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Cooperative Metaheuristics with Dynamic Dimension Reduction for High-Dimensional Optimization Problems 认领 引用
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作者 Junxiang Li Zhipeng Dong +2 位作者 Ben Han Jianqiao Chen Xinxin Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1484-1502,共19页
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta... Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems. 展开更多
关键词 Dimension reduction modified principal components analysis high-dimensional optimization problems cooperative metaheuristics metaheuristic algorithms
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Global aerodynamic design optimization based on data dimensionality reduction 认领 引用 被引量：17
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作者 Yasong QIU Junqiang BAI +1 位作者 Nan LIU Chen WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2018年第4期643-659,共17页
In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However,for complex problems in which large number... In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However,for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition（POD） in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method. 展开更多
关键词 Aerodynamic shape design optimization Data dimensionality reduction Genetic algorithm Kriging surrogate model Proper orthogonal decomposition
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Rough Sets Hybridization with Mayfly Optimization for Dimensionality Reduction 认领 引用 被引量：1
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作者 Ahmad Taher Azar Mustafa Samy Elgendy +1 位作者 Mustafa Abdul Salam Khaled M.Fouad 《Computers, Materials & Continua》 SCIE EI 2022年第10期1087-1108,共22页
Big data is a vast amount of structured and unstructured data that must be dealt with on a regular basis.Dimensionality reduction is the process of converting a huge set of data into data with tiny dimensions so that ... Big data is a vast amount of structured and unstructured data that must be dealt with on a regular basis.Dimensionality reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed easily.These tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning issues.To achieve dimensionality reduction for huge data sets,this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS.A novel hybrid strategy based on the Mayfly algorithm(MA)and the rough set(RS)is proposed in particular.The performance of the novel hybrid algorithm MA-RS is evaluated by solving six different data sets from the literature.The simulation results and comparison with common reduction methods demonstrate the proposed MARS algorithm’s capacity to handle a wide range of data sets.Finally,the rough set approach,as well as the hybrid optimization techniques PSO-RS and MARS,were applied to deal with the massive data problem.MA-hybrid RS’s method beats other classic dimensionality reduction techniques,according to the experimental results and statistical testing studies. 展开更多
关键词 Dimensionality reduction metaheuristics optimization algorithm mayfly particle swarm optimizer feature selection
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A dimension reduction assisted credit scoring method for big data with categorical features 认领 引用
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作者 Tatjana Miljkovic Pei Wang 《Financial Innovation》 2025年第1期725-754,共30页
In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existin... In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods. 展开更多
关键词 Credit scoring Dimension reduction Logistic regression Majorization-minimization algorithm
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Review of Dimension Reduction Methods 认领 引用 被引量：1
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作者 Salifu Nanga Ahmed Tijani Bawah +5 位作者 Benjamin Ansah Acquaye Mac-Issaka Billa Francis Delali Baeta Nii Afotey Odai Samuel Kwaku Obeng Ampem Darko Nsiah 《Journal of Data Analysis and Information Processing》 2021年第3期189-231,共43页
Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used da... Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored. 展开更多
关键词 Dimension Reduction Machine Learning Linear Dimension Reduction Techniques Non-Linear Reduction Techniques
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Variable-fidelity optimization with design space reduction 认领 引用 被引量：3
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作者 Mohammad Kashif Zahir Gao Zhenghong 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2013年第4期841-849,共9页
Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task ow... Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity （LF） model provides physics-based approximation of the high-fidelity （HF） model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings. 展开更多
关键词 Airfoil optimization Curse of dimensionality Design space reduction Genetic algorithms Kriging Surrogate models Surrogate update strategies Variable fidelity
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基于特征指标降维与改进密度峰值聚类算法的异常用电行为辨识 认领 引用 被引量：2
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作者 郭贺宏 任宇路 +1 位作者 姚俊峰 肖春 《太原理工大学学报》 CAS 北大核心 2026年第2期309-320,共12页
【目的】为准确辨识用户的异常用电行为以降低电网的非技术性损失,提出一种基于线性判别分析(LDA)和改进密度峰值聚类(IDPC)算法的异常用电行为辨识模型。【方法】首先从用电数据中构造能反映用户用电行为的特征集;其次使用LDA对提取的... 【目的】为准确辨识用户的异常用电行为以降低电网的非技术性损失,提出一种基于线性判别分析(LDA)和改进密度峰值聚类(IDPC)算法的异常用电行为辨识模型。【方法】首先从用电数据中构造能反映用户用电行为的特征集;其次使用LDA对提取的特征集进行降维处理;然后使用IDPC算法对降维后的特征集进行聚类分析,将具有不同用电行为特征的用户聚类后再进行异常辨识;最后定义离群异常指数来描述降维后的特征集中用户的离群程度,最终模型输出所有用户用电行为的异常程度排序。针对密度峰值聚类(DPC)算法中截断距离需要人为设定的不足,定义邦费罗尼指数,并将改进的鲸鱼优化算法(IWOA)应用于DPC截断距离参数的优化。【结果】实验结果表明,文章所提出的模型只需检测异常程度较大的少数用户即可稽查出大部分异常用户,且相比其他模型具有更高的召回率与精确率。 展开更多
关键词 数据挖掘 异常用电行为 特征降维 密度峰值聚类 鲸鱼优化算法
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基于TSNE-NGO-RF算法的混凝土坝变形预测模型 认领 引用
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作者 郑东健 赵宇 +2 位作者 冉成 林英浩 陈林泽 《郑州大学学报（工学版）》 CAS 北大核心 2026年第2期122-127,135,共6页
对混凝土坝变形监测资料进行合理的数据分析和准确的预测是确保大坝安全长效运行的关键手段,针对影响大坝变形的环境量具有周期性和非线性的特点,以及传统随机森林模型参数寻优方法适用性差和计算效率低等问题,提出了一种新型的大坝变... 对混凝土坝变形监测资料进行合理的数据分析和准确的预测是确保大坝安全长效运行的关键手段,针对影响大坝变形的环境量具有周期性和非线性的特点,以及传统随机森林模型参数寻优方法适用性差和计算效率低等问题,提出了一种新型的大坝变形预测模型。该模型采用t-分布式随机邻域嵌入对特征值进行降维,提高模型的分类性能,并运用北方苍鹰优化算法对传统随机森林模型进行了改进,提高了随机森林模型参数的择优选取效率。运用北方苍鹰优化算法在第80次迭代时即可确定随机森林模型的参数,且适应度函数为0.2493,相较麻雀搜索算法和粒子群优化算法取得了较好的结果。选取某混凝土坝第18#坝段和第26#坝段进行实例分析,结果表明:所提融合模型预测结果的平均绝对误差分别为0.50193和0.17302 mm,均方误差分别为0.35971和0.04387 mm2,平均绝对百分比误差分别为0.81959%,0.11362%,决定系数分别为0.91456和0.89274,相较于其他模型,该模型在预测准确性和模型稳定性方面表现最优,为混凝土坝变形的精准预测开辟了新的可能性。 展开更多
关键词 混凝土坝 变形预测 降维 北方苍鹰优化算法 随机森林算法
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基于等角映射的高维不平衡数据增量式降维算法 认领 引用
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作者 任宁宁 陈曦 孙力帆 《现代电子技术》 北大核心 2026年第5期138-141,146,共4页
高维不平衡数据增量变化时,因多类别样本数目不一、特征分布不均,降维时难免过度关注多数类样本,忽视少数类样本,导致降维后少数类数据失真。为此,文中提出基于等角映射的高维不平衡数据增量式降维算法。利用模糊C-means算法将高维不平... 高维不平衡数据增量变化时,因多类别样本数目不一、特征分布不均,降维时难免过度关注多数类样本,忽视少数类样本,导致降维后少数类数据失真。为此,文中提出基于等角映射的高维不平衡数据增量式降维算法。利用模糊C-means算法将高维不平衡数据划分为不同类型数据后,使用基于时间窗口的增量数据抽取方法,抽取不同类型高维不平衡数据的增量数据。由基于等角映射的增量流形学习降维算法运算增量数据与原始数据点距离。结合距离设定权重因子,将此增量数据映射于低维空间,实现高维不平衡数据增量式降维。实验结果表明:所提算法在不同类别高维不平衡数据增量式降维中,无论是1 GB还是10 GB的新增数据量,降维后数据维度较低,数据结构和信息的保真度较高,没有出现明显失真情况。该方法是一种有效的数据降维算法,可应用于处理大规模高维不平衡数据增量式降维问题中。 展开更多
关键词 模糊C-means算法 等角映射 高维不平衡数据 增量式降维 时间窗口 增量数据抽取 流形学习 加权处理
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深海科技关键技术群落识别与竞争态势分析 认领 引用
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作者 付雨芳 刘康睿 顾波军 《中国海洋大学学报（社会科学版）》 2026年第1期10-20,共11页
深海科技作为国家战略前沿与全球科技竞争的重要领域,其关键技术群落的识别与竞争态势分析对把握创新方向、优化资源配置具有重要意义。基于全球深海科技领域74752项专利数据,构建技术相对影响力(RIT)指标识别强势、新兴、衰退与沉睡四... 深海科技作为国家战略前沿与全球科技竞争的重要领域,其关键技术群落的识别与竞争态势分析对把握创新方向、优化资源配置具有重要意义。基于全球深海科技领域74752项专利数据,构建技术相对影响力(RIT)指标识别强势、新兴、衰退与沉睡四类技术态势,并采用Louvain社群发现算法识别出深海科技关键技术群落,进一步通过核心专利筛选与t-SNE降维可视化算法,绘制国际竞争态势图谱,系统揭示主要国家在深海科技关键领域的优势分布与竞争格局。研究表明:(1)深海科技整体处于快速演进与技术迭代阶段,在数字信息传输、电子器件及高分子耐腐蚀材料等方向创新活跃;(2)深海科技领域包括15个关键技术群落,涵盖海洋药物、耐腐蚀材料、储能技术、智能探测、深远海养殖装置等多个方向;(3)中国在深海科技领域专利总量处于领先地位,但核心专利占比低,尤其在基础材料与能源系统方面与美国、日本存在显著差距。本研究为深海科技领域的创新布局与国际竞争策略提供数据支撑与决策参考。 展开更多
关键词 深海科技 RIT指数 技术群落 Louvain社群发现算法 t-SNE降维
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Compressed representation of quantum states via orthogonal polynomials for flow field analysis 认领 引用 被引量：2
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作者 Yu Fang Cheng Xue +7 位作者 Taiping Sun Xiaofan Xu Chuangchao Ye Tengyang Ma Huanyu Liu Yuchun Wu Zhaoyun Chen Guoping Guo 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期94-109,共16页
Quantum computing promises exponential acceleration for fluid flow simulations,yet the measurement overhead required to extract classical information from the resulting quantum states fundamentally undermines this adv... Quantum computing promises exponential acceleration for fluid flow simulations,yet the measurement overhead required to extract classical information from the resulting quantum states fundamentally undermines this advantage—a challenge termed the“output problem”.To address this,we propose an orthogonal-polynomial-based quantum neural network(OP-QNN)that generates a compressed,low-dimensional representation of these states,enabling the efficient extraction of classical information with significantly reduced measurement overhead.Within OP-QNN,we develop an orthogonal-polynomial-based variational quantum circuit as a core component,which embeds trainable parameters into orthogonal basis transformations to enhance expressivity and generate compressed coefficients.We evaluate the compressed representation through two critical post-processing tasks on fluid flow data:reconstruction and classification,demonstrating exceptional performance in both areas.The high reconstruction fidelity confirms that the compressed data preserves the state’s global structure,while the high classification accuracy proves that it retains key discriminative features.Achieved with significantly reduced computational complexity and parameter counts compared to benchmarks,these results validate OP-QNN as an effective solution to the output problem—bridging quantum simulation outputs with practical fluid analysis and offering a scalable pathway to exploit quantum advantages in computational fluid dynamics. 展开更多
关键词 Quantum computing Computational fluid dynamics Orthogonal polynomials Dimensionality reduction Variational quantum algorithm
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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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基于流形学习的风电机组异常数据识别方法 认领 引用 被引量：1
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作者 杨磊 郭鹏 张雨潇 《分布式能源》 2026年第1期11-19,共9页
为有效识别和剔除风电机组实测数据中的异常数据,通过分析风电机组实测数据的高维特征,提出一种基于流形学习的异常数据识别算法。首先,采用k-近邻互信息算法实现风电机组特征变量选择;随后,使用将样本间距离度量替换为欧几里得度量和... 为有效识别和剔除风电机组实测数据中的异常数据,通过分析风电机组实测数据的高维特征,提出一种基于流形学习的异常数据识别算法。首先,采用k-近邻互信息算法实现风电机组特征变量选择;随后,使用将样本间距离度量替换为欧几里得度量和局部主成分分析(local principal component analysis,LPCA)差别加权和的优化t-分布随机近邻嵌入(t-distributed stochastic neighbor embedding,t-SNE)算法挖掘出高维流形数据中具有内在规律的低维特征,使得具有不同分布特征的数据在可视化二维空间中显著分离;最后,采用基于密度的噪声空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对二维空间中的数据进行聚类。结果表明,与主成分分析(principal component analysis,PCA)算法、局部线性嵌入(locally linear embedding,LLE)算法和原t-SNE算法相比,所提方法能够对各种复杂工况数据进行可视化分离聚类,并对异常数据进行识别和剔除。 展开更多
关键词 风电机组 异常数据 流形学习 降维 基于密度的噪声空间聚类(DBSCAN)算法
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Planning of arrival and departure routes in terminalmaneuvering area based on high dimensionality reduction environment modeling method 认领 引用 被引量：1
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作者 Siyu Su Youchao Sun +1 位作者 Chong Peng Haiyun Yang 《Aerospace Systems》 2020年第4期297-307,共11页
The efficient design of arrival and departure routes in the terminal maneuvering area plays a key role in increasing airport capacity and reducing traffic congestion.In our study,we establish an arrival and departure ... The efficient design of arrival and departure routes in the terminal maneuvering area plays a key role in increasing airport capacity and reducing traffic congestion.In our study,we establish an arrival and departure route planning model in the terminal maneuvering area,taking into account the airspace environmental constraints and aircraft operational constraints.Then the three-dimensional environment modeling method with a high degree of dimensionality reduction is introduced to improve the efficiency of route planning,and routes are planned sequentially using the A*algorithm in a dimensionally reduced environment.Numerical simulation tests,performed on the terminal maneuvering area of Chengdu Shuangliu Airport in China,show the effectiveness of the proposed method.Each route is given two planning schemes considering the maximum and minimum takeoff or descent slope,and a total of seven routes is generated. 展开更多
关键词 Terminal maneuvering area Arrival and departure routes High dimensionality reduction A*algorithm Takeoff/descent slope
A fast MPC algorithm for reducing computation burden of MIMO 认领 引用 被引量：1
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作者 祁荣宾 梅华 +1 位作者 陈超 钱锋 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第12期2087-2091,共5页
The computation burden in the model-based predictive control algorithm is heavy when solving QR optimization with a limited sampling step, especially for a complicated system with large dimension. A fast algorithm is ... The computation burden in the model-based predictive control algorithm is heavy when solving QR optimization with a limited sampling step, especially for a complicated system with large dimension. A fast algorithm is proposed in this paper to solve this problem, in which real-time values are modulated to bit streams to simplify the multiplication. In addition, manipulated variables in the prediction horizon are deduced to the current control horizon approximately by a recursive relation to decrease the dimension of QR optimization. The simulation results demonstrate the feasibility of this fast algorithm for MIMO systems. 展开更多
关键词 Fast MPC algorithm Computation burden One-bit operation Dimension reduction
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高维稀疏电力负荷数据无监督挖掘算法 认领 引用
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作者 丁业豪 杨月 马保全 《沈阳工业大学学报》 CAS 北大核心 2026年第2期57-64,共8页
【目的】在电力系统中,负荷数据分析对电网调度、规划和管理至关重要。然而,随着电力系统的复杂化与智能化程度的加深,电力负荷数据呈现高维度、稀疏性等特点,导致传统数据分析方法在处理效率和捕捉负荷变化内在信息方面面临较大挑战。... 【目的】在电力系统中,负荷数据分析对电网调度、规划和管理至关重要。然而,随着电力系统的复杂化与智能化程度的加深,电力负荷数据呈现高维度、稀疏性等特点,导致传统数据分析方法在处理效率和捕捉负荷变化内在信息方面面临较大挑战。本文提出一种高效无监督数据挖掘算法,旨在提升高维稀疏电力负荷数据的处理效率与信息提取能力。【方法】首先,采用基于信息熵的特征排序法确定特征重要度。通过计算互信息、开展中心化、标准化处理等完成数据初始化,选择互信息最大的特征扩充特征集合,通过计算相关信息熵筛选特征子集,以支持向量机(SVM)分类器为基准模型优化子集筛选过程,引入改进粒子群算法进行特征二次选择,同时借助SVM分类器完成特征初步筛选。然后,引入主成分分析(PCA)实施降维。对样本矩阵进行中心化处理,构建协方差矩阵,获取特征值与特征向量,选择特征向量构建新的矩阵以实现降维。最后,引入基于无监督学习的自编码网络开展数据无监督挖掘。编码阶段将输入数据转化为特征表示,解码阶段完成数据恢复,通过设定数据、执行聚类操作、筛选数据点、开展数据均衡处理、获取训练模型分类界面等步骤,实现隐藏特征提取与网络调节。【结果】本文算法在整个测试过程中兰德指数一直大于0.60,呈现较高的聚类准确性。在60次迭代实验中,最大内存开销占比约为8.3%,表明本文算法的计算资源利用率较高。与其他传统算法相比,本文算法在处理高维稀疏电力负荷数据时,能够表现出更高的处理效率和更优的挖掘效果。【结论】无监督挖掘算法在高维稀疏电力负荷数据分析中表现优异,本文算法通过特征选择与降维处理减少计算量,并借助自编码网络挖掘非线性特征,显著提升了数据挖掘的准确性与效率,具有很强的适用性与可行性。本文算法的创新之处在于,融合信息熵特征排序、支持向量机、改进粒子群、主成分分析与自编码网络等多种方法,从特征处理到数据挖掘形成完整体系,既能有效应对高维稀疏电力负荷数据的挖掘难题,又为电力系统负荷数据分析提供了新的有效手段,因而对推动电力系统智能化发展具有重要意义。 展开更多
关键词 电力负荷数据 特征选择与降维 自编码网络 无监督挖掘 主成分分析 改进粒子群算法 支持向量机
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Degradation algorithm of compressive sensing 认领 引用
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作者 Chunhui Zhao Wei Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第5期832-839,共8页
The compressive sensing(CS)theory allows people to obtain signal in the frequency much lower than the requested one of sampling theorem.Because the theory is based on the assumption of that the location of sparse valu... The compressive sensing(CS)theory allows people to obtain signal in the frequency much lower than the requested one of sampling theorem.Because the theory is based on the assumption of that the location of sparse values is unknown,it has many constraints in practical applications.In fact,in many cases such as image processing,the location of sparse values is knowable,and CS can degrade to a linear process.In order to take full advantage of the visual information of images,this paper proposes the concept of dimensionality reduction transform matrix and then se-lects sparse values by constructing an accuracy control matrix,so on this basis,a degradation algorithm is designed that the signal can be obtained by the measurements as many as sparse values and reconstructed through a linear process.In comparison with similar methods,the degradation algorithm is effective in reducing the number of sensors and improving operational efficiency.The algorithm is also used to achieve the CS process with the same amount of data as joint photographic exports group(JPEG)compression and acquires the same display effect. 展开更多
关键词 compressive sensing(CS) dimensionality reduction transform matrix accuracy control matrix degradation algorithm joint photographic exports group(JPEG)compression.
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Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search 认领 引用 被引量：1
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作者 Soukaina Mjahed Khadija Bouzaachane +2 位作者 Ahmad Taher Azar Salah El Hadaj Said Raghay 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第11期459-494,共36页
This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised ... This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data set.This unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow criterion.The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed approach.Even if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered above.The first contribution lies in the method used for Step(1)to select the most informative features and variables.We used the t-Statistic technique to rank them.Afterwards,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between them.Then,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set dimension.The second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO. 展开更多
关键词 Ant lion optimization binary clustering clustering algorithms Higgs boson feature extraction dimensionality reduction elbow criterion genetic algorithm particle swarm optimization
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面向高维数据降维的核主成分分析改进算法及应用 认领 引用
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作者 谷应翔 《北斗与空间信息应用技术》 2026年第1期21-23,共3页
本文针对高维非线性数据降维问题,提出一种改进的核主成分分析(KPCA)算法。通过对核函数的优化和计算复杂性的降低,该算法在手写数字数据集(MNIST)上的降维精度提高到了92.3%,鲁棒性增强了大约9.2%,而稳定性的波动量也减少到了0.8。实... 本文针对高维非线性数据降维问题,提出一种改进的核主成分分析(KPCA)算法。通过对核函数的优化和计算复杂性的降低,该算法在手写数字数据集(MNIST)上的降维精度提高到了92.3%,鲁棒性增强了大约9.2%,而稳定性的波动量也减少到了0.8。实验结果表明,文章提出的算法相比传统主成分分析(PCA),在计算效率、鲁棒性和解释性上都有明显的优越性,适合大规模图像和语音数据分析的场景。 展开更多
关键词 核主成分分析 高维数据降维 优化算法 鲁棒性
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基于卷积神经网络的水电站一次设备故障诊断研究 认领 引用
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作者 仵博 《电工技术》 2026年第1期172-174,181,共3页
水电站一次设备是电力系统的核心组成部分,其运行状态直接影响电网的稳定性和安全性。传统故障诊断方法在面对复杂设备故障时效果有限,难以满足电力系统需求,因此提出了一种基于改进卷积神经网络的水电站一次设备故障诊断方法。通过引入... 水电站一次设备是电力系统的核心组成部分,其运行状态直接影响电网的稳定性和安全性。传统故障诊断方法在面对复杂设备故障时效果有限,难以满足电力系统需求,因此提出了一种基于改进卷积神经网络的水电站一次设备故障诊断方法。通过引入Retinex算法增强设备红外图像,结合交叉熵函数构建深度卷积去噪自编码器进行数据降维,并利用卷积神经网络确定故障特征与类型的映射关系。实验结果表明,所提方法对不同故障类型的诊断准确率始终保持在95%以上,训练时间控制在4 min以内,显著优于传统方法。 展开更多
关键词 水电站 一次设备 故障诊断 Retinex算法 卷积神经网络 数据降维
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