检索结果-内蒙古大学图书馆

Pulsar identification method based on adaptive grey wolf optimization algorithm in X-ray pulsar-based navigations

ADVANCES IN SPACE RESEARCH 2022年第2期69卷 1220-1235页

作者： Zhao, Hongyang Jin, Jing Shan, Bingjie Jiang, Yu Shen, Yi Harbin Inst Technol Dept Control Sci & Engn Room 502Main Bldg92 West Dazhi St Nangang Dist Harbin 150001 Peoples R China

Fast and accurate identification of pulsar signals is important for X-ray pulsar-based navigation (XNAV). Traditional pulsar signal detection technology based on FFT search and epoch folding (EF) requires a very long time to obtain an appropriate signal-to-noise ratio (SNR) gain, especially for weak pulsar signals with low photon fluxes. This paper proposes an adaptive stochastic resonance (SR) method based on the grey wolf optimizer (GWO) algorithm for fast pulsar identification. The GWO algorithm can optimize the SR parameters to obtain the optimal SR output that matches the input signal. Meanwhile, the initial population of GWO is generated by Chebyshev chaotic maps, which guarantees the diversity of the initial population and enhances the global search capability of GWO. To further improve the performance of SR, we add an extra denoising process, including a high-pass filter and singular value decomposition (SVD) that determines the effective singular components through dispersion entropy (DE), to suppress the noise to a great extent. We use simulation data and Rossi X-ray Timing Explorer (RXTE) observation data to verify the proposed method. The output SNR is greatly improved, and the method can identify pulsars in a short time. The results indicated that the proposed method has great practical value in engineering. Further experiments reveal the importance of SVD, DE and the Chebyshev chaotic map for the algorithm. (c) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Pulsar identification grey wolf algorithm Singular value decomposition Dispersion entropy Chaotic maps

来源：评论

学校读者我要写书评

暂无评论

A novel PV array interconnection scheme to extract maximum power based on global shade dispersion using grey wolf optimization algorithm under partial shading conditions

引用

CIRCUIT WORLD 2022年第1期48卷 28-38页

作者： Balraj, R. Stonier, Albert Alexander Shree Venkateshwara Hitech Engn Coll Dept Elect & Elect Engn Erode India Kongu Engn Coll Dept Elect & Elect Engn Erode India

Purpose Partial shading causes significant power decreases in the PV systems. The purpose of this paper is to address this problem, connectivity regulation is designed to reduce partial shading problems. Design/methodology/approach In this approach, the partial shading was estimated and dispersed evenly on the whole array by global shade dispersion technique (GSD). The grey wolf algorithm was implemented for the interconnection of arrays by an efficient switching matrix. Findings After the implementation of the GSD technique using a grey wolf algorithm, the performance under different shading conditions was analyzed using the MatLab simulation tool. The results were compared with total cross-tied (TCT), Su Do Ku and the proposed method of reconfiguration, where the proposed method improves the maximum power of the PV system appropriately. Research limitations/implications This methodology uses any size of PV systems. Social implications Replacement of conventional energy systems with renewable energy systems such as solar helps the environment clean and green. Originality/value The GSD interconnection scheme using the grey wolf optimization algorithm has proved an improved output performance compared with the existing TCT and Sudoku based reconfiguration techniques. By comparing with existing techniques in literature, the proposed method is more advantageous for reducing mismatch losses between the modules of any size of the PV array with less operating time.

关键词： Global maximum power point global shade dispersion grey wolf algorithm Local Maximum Powerpoint Partial shading

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Convergence Factor Based grey wolf Optimization algorithm and Load Frequency Control 5

Nonlinear Convergence Factor Based Grey Wolf Optimization Al...

引用

5th IEEE Global Power, Energy and Communication Conference (GPECOM)

作者： Hocaoglu, Gokce Sena Cavli, Nazlican Kilic, Elcin Danayiyen, Yahya Karadeniz Tech Univ Dept Elect & Elect Engn Trabzon Turkiye Erzincan Binali Yildirim Univ Tercan Vocat Sch Dept Elect & Energy Erzincan Turkiye

ISBN: (纸本)9798350301984

In this paper, various type of nonlinear convergence factors based grey wolf Optimization (GWO) is proposed to optimally compute the Proportional-Integral-Derivative (PID) controller gains to control the grid frequency in a single area power system. The PID gains in the controlled system are optimally determined by using GWO algorithm, considering the cost function, integral of time-weighted absolute error (ITAE). The efficiency of the designed control system is verified under constant and variable loads, and the comparative results are given for controlled and uncontrolled cases. Moreover, the GWO algorithm is investigated by using linear and nonlinear convergence coefficients comparatively. The model of the control system is constructed using MATLAB, and the results indicate that the transient and steady state responses are superior to those of the linear coefficients when the nonlinear convergence coefficients are applied in GWO.

关键词： grey wolf algorithm PID control load-frequency control optimization

来源：评论

学校读者我要写书评

暂无评论

Enhanced grey wolf algorithm for Energy Efficient Wireless Sensor Networks

Enhanced Grey Wolf Algorithm for Energy Efficient Wireless S...

引用

Zooming Innovation in Consumer Technologies Conference (ZINC)

作者： Zivkovic, Miodrag Bacanin, Nebojsa Zivkovic, Tamara Strumberger, Ivana Tuba, Eva Tuba, Milan Singidunum Univ Fac Informat & Comp Belgrade Serbia Univ Belgrade Sch Elect Engn Belgrade Serbia

ISBN: (纸本)9781728182599

Wireless sensor networks have entered a period of a rapid development, due to several novel technologies which have emerged in the past few years, such as Internet of Things and cloud computing. Miniature sensor nodes are integral components of numerous complex systems. The biggest problem for any wireless sensor network, in any possible application domain, is to maximize the overall network lifetime by improving the total energy consumption of the network. A large number of clustering algorithms have been implemented in the past decade, with a main goal to balance the energy consumption of each node in the network and to increase energy efficiency - the term known in literature as load balancing. One important representative of these traditional algorithms for load balancing which is still in frequent use is LEACH. On the other hand, swarm intelligence meaheuristics have recently been successfully applied in solving a large number of NP hard problems from the wireless sensor networks domain. In this paper, we propose an improved version of grey wolf algorithm, that has been applied to improve the network lifetime optimization. grey wolf algorithm was employed in forming the clusters and the cluster head selection process. As a part of our research, we have evaluated the performance of the proposed exploration enhanced grey wolf algorithm by comparing it to the traditional LEACH algorithm, basic grey wolf approach and particle swarm optimization, that were all tested under the same experimental conditions. Obtained results from conducted simulations have proven that our proposed metaheuristics performs better that other considered algorithms.

关键词： wireless sensor networks lifetime optimization metaheuristics optimization grey wolf algorithm LEACH

来源：评论

学校读者我要写书评

暂无评论

grey wolf Optimization algorithm with random local optimal regulation and first-element dominance

引用

EGYPTIAN INFORMATICS JOURNAL 2024年 27卷 1-14页

作者： Xuan, Yanzhuang Xuan, Shibin Guangxi Minzu Univ Sch Artificial Intelligence Guangxi Key Lab Hybrid Computat & IC Design Anal Daxue east Rd 188 Naning 530006 Guangxi Peoples R China

Due to the classical grey wolf algorithm GWO does not consider the characteristics of the local information of individual in population, a novel local random optimization strategy is proposed to make up for the defect of GWO. In this method, several points in the neighborhood of the current location of each individual are selected at random in the axial direction as candidates, and the best points are selected to participate in the renewal decision of the individual. Furthermore, in our experiments, a special first -element dominance characteristic is found and can greatly improve the combination effect of global and local information. In order to ensure that all constraints are not violated in the process of constraint optimization in industrial design, the random mixed population initialization method is proposed to generate population individuals that meet the constraint requirements and contain boundary values randomly. In addition, a treatment method of shrinking in a specific direction is proposed for dealing with individuals who cross the boundary. Experimental results on several test function sets show that compared with recent improved algorithms for GWO, the proposed algorithm has obvious advantages in fitness value, convergence speed and stability.

关键词： Intelligence algorithm grey wolf algorithm First-element dominance Random local optimization Random mixed population

来源：评论

学校读者我要写书评

暂无评论

Optimal Text Document Clustering Enabled by Weighed Similarity Oriented Jaya With grey wolf Optimization algorithm

引用

COMPUTER JOURNAL 2021年第6期64卷 960-972页

作者： Venkanna, Gugulothu Bharati, K. F. Jawaharlal Nehru Technol Univ Dept Comp Sci & Engn Anantapur 515002 Andhra Pradesh India Jawaharlal Nehru Technol Univ Anantapur 515002 Andhra Pradesh India

Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering model for text document using term frequency-inverse document frequency, which is considered as feature sets. Here, the initial centroid selection is much concentrated which can automatically cluster the text using weighted similarity measure in the proposed clustering process. In fact, the weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents, which is used to minimize weighted similarity among the documents. An advanced model for clustering is proposed by the hybrid optimization algorithm, which is the combination of the Jaya algorithm (JA) and grey wolf algorithm (GWO), and so the proposed algorithm is termed as JA-based GWO. Finally, the performance of the proposed model is verified through a comparative analysis with the state-of-the-art models. The performance analysis exhibits that the proposed model is 96.56% better than genetic algorithm, 99.46% better than particle swarm optimization, 97.09% superior to Dragonfly algorithm, and 96.21% better than JA for the similarity index. Therefore, the proposed model has confirmed its efficiency through valuable analysis.

关键词： text document clustering term frequency inverse document frequency Jaya algorithm grey wolf algorithm hybrid concept

来源：评论

学校读者我要写书评

暂无评论

An Improved grey wolf Optimization algorithm and its Application in Path Planning

引用

IEEE ACCESS 2021年 9卷 121944-121956页

作者： Liu, Jingyi Wei, Xiuxi Huang, Huajuan Guangxi Univ Nationalities Coll Elect Informat Nanning 530006 Peoples R China Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China

grey wolf algorithm (GWO) is a classic swarm intelligence algorithm, but it has the disadvantages of slow convergence speed and easy to fall into local optimum on some problems. Therefore, an improved grey wolf optimization algorithm(IGWO) is proposed. The lion optimizer algorithm and dynamic weights are integrated into the original grey wolf optimization algorithm. When the positions of alpha wolf, beta wolf, and delta wolf are updated, the lion optimizer algorithm is used to add disturbance factors to the wolves to give alpha wolf, beta wolf, and delta wolf active search capabilities. Dynamic weights are added to the grey wolf position update to prevent wolves from losing diversity and falling into local optimum. Through multiple benchmark function test experiments and path planning experiments, the experimental results show that the improved grey wolf optimization algorithm can effectively improve the accuracy and convergence speed, and the optimization effect is better.

关键词： grey wolf algorithm lion optimizer algorithm disturbance factors dynamic weights path planning

来源：评论

学校读者我要写书评

暂无评论

Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm

引用

ENERGY 2023年第1期285卷

作者： Zhou, Yifei Wang, Shunli Xie, Yanxing Shen, Xianfeng Fernandez, Carlos Southwest Univ Sci & Technol Engn & Technol Ctr Sch Informat Engn Mianyang Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland

The prediction of SOH for Lithium-ion battery systems determines the safety of Electric vehicles and stationary energy storage devices powered by LIBs. State of health diagnosis and remaining useful life prediction also rely significantly on excellent algorithms and effective indicators extraction. Since the data obtained from the aging experiment of Lithium-ion batteries is rich in electrochemical and dynamic information, useful health indicators can be extracted for SOH and RUL prediction of machine learning. This paper presents a method for predicting SOH and RUL based on a data-driven model of deep extreme learning machine based on improved grey wolf optimization algorithm. Firstly, GWO algorithm is improved by piecewise chaotic distribution and sine-cosine algorithm, and then multi-layer superposition is performed on an extreme learning machine to form DELM. Additionally, the experimental data of the Center for Advanced Life Cycle Engineering data set was extracted and analyzed, the aging state of batteries was analyzed and verified from multiple scales, and the strong correlation of aging characteristics was extracted and verified. After that, the model was driven by the extracted health indicators, and the accuracy and robustness of the results were checked.

关键词： Lithium-ion batteries State of health Health indicator grey wolf algorithm Deep extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

Optimal state feedback controller for three tank cylindrical interacting system using grey wolf algorithm

引用

MICROPROCESSORS AND MICROSYSTEMS 2020年 79卷 103269-103269页

作者： Anbumani, K. Hemamalini, R. Rani Sathyabama Inst Sci & Technol Chennai Tamil Nadu India SrisairamEngineeringColl Dept Elect & Instrumentat Engn Chennai Tamil Nadu India St Peters Inst Higher Educ & Res Dept Elect & Elect Engn Chennai Tamil Nadu India

This article presents, optimal state feedback controller for three tank cylindrical interacting system was proposed to maintain set point in the tank. grey wolf Optimization algorithm used to describe the optimal tuning of state feedback controller. Generally, optimal state feedback controller reduces fluctuation state with lowest control energy. Gain of the optimal state feedback controller used to reduce the fluctuation state to minimum value. By adding integrator, the measured value of the tank moves towards set point. Linear quadratic regulator is a multivariable state feedback control and it depends on the state of the system. The controller performance enhanced by suitable range of Q and R value. The proposed technique uses heuristic algorithm for tuning the state feedback gain directly. The results show that the developed control technique given better performance.

关键词： Three tank cylindrical interacting system grey wolf algorithm Linear quadratic regulator

来源：评论

学校读者我要写书评

暂无评论

A grey wolf Optimization algorithm with its application on the Controller Placement Problem

A Grey Wolf Optimization Algorithm with its application on t...

引用

International Symposium on Artificial Intelligence and Robotics

作者： Li, Yi Nanjing Univ Posts & Telecommun Coll Automat Nanjing Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing Peoples R China

ISBN: (数字)9781510646131

ISBN: (纸本)9781510646131;9781510646124

The Software Defined Networks is a popular network architecture that separates control and data plane. It is completed as a software to move control plane functions from network hardware to a controller node. To optimize the consistency of the network, a controller placement problem (CPP) could be treated as an optimization problem to minimize the latency between nodes by setting their location. In this paper, as a modern Evolutionary Computation algorithm, grey wolf optimization algorithm recently has been successfully applied into solving CPP. The experimental results demonstrate the effectiveness of the proposed algorithm.

关键词： Software Defined Networks controller placement problem grey wolf algorithm search ability

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：