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

331 篇 会议
47 篇 期刊文献

馆藏范围

378 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

378 篇 whale optimizati...
117 篇 optimization
84 篇 accuracy
53 篇 heuristic algori...
49 篇 feature extracti...
43 篇 prediction algor...
41 篇 metaheuristics
41 篇 predictive model...
37 篇 convergence
37 篇 costs
35 篇 whales
31 篇 simulation
31 篇 stability analys...
30 篇 particle swarm o...
28 篇 resource managem...
25 篇 power system sta...
23 篇 classification a...
22 篇 internet of thin...
22 篇 energy consumpti...
22 篇 data models

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

378 篇 英文

检索条件"主题词=whale optimization algorithms"

共 378 条记录，以下是151-160 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：

Calibration of Fractional-order Car-following Model Using Improved whale optimization Algorithm

Calibration of Fractional-order Car-following Model Using Im...

引用

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

作者： Yongchen Fu Wei Hu Yibing Cui Wenjuan Gu Rui Jiang Yongguang Yu School of Traffic and Transportation Beijing Jiaotong University Beijing China School of Systems Science Beijing Jiaotong University Beijing China School of Mathematics and Statistics Beijing Jiaotong University Beijing China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Intelligent driver model (IDM) is a famous car-following model in the traffic flow theory to illustrate the traffic phenomenon. While fractional-order calculus can precisely depict and predict various complex phenomena with the memory effect and nonlinear characteristics. Thus, this paper creatively proposes a fractional-order intelligent driver model (FIDM). Besides, to calibrate the model more accurately, we innovatively put forward a novel improved whale optimization algorithm, named as WOAFSC, fused with fractional-order strategy and stochastic center search strategy. Finally, the proposed FIDM and WOAFSC are validated on three data sets separately. Experimental results demonstrate their stronger robustness and higher accuracy. It should be noted that this research can provide theoretical support and practical guidance for fractional-order calculus in the fields of swarm intelligence optimization algorithms and car-following behavior modeling.

关键词： Accuracy Computational modeling Stochastic processes Search problems Calculus whale optimization algorithms Stability analysis Calibration optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

DNA-Encoded Design of Arithmetic optimization Algorithm Fused With whales

DNA-Encoded Design of Arithmetic Optimization Algorithm Fuse...

引用

Machine Intelligence Theory and Applications (MiTA), International Conference on

作者： Linpeng Xie Changjun Zhou Riheng Jia College of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

DNA coding is a branch of DNA computation, and the quality of DNA coding is an important indicator that affects the accuracy of DNA computation, and how to optimize the quality of encoding and design a better DNA coding sequence is a problem that needs to be solved, and DNA encoding optimization refers to the selection of the optimal DNA code from the DNA coding set that satisfies the constraints. In this paper, the arithmetic optimization algorithm fused with whales is used to optimize the DNA sequence. The whale strategy is used to make up for the shortcomings of some arithmetic optimization algorithms. Through the DNA sequence optimization design, the obtained results are compared with those of predecessors, and most of the experimental results are better than those of predecessors, which further illustrates the feasibility of the algorithm.

关键词： Codes Accuracy DNA Gaussian distribution Encoding whale optimization algorithms Biological information theory optimization Machine intelligence Arithmetic

来源：评论

学校读者我要写书评

暂无评论

Optimal Power Flow of Power Systems Using Hybrid whale and Particle Swarm optimization Technique

Optimal Power Flow of Power Systems Using Hybrid Whale and P...

引用

IEEE International Conference on Energy Internet (ICEI)

作者： Huanyou Wang Guangqi Xie Yingying Zhan School of Mechanical and Electrical Engineering Guangdong University of Science and Technology Dongguan China Academy of Electronic Information and Electrical Engineering Xiangnan University Chenzhou China

ISBN: (数字)9798331523558

ISBN: (纸本)9798331523565

In order to ensure population diversity and overcome premature convergence, differential evolution strategy was introduced and a whale particle swarm hybrid algorithm was proposed. The proposed method has been achievemented on the IEEE 30-bus test system considering three objective functions, such as line loss, generation cost, and weighted generation cost and voltage offset. By comparing whale particle swarm hybrid algorithm, whale optimization algorithm and particle swarm optimization algorithm, the experimental results show that whale particle swarm hybrid algorithm is more stable and better than the two other optimization algorithms in optimizing power flow.

关键词： Costs System verification Voltage whale optimization algorithms Hybrid power systems Particle swarm optimization MATLAB optimization Load flow Convergence

来源：评论

学校读者我要写书评

暂无评论

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

Chaotic Lévy whale optimization Algorithm with Simulated Annealing and Differential Evolution

Chaotic Lévy Whale Optimization Algorithm with Simulated An...

引用

IEEE International Conference on Networking, Sensing and Control

作者： Jing Bi Wenduo Gu Haitao Yuan Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Eng. Beihang University Beijing China

ISBN: (纸本)9781665440493

Typical whale optimization Algorithm (WOA) suffers from slow convergence, early trapping into local optima, and weak exploration ability. This work aims to solve these problems by combining Differential Evolution (DE), chaos theory, Lévy flight, and Simulated Annealing (SA). This work designs a Chaotic Differential whale optimization based on Simulated annealing and Levy flight (CDWSL). CDWSL improves randomness of solutions, and reduces the possibility of falling into local optima. CDWSL is evaluated with ten typical functions, two composite ones and a real-world one of computation offloading in vehicular edge computing. In addition, experiments on higher- dimension problems are also conducted to evaluate the performance of CDWSL. The comparison of experimental results shows that CDWSL achieves superior performance over its typical state-of-the-art peers in solving both low-dimension and high- dimension simple benchmark problems. Furthermore, CDWSL yields better results for both composite benchmark functions and the real-world computation offloading problem than some typical algorithms.

关键词： Chaos Shape Conferences Simulated annealing Benchmark testing whale optimization algorithms Sensors

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

A novel whale optimization algorithm and its application in metallurgical process

A novel whale optimization algorithm and its application in ...

引用

International Conference on Information Science and Control Engineering (ICISCE)

作者： Liu Yunlian Wu Tiebin Cai Xia Li Bo School of Information Hunan University of Humanities Science and Technology Loudi China School of Energy and Electromechanical Engineering Hunan University of Humanities Science and Technology Loudi China

To solve the limitations existing in the standard whale optimization algorithm (WOA), a convergence factor based on the sine function is designed to balance the exploration and development abilities of the WOA, furthermore, an improved opposition-based learning strategy is proposed to accelerate the convergence rate of the algorithm, in addition, the mutation operation based on the population diversity is employed to reduce the probability that the algorithm gets caught in the local optimum, the simulation result shows that the novel WOA presents a superior convergence rate and accuracy to the basic WOA.

关键词： Information science Simulation Sociology whales whale optimization algorithms Robustness Statistics

来源：评论

学校读者我要写书评

暂无评论

Single neuron adaptive control and parameter optimization of lower limb rehabilitation exoskeleton

Single neuron adaptive control and parameter optimization of...

引用

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhirui Li Wenjie Chen Xiantao Sun Weihai Chen Zaojun Fang Guilin Yang School of Electrical Engineering and Automation Anhui University Hefei China School of Automation Science and Electrical Engineering Beihang University Beijing China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9781665409858

For the bottom position control of the lower limb exoskeleton robot driven by DC motor, a single neuron adaptive control method with parameters selected by the improved whale optimization algorithm is proposed. Since the parameter tuning process existing in traditional control is cumbersome and it is difficult to achieve optimal control accuracy, we present a single neuron adaptive controller to improve the control accuracy. In addition, in order to complete the optimal selection of controller parameters, the whale optimization algorithm is used to select parameters, this algorithm is improved, and the optimization simulation results are displayed. Finally, an adaptive underlying controller is designed, and the simulation results are compared with the traditional PID control. The results show that, compared with the traditional method, the single neuron adaptive control with the optimized parameters of the improved whale optimization algorithm has better control effect.

关键词： PI control Simulation Neurons Exoskeletons Sociology whale optimization algorithms PD control

来源：评论

学校读者我要写书评

暂无评论

Particle Swarm optimization-Based Deep Neural Network vs whale optimization Algorithm-Based Deep Convolutional Neural Networks for Critical Heat Flux Prediction

Particle Swarm Optimization-Based Deep Neural Network vs Wha...

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Messaoud Djeddou Jehad Al Dallal Aouatef Hellal Ibrahim A. Hameed Xingang Zhao Hydraulic Department Oum El-Bouaghi University Ain Beida Algeria Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Kuwait Department of ICT and Natural Sciences Norwegian University of Science and Technology (NTNU) Alesund Norway Department of Nuclear Engineering University of Tennessee Knoxville Knoxville USA

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Critical heat flux (CHF) denotes the thermal limit at which a phase change occurs during heating, that causes a significant decline in heat transfer efficiency and eventual localized overheating of the heating surface. This study applies two hybrid deep learning models: a Deep Convolutional Neural Network (CNN) optimized with the whale optimization Algorithm and a Deep Neural Network (DNN) optimized with Particle Swarm optimization, to predict Critical Heat Flux (CHF). The results show that the latter model outperforms the former in terms of performance, simplicity, and computation efficiency in the CHF prediction task.

关键词： Heating systems Deep learning Training Computational modeling Predictive models Prediction algorithms whale optimization algorithms Convolutional neural networks Particle swarm optimization Heat transfer

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

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38页 << < 12 13 14 15 16 17 18 19 20 21 > >>

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：