initial Solution for the MVPPDP 认领 引用
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作者 Abeer I.Alhujaylan Manar I.Hosny 《Computers, Materials & Continua》 SCIE EI 2020年第3期1025-1051,共27页
Mobile commerce(m-commerce)contributes to increasing the popularity of electronic commerce(e-commerce),allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any time.As demand for e-com... Mobile commerce(m-commerce)contributes to increasing the popularity of electronic commerce(e-commerce),allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any time.As demand for e-commerce increases tremendously,the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer satisfaction.One important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem(MVPPDP),where a selected set of pickup and delivery customers need to be served within certain allowed trip time.In this paper,we proposed hybrid clustering algorithms with the greedy randomised adaptive search procedure(GRASP)to construct an initial solution for the MVPPDP.Our approaches first cluster the search space in order to reduce its dimensionality,then use GRASP to build routes for each cluster.We compared our results with state-of-the-art construction heuristics that have been used to construct initial solutions to this problem.Experimental results show that our proposed algorithms contribute to achieving excellent performance in terms of both quality of solutions and processing time. 展开更多
关键词 Multi-vehicle profitable pickup and delivery problem K-means clustering algorithm ant colony optimisation greedy randomised adaptive search procedure metaheuristic algorithms
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Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud 认领 引用
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作者 I.Mettildha Mary K.Karuppasamy 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期2667-2685,共19页
CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferrin... CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used. 展开更多
关键词 Cloud analytics machine learning ensemble learning distributed learning clustering classification auto selection auto tuning decision feedback cloud DevOps feature selection wrapper feature selection Adaptive Kernel Firefly Algorithm(AKFA) Q learning
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Application of a New Fuzzy Clustering Algorithm in Intrusion Detection 认领 引用 被引量：1
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作者 WU Tiefeng 《现代电子技术》 2008年第4期100-102,共3页
This paper presents a new Section Set Adaptive FCM algorithm.The algorithm solved the shortcomings of local optimality,unsure classification and clustering numbers ascertained previously.And it improved on the archite... This paper presents a new Section Set Adaptive FCM algorithm.The algorithm solved the shortcomings of local optimality,unsure classification and clustering numbers ascertained previously.And it improved on the architecture of FCM al- gorithm,enhanced the analysis for effective clustering.During the clustering processing,it may adjust clustering numbers dy- namically.Finally,it used the method of section set decreasing the time of classification.By experiments,the algorithm can im- prove dependability of clustering and correctness of classification. 展开更多
关键词 模糊聚类算法 干扰检测 计算机技术 FCM
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基于自适应Mean-shift算法的高光谱影像分割 认领 引用
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作者 刘坤 《测绘标准化》 2026年第2期47-54,共8页
本文针对高光谱影像分割维数高、数据量大的特点,将网格聚类算法和Mean-shift密度聚类算法结合,提出一种自适应Mean-shift聚类算法。该算法首先通过网格法粗分割,将高光谱影像划分为多个密度聚类,其次针对每个密度聚类进行Mean-shift带... 本文针对高光谱影像分割维数高、数据量大的特点,将网格聚类算法和Mean-shift密度聚类算法结合,提出一种自适应Mean-shift聚类算法。该算法首先通过网格法粗分割,将高光谱影像划分为多个密度聚类,其次针对每个密度聚类进行Mean-shift带宽估计,最后根据逐个密度聚类进行Mean-shift迭代运算实现影像分割。本文以江苏宜兴地区的实用型模块化成像光谱仪一号(OMIS-1)高光谱数据为例进行实验,实验结果表明,该算法可以精准地分割出影像中的不同地物类型,并可大幅提升运算效率。此外,该算法融合了网格聚类算法的高效性和Mean-shift聚类算法的适配任意形状聚类、抗噪性强、分割边界平滑的优点,可为高光谱影像的快速、精准分割提供有效的技术参考与借鉴。 展开更多
关键词 高光谱影像分割 自适应Mean-shift聚类算法 网格聚类算法
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Design of Clustering Techniques in Cognitive Radio Sensor Networks 认领 引用
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作者 R.Ganesh Babu D.Hemanand +1 位作者 V.Amudha S.Sugumaran 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期441-456,共16页
In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Us... In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity. 展开更多
关键词 Adaptive swarm distributed clustering cognitive radio clustering algorithm distributed swarm intelligent energy efficient distributed cluster-based sensing multi modal optimization
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Identification and novel adaptive fuzzy control of nonlinear system for PEMFC stack 认领 引用
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作者 卫东 许宏 朱新坚 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2006年第2期186-192,共7页
The operating temperature of a proton exchange membrane fuel cell stack is a very important control parameter. It should be controlled within a specific range, however, most of existing PEMFC mathematical models are t... The operating temperature of a proton exchange membrane fuel cell stack is a very important control parameter. It should be controlled within a specific range, however, most of existing PEMFC mathematical models are too complicated to be effectively applied to on-line control. In this paper, input-output data and operating experiences will be used to establish PEMFC stack model and operating temperature control system. An adaptive learning algorithm and a nearest-neighbor clustering algorithm are applied to regulate the parameters and fuzzy rules so that the model and the control system are able to obtain higher accuracy. In the end, the simulation and the experimental results are presented and compared with traditional PID and fuzzy control algorithms. 展开更多
关键词 proton exchange membrane fuel cell （PEMFC） adaptive neural-networks fuzzy infer system （ ANFIS） adaptive neural-network learning algorithm （ANA） nearest-neighbor clustering algorithm （NCA）
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An Adaptive Non-Linear Map and Its Application 认领 引用
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作者 YAN Xuefeng 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第3期572-576,共5页
A novel adaptive non-linear mapping （ANLM）, integrating an adaptive mapping error （AME） with a chaosgenetic algorithm （CGA） including chaotic variable, was proposed to overcome the deficiencies of non-linear map... A novel adaptive non-linear mapping （ANLM）, integrating an adaptive mapping error （AME） with a chaosgenetic algorithm （CGA） including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping （NLM）. The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodieally in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results demonstrated that the whole performance of CGA is better than that of traditional genetic algorithm. Furthermore, a typical example of mapping eight-dimenslonal olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM. 展开更多
关键词 adaptive non-linear map topology- preserving mapping error chaotic variable genetic algorithm cluster
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Detection of Underwater Objects by Adaptive Threshold FCM Based on Frequency Domain and Time Domain 认领 引用
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作者 Xingmei Wang Guangyu Liu +1 位作者 Lin Li Shouxuan Jiang 《国际计算机前沿大会会议论文集》 EI 2015年第1期57-58,共2页
According to the characteristics of sonar image data with big data feature, In order to accurately detect underwater objects of sonar image, a novel adaptive threshold FCM (Fuzzy Clustering Algorithm, FCM) based on fr... According to the characteristics of sonar image data with big data feature, In order to accurately detect underwater objects of sonar image, a novel adaptive threshold FCM (Fuzzy Clustering Algorithm, FCM) based on frequency domain and time domain is proposed. Based on the relationship between sonar image data and big data, Firstly, wavelet de-noising method is used to smooth noise. After de-noising, the sonar image is blocked and each sub-block region is processed by two-dimensional discrete Fourier transform, their maximum amplitude spectrum used as frequency domain character, then time domain of mean and standard deviation, frequency domain of maximum amplitude spectrum are taken for character to complete block k-means clustering, the initial clustering center is determined, after that made use of FCM on sonar image detection, based on clustered image, adaptive threshold is constructed by the distribution of sonar image sea-bottom reverberation region, and final detection results of sonar image are completed. The comparison different experiments demonstrate that the proposed algorithm get good detection precision and adaptability. 展开更多
关键词 Sonar image Character frequency domain Block k-means clustering Fuzzy clustering algorithm Adaptive threshold
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面向建筑群巡检的无人机覆盖路径规划策略研究 认领 引用 被引量：1
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作者 郑建校 谭吉祥 +2 位作者 陈刚 张雨 黄子健 《仪器仪表学报》 EI CAS CSCD 北大核心 2026年第3期141-157,共17页
针对建筑群场景下无人机智能化巡检中覆盖约束下的观察点精简与路径质量优化问题,提出了一种基于密度自适应聚类与分层能耗优化的两阶段规划策略。首先,在观察点生成阶段,以建筑群的三角网格模型为基础,提出了一种密度自适应聚类(DAC)... 针对建筑群场景下无人机智能化巡检中覆盖约束下的观察点精简与路径质量优化问题,提出了一种基于密度自适应聚类与分层能耗优化的两阶段规划策略。首先,在观察点生成阶段,以建筑群的三角网格模型为基础,提出了一种密度自适应聚类(DAC)算法并结合基于Pareto前沿的包含视点数量、覆盖冗余以及观测质量的多目标全局优化策略,实现了观察点的高效生成;其次,在路径规划阶段,构建了综合考虑路径长度、转向角度及升降高度的多因素目标函数,并提出一种基于分层能耗模型的改进LKH算法(S-LKH)求解观察点的最优遍历顺序。为了验证所提算法的优越性,仿真实验在两种不同规模的建筑模型上开展,其结果表明:相较于体素膨胀与随机键遗传算法、两阶段优化方法和模糊聚类法,所提方法在来福士建筑群模型上视点数相较于体素膨胀与随机键遗传算法、两阶段优化方法和模糊聚类法分别减少45.10%、14.07%和27.78%;其S-LKH算法求解的目标函数值相较于LKH算法、可变策略强化算法、灰狼差分进化混合算法、多策略融合的差分进化算法和球面向量粒子群算法分别下降14.70%、6.86%、15.30%、20.89%和13.82%,路径上的最大转向角度和最大升降高度最高分别下降11.68%和52.84%,平均转向角度和平均升降高度最高分别下降10.08%和22.82%。最后,通过模拟与实地飞行验证,进一步证明了该方法的有效性与工程可行性。 展开更多
关键词 覆盖路径规划策略 无人机 密度自适应聚类 分层能耗模型 LKH算法
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基于改进LEACH的多机器人自组网通信路由算法研究 认领 引用
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作者 黄红艳 张志平 +1 位作者 张星 孙科学 《机械设计与制造》 北大核心 2026年第6期189-193,共5页
针对现有工业机器人自组网通信路由算法在智能化仓储中存在的效率低和局限性等问题,在工业机器人自组网方案的基础上,提出了一种改进的低功耗自适应集簇分层型算法用于工业机器人通信路由。根据仓储机器人的特点,从簇区划分、簇头选择... 针对现有工业机器人自组网通信路由算法在智能化仓储中存在的效率低和局限性等问题,在工业机器人自组网方案的基础上,提出了一种改进的低功耗自适应集簇分层型算法用于工业机器人通信路由。根据仓储机器人的特点,从簇区划分、簇头选择和动态维护三个方面对低功耗自适应集簇分层型算法进行了改进。通过试验对多种情况进行对比分析,验证了该方法的可行性。结果表明,与传统的路由策略相比,该方法能有效地降低整体损耗,更好地平衡能量耗与通信延迟之间的关系。为机器人通信系统的发展提供一定的参考。 展开更多
关键词 工业机器人 智能化仓储 通信网络 路由算法 低功耗自适应集簇分层型算法 降低综合损耗
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基于自适应精英变异优化算法的微电网群调度研究 认领 引用
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作者 唐杰 刘凯 +2 位作者 邵武 杨海能 易资兴 《邵阳学院学报（自然科学版）》 2026年第1期31-40,共10页
“双碳”目标下分布式能源高渗透的微电网群经济优化调度中,非线性优化问题导致的传统算法易陷入局部最优,现有智能算法在收敛速度与寻优精度方面仍可进一步改善。提出一种自适应精英变异优化算法(adaptive elite mutation optimization... “双碳”目标下分布式能源高渗透的微电网群经济优化调度中,非线性优化问题导致的传统算法易陷入局部最优,现有智能算法在收敛速度与寻优精度方面仍可进一步改善。提出一种自适应精英变异优化算法(adaptive elite mutation optimization algorithm,AEMOA),以自适应权重动态平衡全局探索与局部开发,借助精英反向学习扩展搜索空间并提升寻优广度,再通过柯西变异与高斯变异的多尺度协同增强全局跳脱能力与局部微调精度。将其应用于含96时段的微电网群调度模型仿真验证,并与遗传算法(genetic algorithm,GA)、模拟退火算法(simulated annealing,SA)等经典算法进行对比分析,AEMOA在收敛速度和寻优精度方面都得到提升。 展开更多
关键词 微电网群 优化调度 自适应精英变异优化算法 96时段
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面向约束优化问题的聚类多目标狼群算法 认领 引用 被引量：1
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作者 吴莉娟 吕莉 +2 位作者 肖人彬 吴烈阳 王晖 《信息与控制》 CSCD 北大核心 2026年第1期100-115,149,共16页
针对多目标狼群算法在寻优过程中存在的多样性不足、难以摆脱局部最优的问题,提出了一种面向约束优化问题的聚类多目标狼群算法(CMOWPA-C)。首先,通过融合自适应惩罚与自适应权衡模型,提出了一种将约束问题转化为无约束问题的新方法。然... 针对多目标狼群算法在寻优过程中存在的多样性不足、难以摆脱局部最优的问题,提出了一种面向约束优化问题的聚类多目标狼群算法(CMOWPA-C)。首先,通过融合自适应惩罚与自适应权衡模型,提出了一种将约束问题转化为无约束问题的新方法。然后,引入随机扰动因子,优化种群的移动步长,防止种群陷入局部最优。最后,采用K均值聚类算法对种群分组,根据种群距簇心的距离将种群划分为不同的类簇,确保每个簇心周围都有个体与之关联,增加种群的多样性。为验证算法性能,在基准测试问题上与9种新兴算法进行了比较,并在实际约束问题上与9种约束多目标进化算法进行了比较。结果表明,CMOWPA-C的多样性显著提升,且能有效地避免局部最优。 展开更多
关键词 多目标狼群算法 约束优化 随机扰动因子 聚类 自适应惩罚 自适应权衡模型
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基于CEEMDAN-DBO-VMD-TCN-BiGRU的短期风电功率预测 认领 引用 被引量：1
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作者 陈旭东 卞礼杰 +3 位作者 马刚 陈浩 詹孝升 彭乐瑶 《综合智慧能源》 CAS 2026年第1期13-22,共10页
提升风电功率预测的准确性对于保障电网安全与稳定运行至关重要。然而,风电具有高度的随机性和波动性,传统预测方法在特征提取和建模能力方面存在不足。为此,提出一种融合完全自适应噪声集合经验模态分解(CEEMDAN)、蜣螂优化(DBO)算法... 提升风电功率预测的准确性对于保障电网安全与稳定运行至关重要。然而,风电具有高度的随机性和波动性,传统预测方法在特征提取和建模能力方面存在不足。为此,提出一种融合完全自适应噪声集合经验模态分解(CEEMDAN)、蜣螂优化(DBO)算法、变分模态分解(VMD)、时间卷积网络(TCN)与双向门控循环单元(BiGRU)的短期风电功率预测模型CEEMDAN-DBO-VMD-TCN-BiGRU。利用CEEMDAN对原始风电功率数据进行分解,提取内在模态函数(IMF)以捕捉时间序列的关键特征;通过样本熵与K-means聚类将IMF划分为高频、中频和低频分量,选取高频分量采用DBO优化的VMD进行二次分解,以提高特征提取效果并降低计算复杂度;所有分量经归一化处理后输入TCN-BiGRU组合模型进行预测,各分量预测结果经叠加与反归一化处理获得最终预测值。试验结果显示,相较于对比模型,该模型的预测精度最优,验证了所提模型的有效性、稳定性和应用潜力。 展开更多
关键词 风电功率预测 完全自适应噪声集合经验模态分解 蜣螂优化算法 变分模态分解 样本熵 K-means聚类 时间卷积网络 双向门控循环单元
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基于熵权利用率与预测算法的Kubernetes弹性伸缩优化研究 认领 引用
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作者 宋哲代 朱金荣 +1 位作者 梁琛悦 程心雨 《计算机工程》 CAS CSCD 北大核心 2026年第4期349-357,共9页
为解决Kubernetes内置的弹性伸缩策略衡量指标单一、反应滞后和资源利用效率低的问题,提出一种熵权利用率复合算法结合预测模型的改进弹性伸缩策略。熵权利用率复合算法通过关注多种指标的资源利用率在不同节点上的分布差异(信息熵权法... 为解决Kubernetes内置的弹性伸缩策略衡量指标单一、反应滞后和资源利用效率低的问题,提出一种熵权利用率复合算法结合预测模型的改进弹性伸缩策略。熵权利用率复合算法通过关注多种指标的资源利用率在不同节点上的分布差异(信息熵权法)和整体趋势(平均利用率权重法),计算Kubernetes集群的综合负载值,从而解决衡量指标单一的问题。构建自适应变分模态分解(AVMD)算法结合基于注意力机制增强的长短期记忆(Attention Mechanism-based LSTM)算法的预测模型,通过预测负载变化以解决反应滞后和资源利用率低的问题。该模型根据预测的负载值,在高流量初期促使系统快速响应进行扩容,流量结束后迅速缩容以节约资源。实验结果表明,与Kubernetes伸缩策略相比,改进弹性伸缩策略在突发流量前期,请求响应时间降低了52%,在流量结束后快速缩容释放资源,具有较高的实际应用价值。 展开更多
关键词 Kubernetes集群 熵权利用率复合算法 自适应变分模态分解算法 长短期记忆算法 负载预测
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聚类驱动交叉遗传算法的机器人路径规划 认领 引用
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作者 常美玉 王天昊 +1 位作者 邢轩瑀 董华军 《计算机工程与应用》 EI CSCD 北大核心 2026年第11期104-114,共11页
传统遗传算法在机器人路径规划中面临诸多挑战,尤其在复杂静态环境中存在收敛速度慢、参数敏感性强、计算效率低及路径平滑性不足等问题,现有改进算法多聚焦单一性能指标优化,难以实现综合性能均衡提升。为此,提出一种聚类驱动交叉优化... 传统遗传算法在机器人路径规划中面临诸多挑战,尤其在复杂静态环境中存在收敛速度慢、参数敏感性强、计算效率低及路径平滑性不足等问题,现有改进算法多聚焦单一性能指标优化,难以实现综合性能均衡提升。为此,提出一种聚类驱动交叉优化算法。引入A*算法生成启发式初始种群,提高全局搜索能力及效率;依据基因库思想,设计动态自适应进化参数调节机制实时调整种群状态,采用聚类驱动策略将种群分层,族内使用算术交叉,跨族使用模拟二进制交叉,同时实施局部扰动变异算子,在最优区域增强搜索多样性;采用三次B样条曲线对最终路径进行几何重构,显著提升路径平滑度。仿真实验表明,所提出的算法在大规模栅格地图中的综合性能:收敛速度、路径长度、计算效率、路径平滑性均体现出了有效性和优越性,为移动机器人在复杂静态环境中的全局路径规划提供了可靠的技术支撑。 展开更多
关键词 机器人路径规划 遗传算法 聚类驱动交叉 自适应 三次B样条曲线
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面向无人机自组网的多信道自适应节点度差分簇算法 认领 引用
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作者 张力丹 王海军 +3 位作者 张姣 马东堂 周力 魏急波 《国防科技大学学报》 EI CAS CSCD 北大核心 2026年第4期97-106,共10页
为解决在频谱竞争激烈或者电磁环境多变的场景下,无人机节点因可用信道差异面临在同一信道上组网困难的问题,提出一种基于自适应节点度差的多信道无人机自组网分簇算法。该算法在基于模块度优化的层次聚类算法基础上,将面向多信道的自... 为解决在频谱竞争激烈或者电磁环境多变的场景下,无人机节点因可用信道差异面临在同一信道上组网困难的问题,提出一种基于自适应节点度差的多信道无人机自组网分簇算法。该算法在基于模块度优化的层次聚类算法基础上,将面向多信道的自适应节点度差引入节点相似度计算中,通过最大化网络模块度函数对大规模无人机节点进行分簇,并基于Bianchi模型对网络吞吐量进行分析。仿真结果表明,所提算法相比Fast Unfolding、JS_CNC和HVC_MCNC等算法能形成分簇更均衡的拓扑结构,有效提升了网络吞吐量。 展开更多
关键词 大规模网络 多信道 无人机自组网 分簇算法 自适应节点度差
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基于FCM聚类和SSA优化的二次分解光伏发电功率预测研究 认领 引用
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作者 欧阳旭文 赖毅辉 +3 位作者 蓝贤桂 谢芳娟 何景阳 范清斌 《热能动力工程》 CAS CSCD 北大核心 2026年第4期188-198,共11页
为提升传统光伏功率预测方法的精度,提出一种由模糊C均值聚类(Fuzzy C-means Clustering,FCM)算法、麻雀搜索算法(Sparrow Search Algorithm,SSA)、变分模态分解(Variational Mode Decomposition,VMD)、自适应噪声集合经验模态分解(Comp... 为提升传统光伏功率预测方法的精度,提出一种由模糊C均值聚类(Fuzzy C-means Clustering,FCM)算法、麻雀搜索算法(Sparrow Search Algorithm,SSA)、变分模态分解(Variational Mode Decomposition,VMD)、自适应噪声集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)和双向门控循环神经网络(Bidirectional Gated Recurrent Unit,Bi GRU)耦合的短期光伏发电功率预测模型。利用皮尔逊相关系数从数据集中筛选出对光伏发电功率影响显著的关键因素,并利用FCM聚类将具有相似气象特征的数据划分为3类天气数据集,以降低光伏数据的复杂度。通过SSA分别对VMD和CEEMDAN两步分解过程进行自适应优化,使其能够实现对聚类后的光伏功率数据的有效分解,提取功率信号的频率特征。各所得子序列经二次分解后输入由SSA优化超参数的Bi GRU神经网络进行预测,最终通过时序重构得到功率预测结果。模型在澳大利亚DKASC光伏发电站Alice Springs 10.5 k W子阵的实测数据集上进行了验证。结果表明:该模型在不同气象条件下均能准确捕捉光伏功率的频率特征,其预测结果的决定系数均高于0.96,说明该模型在短期光伏发电功率预测中具有较高的精度和适用性。 展开更多
关键词 光伏发电功率预测 FCM聚类 麻雀搜索算法 变分模态分解 自适应噪声集合经验模态分解 双向门控循环神经网络
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