检索结果-南通市图书馆

Improved cat swarm optimization for parameter estimation of mixed additive and multiplicative random error model

Geodesy and Geodynamics 2023年第4期14卷 385-391页

作者： Leyang Wang Shuhao Han Faculty of Geomatics East China University of TechnologyNanchang 330013China Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake Ministry of Natural ResourcesNanchang 330013China

To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter *** embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search *** use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,*** experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm *** the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array *** method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.

关键词： Mixed additive and multiplicative random error model Parameter estimation Least squares cat swarm optimization Powell method

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Multi-objective optimization of feature selection using hybrid cat swarm optimization

引用

Science China(Technological Sciences) 2021年第3期64卷 508-520页

作者： GAO Xiao-Zhi NALLURI Madhu Sudana Rao KANNAN K SINHAROY Diptendu School of Computing University of Eastern FinlandKuopio 70210Finland School of Engineering Department of MathematicsAmrita Vishwa VidyapeethamCoimbator 641112India School of Humanities and Sciences SASTRA UniversityThanjavur 612001India Department of Computer Science and Engineering National Institute of TechnologyMeghalayaShillong 793003India

With the pervasive generation of information from a wide range of sensors and devices,there always exist a large number of input features in databases,thus complicating machine learning problem ***,certain features are relatively impertinent to specific problems,which may degrade the performances of classifiers in terms of prediction accuracy,sensitivity,specificity,and recall *** main goal of a multi-objective optimization problem is to identify the subsets of the given *** this end,a hybrid cat swarm optimization(HCSO)algorithm is proposed in our paper for performance improvement of the basic cat swarm optimization(CSO)that incorporates guided and competitive&inherent characteristics into the original *** performance of HCSO has been tested by finding the optimal feature subset for 15 benchmark *** number of class labels for these datasets varies between 2 and *** time complexity analysis of both CSO and HCSO has also been ***,the performance of the proposed algorithm has been compared with that of simple CSO and other state-ofthe-art *** performances obtained by HCSO have an average 2.68%improvement with a standard deviation of *** maximum performance improvement is up to 10.09%in prediction *** on the same datasets,CSO has yielded improvements within the range of-7.27%to 8.51%with an average improvement 0.9%and standard deviation *** statistical tests carried out in the experiments prove that HCSO manifests a moderately better feature selection capacity than that of its counterparts.

关键词： feature selection competition cat swarm optimization guided search parameter evolution

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

cat swarm with Fuzzy Cognitive Maps for Automated Soil Classification

引用

Computer Systems Science & Engineering 2023年第2期44卷 1419-1432页

作者： Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Majed Alsanea Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the *** an auto-mated soil prediction tool helps to considerably improve the efﬁcacy of the *** the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive *** other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering *** this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classiﬁca-tion(FCMCSO-ASC)*** goal of the FCMCSO-ASC technique is to identify and categorize seven different types of *** accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature *** addition,the FCMCSO model is applied for soil classiﬁcation and the weight values of the FCM model are optimally adjusted by the use of CSO *** exam-ining the enhanced soil classiﬁcation outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

关键词： Soil classiﬁcation intelligent models fuzzy cognitive maps cat swarm optimization fuzzy logic

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

引用

Intelligent Automation & Soft Computing 2023年第9期37卷 2849-2863页

作者： Ahmed S.Almasoud Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

The Internet of Things(IoT)is considered the next-gen connection network and is ubiquitous since it is based on the *** Detection System(IDS)determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified *** this back-ground,the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification(EMML-CADC)model in an IoT *** aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced *** attain this,the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform *** addition,Enhanced cat swarm optimization based Feature Selection(ECSO-FS)approach is followed to choose the optimal feature ***,Mayfly optimization(MFO)with Twin Support Vector Machine(TSVM),called the MFO-TSVM model,is utilized for the detection and classification of ***,the MFO model has been exploited to fine-tune the TSVM variables for enhanced *** performance of the proposed EMML-CADC model was validated using a benchmark dataset,and the results were inspected under several *** comparative study concluded that the EMML-CADC model is superior to other models under different measures.

关键词： Metaheuristics cyberattack detection machine learning cat swarm optimization security

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

CSO Algorithm for Economic Dispatch Decision of Hybrid Generation System

引用

Journal of Energy and Power Engineering 2011年第8期5卷 743-749页

作者： J.C. Chen J.C. Hwang J.S. Pan Department of Electrical Engineering National Kaohsiung University of Applied Sciences Kaohsiung 807 Taiwan

The aim of this research is to study the optimal economic dispatch (ED) through cat swarm optimization (CSO) algorithm. Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the CSO can, therefore, be effectively applied to different optimization problems. In this paper, the CSO is also extended to coordinate wind and thermal dispatch and to minimize total generation cost. Results indicated that the CSO is superior to PSO in the fast convergence and better performance to find the global best solution.

关键词： cat swarm optimization particle swarm optimization economic dispatch load management

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：