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

339 篇 会议
47 篇 期刊文献

馆藏范围

386 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

46 篇 工学
- 44 篇 电气工程
- 36 篇 信息与通信工程
- 33 篇 计算机科学与技术...
- 4 篇 交通运输工程
- 2 篇 仪器科学与技术
- 2 篇 土木工程
- 1 篇 冶金工程
- 1 篇 测绘科学与技术
- 1 篇 生物医学工程（可授...
3 篇 理学
- 2 篇 物理学
- 1 篇 地球物理学
2 篇 医学
- 1 篇 临床医学

主题

386 篇 whale optimizati...
121 篇 optimization
88 篇 accuracy
56 篇 heuristic algori...
49 篇 feature extracti...
45 篇 prediction algor...
43 篇 predictive model...
42 篇 metaheuristics
39 篇 convergence
37 篇 costs
35 篇 whales
32 篇 stability analys...
31 篇 simulation
31 篇 particle swarm o...
28 篇 resource managem...
25 篇 data models
25 篇 power system sta...
24 篇 classification a...
22 篇 internet of thin...
22 篇 energy consumpti...

机构

4 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 xu zhang

语言

386 篇 英文

检索条件"主题词=Whale optimization algorithms"

共 386 条记录，以下是171-180 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：

Research on Capacity optimization Configuration of Light Storage Charging DC Microgrid

Research on Capacity Optimization Configuration of Light Sto...

引用

2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Lei Fu Zicheng Li School of Electrical Information Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Due to the high investment cost and insufficient system stability of the photovoltaic charging DC microgrid, a capacity optimization model is constructed with the objective functions of annual net cost, which represents economic benefits, and power deviation rate, which represents system stability. Aiming at the problem of insufficient search ability and low optimization accuracy in solving optimization configuration models with non-zero solutions using traditional whale optimization algorithms, a multi strategy improved whale optimization algorithm is proposed, which integrates quasi adversarial learning mechanism, adaptive weights, nonlinear convergence factor, Gaussian difference mutation, and greedy selection. Finally, a case study was conducted on a residential area in Wuhan, and the results showed the feasibility of the model and the effectiveness of the improved algorithm.

关键词： Photovoltaic systems Adaptation models Costs Biological system modeling Microgrids whale optimization algorithms Stability analysis optimization Convergence Load modeling

来源：评论

学校读者我要写书评

暂无评论

Space Crowdsourcing Allocation Scheme Based On Improved Fishing optimization Algorithm

Space Crowdsourcing Allocation Scheme Based On Improved Fish...

引用

Computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Xingyu Zhou Minglan Fu Xu Zhang School of Computer Science and Technology Huaibei Normal University Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

This study proposes a mobile crowdsourcing task allocation method based on the Improved Fishing optimization Algorithm (ICFOA-SCAM), aiming to address the issue of user preferences and waiting time being overlooked in existing crowdsourcing platforms, thereby improving user satisfaction. Optimizing task allocation has become the key to improving service quality. The ICFOA-SCAM method comprehensively considers the personalized preferences of users, the abilities of workers, and the spatiotemporal characteristics of tasks. By defining key concepts such as spatiotemporal units, user preference evaluation scores, waiting time evaluation scores, and user satisfaction evaluation scores, it enriches the definition of tasks and crowdsourcing authors, enabling the model to more comprehensively consider the complexity of spatial crowdsourcing task allocation. The experimental results show that the ICFOA-SCAM method outperforms traditional white whale optimization algorithm and grey wolf optimization algorithm in terms of user satisfaction and task allocation efficiency. ICFOA-SCAM improves the global search capability and convergence speed of the algorithm by introducing Singer chaotic mapping initialization and Lévy flight strategy. The contribution of this article lies in proposing a new crowdsourcing task allocation model, which not only improves the efficiency and quality of task allocation, but also enhances user satisfaction. Future research will explore how to leverage workers' social relationships to further enhance the effectiveness of task allocation, as well as improve the service quality and user satisfaction of crowdsourcing workers while protecting location and information privacy.

关键词： Crowdsourcing Social networking (online) Soft sensors whale optimization algorithms Stability analysis Spatiotemporal phenomena Resource management User preference optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Improved whale optimization Algorithm and Optimized Long Short-Term Memory for DDoS Cyber Security Threat

Improved Whale Optimization Algorithm and Optimized Long Sho...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sanjaikanth E Vadakkethil Kiran Polimetla Srikanth Velpula Piyush Kumar Pareek Dipti Sontakke Somanathan Pillai School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Dept. of Information Technology Adobe Inc Austin Texas USA Department of Electrical and Electronics Engineering SR University Warangal India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Consultant Capgemini Inc USA

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The Distributed Denial of Service (DDoS) attacks is a critical cyber security threat that blocks the services for users and leads to important damage for reputation and effective customers. The existing methods provides low classification accuracy due to irrelevant features in classification performance. The Improved whale optimization Algorithm (IWOA) is proposed for the selecting of relevant features which improved the classification performance. The selected features are detected and classified by Optimized Long Short-Term Memory (OLSTM) that provided high detection rate of DDoS attacks. The one-hot encoding and min-max normalization techniques are used in the data pre-processing stage to improve the performance of classification. The CIC-DDoS 2019 dataset are used for evaluating the proposed method. The IWOA-OLSTM method attained highest accuracy 97.12%, precision 96.74%, recall 96.27%. f1-score 96.54% and DR 93.26% on CIC-DDoS 2019 dataset which is effective than Convolutional Neural Network (CNN) - Bidirectional Long Short-Term Memory (BiLSTM) and CNN-LSTM.

关键词： Deep learning Denial-of-service attack Feature extraction whale optimization algorithms Encoding Classification algorithms Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Refraction-Learning Based whale optimization Algorithm with Opposition-Learning and Adaptive Parameter optimization

Refraction-Learning Based Whale Optimization Algorithm with ...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Aditya Anand Lakshay Rastogi Ansh Agarwaal Shashank Bhardwaj Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In this paper, the development of an enhanced variant of the whale optimization Algorithm (WOA) is proposed which addresses some of the limitations, namely slow convergence speed, a tendency to get stuck in local optima, and loss of population diversity. The proposed variant integrates novel strategies namely Refraction Learning (RL) and Opposition-Based Learning (OBL) along with an adaptive parameter optimization mechanism. Owing to the unique amalgamation of the above-mentioned strategies, the performance of the conventional WOA is augmented by intensifying the search process and smoothing it overall. This leads to superior performance in the case of multi-modal landscapes, escaping local optima, and a more eclectic investigation of the search space. Through comprehensive benchmark testing across 23 functions, demonstration occurs that the algorithm outperforms the original WOA and other modern optimization techniques. The proposed WOA variant shows significant improvements in terms of convergence rate, global search ability, and solution accuracy. The empirical results highlight the strengths of the approach and indicate its potential as an effective optimization tool for solving complex real-world problems.

关键词： Smoothing methods Sociology Circuits Benchmark testing whale optimization algorithms Task analysis Statistics

来源：评论

学校读者我要写书评

暂无评论

Symmetric Adaptive Population Division Based Salp Swarm Algorithm for Resource optimization in Cloud Computing

Symmetric Adaptive Population Division Based Salp Swarm Algo...

引用

Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Sidagouda Basagouda Patil Mukund A. Kulkarni Computer Science (Management Studies) Bharati Vidyapeeth (Deemed to be University) Kolhapur India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In the present advanced world, Cloud Computing (CC) is essential across various applications because of its quick development and wide utilization of Internet technologies. However, it faces some issues in the identification of best resources and task allocation for classifying relevant tasks based on the load. The Symmetric Adaptive Population Division based Salp Swarm Algorithm (SAPD-SSA) is proposed for optimal resource allocation in the CC environment. The SAPD-SSA enhances the convergence speed by efficiently dividing populations into subgroups. This approach enhances the balance among exploration and exploitation which leads to better resource utilization in CC. Moreover, it effectively handles complex and dynamic cloud environments which ensures optimal resource allocation with less computational cost. The multi-objective functions of makespan, resource utilization and computation cost are considered in this research for estimating the effectiveness of proposed SAPD-SSA. The makespan, resource utilization and computation cost are taken as evaluation metrics for assessing SAPD-SSA performance. The SAPD-SSA attains a makespan of 250, resource utilization of 98.28% and computation cost of 950$ for the task of 200, which is superior to the Hybrid whale optimization algorithm-with Mutation based Bees algorithm (HWOA-mBA).

关键词： Measurement Cloud computing Costs Heuristic algorithms Metaheuristics Dynamic scheduling whale optimization algorithms Computational efficiency Resource management Convergence

来源：评论

学校读者我要写书评

暂无评论

Scheduling the Tasks in Cloud Computing using Adaptive Adjustment in whale optimization Algorithm

Scheduling the Tasks in Cloud Computing using Adaptive Adjus...

引用

Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Piyush Kumar Pareek G D Nithyashree S A Rajashekhar Pankaj Zanke K S Navyashree Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Senior Technical Business Analyst Sapiens International Corporation USA

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

One of the most popular infrastructures for employing virtual machines, also known as processing units, is cloud computing. Task scheduling is fundamental problems of cloud computing. A number of methods to handle the nondeterministic polynomial-time (NP)-complete optimisation issue of choosing the best scheduling criteria in cloud computing have been proposed by academics in the past. An effective optimisation approach is suggested in this research, based on the whale optimisation algorithm (WOA), to address the common problems of solution algorithms, which include poor global optimisation ability, ease of sliding into local optimisation, and sluggish convergence speed. We provide an adaptive adjustment method that may dynamically alter search behaviour during the whale optimisation algorithm’s iterative operation. The technique described to further update the position of search individuals also uses the evolutionary approach of differential variation. The algorithm’s exploration and development capacities are then balanced, and a restart approach is included to keep the system from entering local optimisation. The approach simultaneously resolves the work planning issue in the situation of rain cloud processing. When compared to other algorithms, this technique uses more resources and converges more quickly. Additionally, the evaluation function seeks for the optimal answers by taking the makespan, resource cost, and load balancing level into account.

关键词： Cloud computing Scheduling algorithms Heuristic algorithms Scalability whales whale optimization algorithms Scheduling

来源：评论

学校读者我要写书评

暂无评论

An Analysis of Frequency Response in Integrated Power System Using Power Electronics Controller Tuned With whale optimization Technique

An Analysis of Frequency Response in Integrated Power System...

引用

Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Somnath Ganguly Joon-Ho Choi Joyti Mudi S. Kundu Aloke Kumar Roy V. Mukherjee Department of Electrical Engineering Chonnam National University Gwangju South Korea Bankura Unnayani Insstitute of Engineering Bankura West Bengal India Department of Electrical Engineering Bankura Unnayani Institute of Engineering Bankura West Bengal India Dept. of EEE Bharat Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Bankura Unnayani Institute of Engineering Bankura West Bengal India Department of Electrical Engineering Indian Institute Technology (Indian School of Mines) Dhanbad Jharkhand India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

The performance of any isolated hybrid power system is characterized by the type of controller being used. The considered power generating units are the solar thermal power plant (STPP), wind turbine generator (WTG), and diesel engine generator (DEG), whose frequency stabilization has been done with the utilization of proportional-integral-derivative (PID), integral-tilt-derivative (I-TD), and tilt-integral-derivative (TID) controllers. Tuning controller gains using the whale optimization algorithm (WOA) and the quasi-oppositional based whale optimization algorithm (QOWOA) involves optimizing the parameters to achieve the best performance for a given hybrid system. A comparative frequency response of the system under the individual influence of PID, TID, and I-TD controllers has been shown with different types of load perturbations. The simulation result shows that QOWOA-TID outperforms the other controllers. The study addresses the critical need for optimizing control strategies in hybrid power systems to ensure stability, efficiency, and reliability, thereby contributing to the advancement of sustainable energy solutions.

关键词： Time-frequency analysis Perturbation methods Power system stability whale optimization algorithms Hybrid power systems Generators Frequency response

来源：评论

学校读者我要写书评

暂无评论

Improved Energy Balancing based Data Forwarding incorporating whale optimization Technique in Underwater Acoustic Sensor Network

Improved Energy Balancing based Data Forwarding incorporatin...

引用

Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： R. Bhairavi M. Thiruveni Anita Christaline J N. Ashokkumar I. Chandra P. Nagarajan Department of ECE SRM Institute of Science and Technology Chennai India Department of ECE PSNA College of Engineering and Technology Dindigul India School of Computer Science and Engineering VIT Chennai India Department of Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

UWASN has various challenges such as minimal bandwidth availability, long propagation delay, increased energy exhaustion, increased deployment cost and security. The routing protocols should facilitate an enhanced data forwarding towards sink nodes. In this paper, an Improved Energy Balancing based Data Forwarding incorporating whale optimization Technique (IEBDF-WO) is proposed to effectively elect optimal forwarders to participate in the routing process. The whale optimization algorithm has three phases namely, scouring phase, encompassing phase and final bubble net raiding phase for facilitating enhanced data transmission towards sink node. The proposed technique determines a fitness function by using the weighted depth difference W D , Potential Relay Count (PRC), Residual energy of each potential relay node. The performance of the proposed IEBDF-WO technique is analyzed and is collated with the existing Energy-Efficient Probabilistic Depth-Based Routing (EEPDBR) technique. It is seen that, for a network comprising of 200 nodes, the proposed scheme has improved Packet Delivery Ratio of 96.04% while minimizing the end to end delay, energy tax and average number of hops.

关键词： Electric potential Routing Probabilistic logic whale optimization algorithms Routing protocols Security Relays Underwater acoustics optimization Propagation delay

来源：评论

学校读者我要写书评

暂无评论

Adaptive Inertia Weight with whale optimization Algorithm for Mathematical Models in Energy Saving Efficiency

Adaptive Inertia Weight with Whale Optimization Algorithm fo...

引用

Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Lina Ding Zeyu Liu Baotou Light Industry Vocational and Technical College Baotou China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The global manufacturing industry there is increasing the recognition of need to enhance energy efficiency to reduce both costs and accuracy. However, achieving accurate energy control is challenging due to the dynamic performance requirements and the need for precise installation of variable displacement pumps or frequency conversion equipment. This paper, proposed Adaptive Inertia Weight with whale optimization Algorithm (AIW-WOA) method is efficiently performed in the energy efficiency in refrigeration plant to achieve better accuracy balance energy. The adaptive mechanism of the inertia weight adjusts based on the iteration process to evaluated the refrigeration of the flexible to changing energy level condition is mathematically represented. The AIW mechanism allow the optimization process to adapt to different operational condition in refrigeration plant such as varying load demand and energy. The proposed AIW-WOA method for energy saving efficient in refrigeration is achieving better outcomes such as accuracy of 92.12% respectively. The existing method such as Genetic Algorithm (GA) and Artificial Neural Network (ANN) and Asymmetric Hydraulic Cylinder (AHC) are evaluated for proposed method.

关键词： Accuracy Energy conservation Artificial neural networks Hydraulic systems Energy efficiency whale optimization algorithms Energy states Refrigeration optimization Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Electrical Parameters Extraction of Diode Using whale optimization Algorithm

Electrical Parameters Extraction of Diode Using Whale Optimi...

引用

Electrical Engineering and Automatic Control (ICEEAC), International Conference on

作者： Elyes Garoudja Walid Filali Slimane Oussalah Fouaz Lekoui Rachid Amrani Plateforme Technologique de Microfabrication Centre de Développement des Technologies Avancées (CDTA) Algiers Algeria Division Microélectronique et Nanotechnologies Centre de Développement des Technologies Avancées (CDTA) Algiers Algeria Division Milieux Ionisés et Laser Centre de Développement des Technologies Avancées(CDTA) Algiers Algeria Département des Sciences de la Matière Université Benyoucef Benkhedda Algiers Algeria

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

In this paper, we propose a new method based on an heuristic algorithm, which is called whale optimization algorithm (WOA) to extract the main electrical parameters of a diode, such as saturation current, ideality factor, and series resistance. WOA is an efficient optimization algorithm that imitates the humpback whales natural hunting strategy. Three operators are used to mimic humpback whales behaviors of bubble-net foraging, surrounding, and searching for prey. The findings of the suggested model are compared with analytical and experimental current-voltage data, and they are shown to be in good agreement.

关键词： Resistance Semiconductor device modeling Statistical analysis Heuristic algorithms whales whale optimization algorithms Semiconductor diodes

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39页 << < 14 15 16 17 18 19 20 21 22 23 > >>

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：