检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

351 篇 会议
52 篇 期刊文献

馆藏范围

403 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

50 篇 工学
- 47 篇 电气工程
- 38 篇 信息与通信工程
- 36 篇 计算机科学与技术...
- 4 篇 交通运输工程
- 3 篇 仪器科学与技术
- 2 篇 电子科学与技术（可...
- 2 篇 土木工程
- 1 篇 冶金工程
- 1 篇 控制科学与工程
- 1 篇 测绘科学与技术
- 1 篇 生物医学工程（可授...
- 1 篇 软件工程
4 篇 理学
- 2 篇 物理学
- 1 篇 化学
- 1 篇 地球物理学
2 篇 医学
- 1 篇 临床医学
1 篇 管理学
- 1 篇 管理科学与工程(可...

主题

403 篇 whale optimizati...
131 篇 optimization
92 篇 accuracy
60 篇 heuristic algori...
53 篇 feature extracti...
48 篇 prediction algor...
44 篇 predictive model...
42 篇 metaheuristics
42 篇 convergence
37 篇 costs
36 篇 whales
35 篇 stability analys...
33 篇 particle swarm o...
31 篇 simulation
30 篇 resource managem...
26 篇 classification a...
26 篇 data models
25 篇 power system sta...
23 篇 internet of thin...
22 篇 energy consumpti...

机构

5 篇 department of co...
3 篇 school of inform...
3 篇 college of elect...
3 篇 faculty of engin...
2 篇 department of co...
2 篇 school of automa...
2 篇 department of co...
2 篇 china electric p...
2 篇 graphic era deem...
2 篇 department of el...
2 篇 department of ai...
2 篇 faculty of infor...
2 篇 school of commun...
2 篇 department of el...
2 篇 department of ai...
2 篇 department of el...
2 篇 chitkara univers...
2 篇 faculty of infor...
2 篇 big data instuti...
2 篇 school of electr...

作者

5 篇 piyush kumar par...
3 篇 baohang zhang
3 篇 krittika kantawo...
3 篇 jie zhang
3 篇 sakkayaphop prav...
3 篇 haichuan yang
2 篇 md ashraful isla...
2 篇 sami abduljabbar...
2 篇 louay karadsheh
2 篇 duangjai jitkong...
2 篇 mustafa hamid ha...
2 篇 liangbo xie
2 篇 haitao yuan
2 篇 oguzhan ceylan
2 篇 renu sharma
2 篇 yang yang
2 篇 shangce gao
2 篇 sizhuo lv
2 篇 mohammed i. habe...
2 篇 karim baiche

语言

403 篇 英文

检索条件"主题词=Whale Optimization Algorithms"

共 403 条记录，以下是131-140 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Shared Energy Storage Capacity Allocation for Wind Farm Stations Based on whale optimization Algorithm

Shared Energy Storage Capacity Allocation for Wind Farm Stat...

引用

Power Science and Technology (ICPST), IEEE International Conference on

作者： Shuai Gao Weiqing Wang Engineering Research Center of Ministry of Education for Renewable Energy Generation and Grid Connection Technology Xinjiang University Urumqi China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Due to the singular nature of energy storage’s insufficient flexible and low economic efficiency difficulty in coping with the volatility and uncertainty of wind power grid integration. Therefore, a collaborative shared energy storage grid-connected system for wind farm stations is established to distribute shared energy storage capacity among multiple wind farm stations. Based on the error model of the actual output and planned output of multiple wind power stations, a deviation penalty mechanism is established to fix the price of energy trading and capacity leasing, and the optimal shared energy storage capacity is finally allocated. Through the utilization of the whale optimization Algorithm (WOA) to address the model, the proposed capacity allocation method’s rationality is validated. This method effectively reduces wind farm station costs by 9.96%, demonstrating the feasibility and continuity of the shared energy storage model.

关键词： Costs Uncertainty Collaboration Wind farms Wind power generation whale optimization algorithms Resource management

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Adaptive Capability of Fuzzy Petri Net by an Improved whale optimization Algorithm

Enhancing the Adaptive Capability of Fuzzy Petri Net by an I...

引用

Chinese Automation Congress (CAC)

作者： Shao-Qiang Ye Azlan Mohd Zain Yusliza Yusoff Wen-Jie Ruan Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia School of Information Engineering Hunan Applied Technology University Changde China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Fuzzy Petri Nets (FPNs) are an advanced topological model that extends traditional Petri nets (PNs) by integrating fuzzy theory. However, the generalization capability of FPN models is often limited due to their reliance on expert-driven parameter determination. This paper proposes an improved whale optimization algorithm (IWOA) for optimizing FPN models to address this challenge. The IWOA employs an adaptive weighting strategy, optimal search techniques, population grouping, and the Lévy flight strategy to dynamically adjust the search process, enhancing the WOA's effectiveness in handling complex problems and improving the balance between exploration and exploitation. The IWOA is tested on six classical benchmark functions in the experimental section and applied to the FPN model. The results demonstrate that the IWOA-FPN model exhibits strong generalization capabilities and superior convergence performance.

关键词： Adaptation models Automation Heuristic algorithms Petri nets Benchmark testing Search problems whale optimization algorithms Convergence

来源：评论

学校读者我要写书评

暂无评论

An Improved Adaptive Beluga whale optimization for Global optimization

An Improved Adaptive Beluga Whale Optimization for Global Op...

引用

International Conference on Computational Intelligence and Applications (ICCIA)

作者： Xiaohui Shen Yonggang Wu School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

A multi-strategy improved beluga whale optimization (IBWO) is proposed to overcome the shortcomings of BWO, such as its susceptibility to local optimization and poor accuracy. In IBWO, three improvement strategies are introduced into the basic BWO. Firstly, the opposition-based initialization process provides the search procedure with good initial candidate solutions. Additionally, the double adaptive parameter approach provides a better balance between exploration and exploitation. Lastly, the combination of elite strategy with spiral search directs searchers to concentrate on important regions, consequently minimizing the amount of invalid search space. To test the scalability of IBWO, 18 benchmark functions in four different dimensions (30, 50, 100, 500) are tested and compared with GWO, HHO, WOA, SMA, PSO, GA, and MPA. In addition, two engineering design problems demonstrate the potential of IBWO to solve real-world optimization problems. The simulation results show that for most test problems, IBWO improves the optimization performance of BWO in terms of convergence accuracy and convergence speed.

关键词： Accuracy Spirals Simulation Scalability Benchmark testing Search problems whale optimization algorithms Robustness optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Optimal Configuration and Economic Evaluation of Hybrid Energy Storage Using an Improved whale optimization Algorithm

Optimal Configuration and Economic Evaluation of Hybrid Ener...

引用

Electrical Engineering and Automation (IFEEA), International Forum on

作者： Yu Chen Liping Guo Chun Yang Jinyu Zhai College of Electric Power Inner Mongolia University of Technology Hohhot Inner Mongolia China Engineering Research Center of Large Energy Storage Technology Ministry of Education Hohhot Inner Mongolia China

ISBN: (数字)9798331516611

ISBN: (纸本)9798331516628

The new distribution network faces the problems of medium and long-period storage difficulties, low carbon and economy to be improved. Optimizing the configuration of hybrid energy storage will help to promote the goal of “double carbon.” The optimal configuration and economic evaluation of the hybrid energy storage composed of lithium battery energy storage and hydrogen energy storage are carried out in order to give full play to the role of energy storage in the new distribution network in this paper. Based on the sensitivity of network losses and the structural characteristics of the distribution network, optimal connection points for the hybrid storage system are identified. A bi-level optimization model is established, incorporating both capital investment and operational costs. The lower-level optimization focuses on minimizing operational costs to determine the optimal energy storage capacity, while the upper-level optimization aims to minimize the overall annual cost of the system. The Topsis method is used to select the Pareto-optimal solutions, yielding the optimal configuration of the hybrid energy storage system's capacity. During the optimization process, the fitness evaluation criterion from the simulated annealing algorithm is integrated into the whale optimization algorithm, which enhances the algorithm's ability to avoid premature convergence and accelerates its convergence speed. An economic evaluation framework for the distribution network is developed, and four different energy storage planning scenarios are assessed using a combination of gray relational analysis and the entropy weighting method. Simulation results based on an improved IEEE 33-node distribution network show that the optimized hybrid energy storage system can reduce line losses, improve voltage quality at nodes, enhance the utilization of distribution transformers, and significantly increase CO 2 and SO 2 emission reductions. Both operational and environmental performance indicat

关键词： Costs Sensitivity Distribution networks Simulated annealing whale optimization algorithms Planning Carbon optimization Energy storage Convergence

来源：评论

学校读者我要写书评

暂无评论

Pollution Flashover Pre-warning Method Based on Beluga whale optimization Algorithm

Pollution Flashover Pre-warning Method Based on Beluga Whale...

引用

Power and Electrical Technology Conference (APET), Asia

作者： Chaoying Fang Yunchu Cheng Jun Xu Xiaojie Wang Jiafeng Lin Wei Gong Electric Power Research Institute State Grid Fujian Electric Power Co. Ltd. Fuzhou China Fujian Zhongshisuo Electric Power Testing & Commissioning Co. Ltd. Fuzhou China

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The problem of pollution flashover of transmission lines seriously interferes with the safety and stability of the power grid system, and insulators occur pollution flashover has a close connection with meteorological conditions. In order to prevent pollution flashover disasters, a pollution flashover pre-warning model considering meteorological data is constructed, and a pre-warning method of the risk of pollution flashover based on the beluga optimization algorithm is proposed. Firstly, the meteorological characteristic quantities are screened according to the fouling mechanism; secondly, the risk assessment model of pollution flashover disaster of transmission lines is constructed based on the meteorological parameters; then, the parameters of the pollution flashover pre-warning model are optimized by the beluga optimization algorithm; finally, an example analysis of line pollution flashover is carried out to verify the effectiveness of the proposed method. The results of the study can provide technical references for power grid anti-pollution flashover work.

关键词： Pollution Rain Power transmission lines Surface contamination Flashover Insulators whale optimization algorithms Power grids Surface treatment optimization

来源：评论

学校读者我要写书评

暂无评论

An Improvement on Exploration Step of whale optimization Algorithm with Levy Distribution for Classification Problems

An Improvement on Exploration Step of Whale Optimization Alg...

引用

Big Data Analytics and Practices (IBDAP), International Conference on

作者： Sakkayaphop Pravesjit Krittika Kantawong Natdanai Kamkhad Saksit Sabaiporn Jantawan Monchanuan Duangjai Jitkongchuen Arit Thammano Panchit Longpradit School of Information and Communication Technology University of Phayao Phayao Thailand Big Data Instutite Bangkok Thailand Facluty of Information Technology King Mongkut's Institute of Technology Ladkrabang Ladkrabang Thailand Faculty of Social Sciences and Humanities Mahidol University Nakhon Pathom Thailand

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

The proposed system represents an enhanced movement in search of food of whale optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (***), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than Visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).

关键词： Support vector machines Convolution Pipelines Data preprocessing Measles Transforms whale optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

optimization of Unmanned Aerial Vehicles Placement in 5G Networks Using the Marine Predator Algorithm

Optimization of Unmanned Aerial Vehicles Placement in 5G Net...

引用

African Federation of Operational Research Societies (AFROS), International Conference of the

作者： Sylia Mekhmoukh Taleb Karim Baiche Walid Aissaoui Adem Boualeg Rachid Tinakiche Yassine Meraihi LIST Laboratory University of M'Hamed Bougara Boumerdes Algeria

ISBN: (数字)9798350386448

ISBN: (纸本)9798350386455

The most important challenges concerning the deployment of Unmanned Areal Vehicles (UAVs) in 5G networks regard coverage optimization and resource efficiency. This paper deals with the Marine Predator Algorithm (MPA) applied for solving the UAVs placement problem in 5G scenarios. In this paper, a comparison is conducted between the MPA algorithm with four algorithms: Grey Wolf optimization (GWO), whale optimization Algorithm (WOA), Black Widow optimization Algorithm (BWOA), and Coronavirus optimization Algorithm (COVIDOA). In this paper, there are test cases for multiple numbers of users and UAVs. Results show that MPA always outperforms the comparative algorithms in terms of coverage and altitude management.

关键词： COVID-19 Machine learning algorithms Three-dimensional displays 5G mobile communication Heuristic algorithms Autonomous aerial vehicles whale optimization algorithms Vehicle dynamics optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Multi-User Computation Offloading in Mobile Edge Computing with Hybrid whale optimization

Multi-User Computation Offloading in Mobile Edge Computing w...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bil Ning Li Haitao Yuan Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the increasing amount of data and the need for real-time processing, Mobile Edge Computing (MEC) is growing rapidly, driving the shift from traditional cloud computing to distributed edge architectures. When offloading these applications with large amounts of data on mobile devices, a lot of computing and storage resources and high energy consumption are required. Yet, mobile devices' computing power, resource storage, and battery power are often limited and cannot meet these needs. To solve a computation offloading problem for joint optimization of time, cost, and energy, this work proposes an improved hybrid algorithm called Chaos and Lévy flights-based whale optimization Algorithm (CLWOA) to solve the multi-user offloading problem in an MEC-Cloud system. Each task is offloaded to local processors of mobile devices, edge servers, and cloud servers in proportion to jointly minimize the completion time, energy consumption, and total cost. Finally, compared with the whale optimization algorithm, lévy flight whale optimization algorithm, refined whale optimization algorithm, and chaos-based whale optimization algorithm, CLWOA reduces the weighted cost by 1.89%, 0.31%, 0.19%, and 0.42%, respectively.

关键词： Chaos Energy consumption Cloud computing Costs Multi-access edge computing whale optimization algorithms Mobile handsets User experience Servers optimization

来源：评论

学校读者我要写书评

暂无评论

An Improved Beluga whale optimization Algorithm Based on Chaotic Opposition-Based Learning and Adaptive Weighting Factor

An Improved Beluga Whale Optimization Algorithm Based on Cha...

引用

New Trends in Computational Intelligence (NTCI), International Conference on

作者： Bingbing Zhang Jingqing Jiang Jiazhi Song College of Mathematics Science Inner Mongolia Minzu University Inner Mongolia China College of Computer Science and Technology Inner Mongolia Minzu University Inner Mongolia China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Beluga whale optimization (BWO) algorithm faces challenges such as slower convergence, lower accuracy, and instability for dealing with unimodal functions. To tackle these problems, we propose an enhanced version of Beluga whale optimization Algorithm (ABWO). ABWO algorithm incorporates chaotic Opposition-Based Learning (OBL) and adaptive weighting factor. We fuse Logistic and Tent chaotic mappings with OBL to ensure an even distribution of individuals in the initial solution space during initialization. Consequently, the population diversity is increased and the quality of the initial solution is better. The adaptive weighting factor $F$ is introduced in the exploitation phase. It increases and then decreases with each iteration. This adjustment enhances the guiding effect of the global optimal position on individual position updates in the early stage of BWO. It also enables the algorithm to execute more effective global searches in the middle stage. Meanwhile, the algorithm converges more quickly in the late stage. Overall, the solution accuracy and convergence stability of the algorithm are improved. On nine unimodal test functions, ABWO has better optimization and convergence performance compared with six basic algorithms. It also outperforms three improved beluga whale optimization algorithms proposed by other scholars.

关键词： Accuracy Fuses Market research whale optimization algorithms optimization Faces Computational intelligence Convergence Logistics

来源：评论

学校读者我要写书评

暂无评论

Big Data Clustering using Ghost Opposition Learning based Crayfish optimization Algorithm

Big Data Clustering using Ghost Opposition Learning based Cr...

引用

Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Yifeng Wang Yuxia Du Shandong Institute of Commerce and Technology Jinan China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Traditional clustering methods often struggle with the sheer scale and complexity of big data. Clustering algorithm of big data models by efficiently handling large datasets, making them more scalable and capable of processing diverse data types. In this big data Clustering technique-based Swarm intelligence optimization various of model are created but still the optimization algorithms are trapping in local optima and its takes huge time for finding the global solution. Addressing these issues and overcome in this paper, proposed the Crayfish optimization Algorithm by using Ghost Opposition-based Learning (GOL-COA) Strategy and environment update technique for enhancing exploitation phase and increasing Exploration convergence speed of optimization, and avoiding local optima problem. The model is evaluated with the accuracy and F1-score metrices and experiment did on various types of datasets. The numerical validation results showing accuracy of 97.56% in LD dataset, and F1 score of 98.10% in LD dataset. This model compares to the other recent year optimization algorithms such as Particle Swarm optimization (PSO), whale optimization (WOA), Ant Colony optimization (ACO), Sand Cat Swam optimization (SCSO), and Werewolf optimization algorithms (WOA).

关键词： Accuracy Clustering algorithms Big Data Data models whale optimization algorithms Numerical models Particle swarm optimization Computer security optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共41页 << < 10 11 12 13 14 15 16 17 18 19 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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

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：