检索结果-内蒙古大学图书馆

Data dissemination protocol for VANETs to optimize the routing path using hybrid particle swarm optimization with sequential variable neighbourhood search

引用

TELECOMMUNICATION SYSTEMS 2023年第2期84卷 153-165页

作者： Gopalan, S. Harihara Ashok, J. Manikandan, A. Ramalingam, S. Sri Ramakrishna Engn Coll Coimbatore India VSB Engn Coll Dept Elect & Commun Kaur India SSM Inst Engn & Technol Dept Elect & Commun Engn Dindugal 624002 India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore 642109 India

A vehicular Ad-Hoc Network (VANET) is a form of Mobile Ad-Hoc Network (MANET) which employs wireless routers that are inside every vehicle to operate as a node. The process of data dissemination is used to improve the quality of travel to avoid unnecessary accidents in VANET. Many legacy protocols use this type of messaging activity to ensure fair road safety without concern for network congestion. Node congestion increases with control of routing overhead packets. Therefore, this paper proposes a Data Dissemination Protocol (DDP). VANET routing protocols can be divided into two categories: topology-based routing protocols and location-based routing protocols. The goal is to relay emergency signals to stationary nodes as soon as possible. The standard messages will be routed to the FIFO queue. Multiple routes were found using the Time delay-based Multipath Routing (TMR) approach to transmit these messages to a destination node, and particle swarm Optimisation (PSO) is utilized to find the optimal and secure path. Sequential Variable Neighborhood Search (SVNS) algorithm is applied in order to optimize the particles' position with Local Best particle and Global Best particle (LBGB). The proposed method PSO-SVNS-LBGB is compared with different methods such as PSO-SVNS-GB, PSO-SVNS-LB, PSO-SVNS-CLB, PSO-SVNS-CGB. The experimental results show significant improvements in throughput and packet loss ratio, reduced end-to-end delay, rounding overhead ratio, and energy consumption. The simulation environment was conducted in NS2.34 is preferred for network simulation, and the VANET simulator used is SUMO and MOVE software. With a 98.41 ms delay and an average speed of 60 km/h, the PSO-SVNS-LBGB approach is suggested.

关键词： Vehicular ad-hoc networks (VANETs) Bio-inspired routing Meta-heuristics particle swarm optimization algorithm Sequential variable neighbourhood search

来源：评论

学校读者我要写书评

暂无评论

Research on identification of unbalance parameters of rotor with multi-plane using improved particle swarm optimization

引用

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2021年第2期43卷 1-12页

作者： Yun, Xialun Pang, Zhekai Jiang, Gedong Mei, Xuesong Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710048 Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Intelligent Robots Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Based on Bentley RK4 test rig, the method of nonlinear particle swarm optimization (PSO) is used to solve inverse problem instead of previous methods, which reduces identification complexity and avoids the ill posed problem caused by the singular equation in the process of solving. This paper realized model-based standard particle swarm optimization (SPSO) algorithm after modelling of the finite element model of rotor system and creating the optimization variables and individual fitness function, and then established asynchronous adaptive particle swarm optimization with optimizing speed weight and search scope. In order to further improve algorithm accuracy, chaos weighted particle swarm optimization and double chaos particle swarm optimization (DCPSO) are established. The simulation results of four methods show that DCPSO can realize accurate identification of unbalanced parameters with an average error of 2.86%. The effectiveness and rationality of this algorithm are verified from the speed of 240-3000 r/min. Particularly, at 2040 r/min, the percentage of amplitude decrease in two measuring points is 94.07% and 95.93%, which has achieved excellent vibration suppression effect.

关键词： Multi-plane rotor system Finite element model particle swarm optimization algorithm Identification of unbalanced parameters

来源：评论

学校读者我要写书评

暂无评论

Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期39卷 9027-9035页

作者： Chen, Xi Zhoukou Normal Univ Sch Mech & Elect Engn 9 Wenchang St Chuanhui Dist Zhoukou Peoples R China

During the COVID-19 pandemic, the maintenance of the wind turbine is unable to be processed due to the problem of personnel. This paper presents two neural network models: BP neural network and LSTM neural network combined with particle swarm optimization (PSO) algorithm to realize obstacle maintenance detection for wind turbine Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term memory neural network. Through the analysis of an example, it is verified that the diagnosis results of this method are consistent with the actual fault diagnosis results of wind turbine rolling bearing and the diagnosis accuracy is high. The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance when the difference of fault characteristics is not obvious, which shows the feasibility and effectiveness of the method.

关键词： Wind turbine fault diagnosis particle swarm optimization algorithm BP neural network

来源：评论

学校读者我要写书评

暂无评论

ADVANTAGES OF THERMAL INDUSTRY CLUSTER AND APPLICATION OF particle swarm optimization MODEL

引用

THERMAL SCIENCE 2021年第2期25卷 977-987页

作者： Chen, Yiwei Song, Xiaoli Bai, Yakai Hu, Chunyu Southeast Univ Sch Econ & Management Nanjing Peoples R China Zhejiang Agr & Forestry Univ Jiyang Coll Sch Humanities Hangzhou Zhejiang Peoples R China Chinese Acad Sci Beijing Peoples R China Southeast Univ Coll Civil Engn Nanjing Peoples R China

This paper first analyzes that the waste heat before the flue gas of thermal power company enters the desulfurizationwer can be heated by the low pressure economizer, and then the heated condensate can be incorporated into the regenerative system, so as to recover the waste heat and improve the unit efficiency. In this paper, the possible lay-out scheme of the low pressure economizer in the thermal system is constructed firstly, and then the mathematical model of the equivalent enthalpy drop increment of the unit is established with the water partition coefficient as the independent variable. particle swarm optimization and co-ordinate rotation are used to optimize the model, and the results show that particle swarm optimization is more effective in solving the problem. After the low pressure economizer is added to the unit, the heat consumption of the whole plant of the unit is reduced by 61.131, the unit efficiency is increased by 0.783%, and the coal consumption of the whole plant is reduced by 2.293. Finally, from the perspective of industrial agglomeration, industrial diversification and economic resilience, this paper analyzes the advantages of diversified agglomeration of thermal power companies.

关键词： flue gas waste heat low pressure economizer economic resilience particle swarm optimization algorithm industrial agglomeration industrial diversification

来源：评论

学校读者我要写书评

暂无评论

Application of particle swarm optimization to association rule mining

引用

APPLIED SOFT COMPUTING 2011年第1期11卷 326-336页

作者： Kuo, R. J. Chao, C. M. Chiu, Y. T. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Business Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan

In the area of association rule mining, most previous research had focused on improving computational efficiency. However, determination of the threshold values of support and confidence, which seriously affect the quality of association rule mining, is still under investigation. Thus, this study intends to propose a novel algorithm for association rule mining in order to improve computational efficiency as well as to automatically determine suitable threshold values. The particle swarm optimization algorithm first searches for the optimum fitness value of each particle and then finds corresponding support and confidence as minimal threshold values after the data are transformed into binary values. The proposed method is verified by applying the FoodMart2000 database of Microsoft SQL Server 2000 and compared with a genetic algorithm. The results indicate that the particle swarm optimization algorithm really can suggest suitable threshold values and obtain quality rules. In addition, a real-world stock market database is employed to mine association rules to measure investment behavior and stock category purchasing. The computational results are also very promising. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Association rule mining particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Factor analysis and forecasting of CO₂ emissions in Hebei, using extreme learning machine based on particle swarm optimization

引用

JOURNAL OF CLEANER PRODUCTION 2017年 162卷 1095-1101页

作者： Sun, Wei Wang, Caifei Zhang, Chongchong North China Elect Power Univ Dept Business Adm Baoding 071000 Peoples R China

In the prevailing low-carbon economy, China is under enormous pressure to control CO2 emissions, therefore, of great significance is the study to analyze what influential factors mainly contribute to emissions, so as to forecast emissions accurately and harness the growth from the source. In this paper, basing on 22 influencing factors identified by bivariate correlation analysis, factor analysis is then adopted to extract the latent factors which essentially affect emissions and 8 special factors transformed by scoring coefficients are acquired. Extreme learning machine (ELM) whose input weights and bias threshold were optimized by particle swarm optimization (PSO), hereafter referred as PSO-ELM, is established to predict CO2 emissions and testify the availability of the factor analysis. Case studies reveal that the factor analysis which generates 8 factors as input can highly improve prediction accuracy. And the simulation results demonstrate that the built model PSO-ELM outperforms the compared ELM and back propagation neural network in forecasting CO2 emissions. Eventually, the analysis made in this study can provide valuable policy implications for Hebei's CO2 emissions reduction and strategic low carbon development. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： CO2 emissions Extreme learning machine particle swarm optimization algorithm Factor analysis

来源：评论

学校读者我要写书评

暂无评论

Parameter identification of Hammerstein-Wiener nonlinear systems with unknown time delay based on the linear variable weight particle swarm optimization

引用

ISA TRANSACTIONS 2022年 120卷 89-98页

作者： Li, Junhong Zong, Tiancheng Lu, Guoping Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

This paper deals with the parameter estimation of Hammerstein-Wiener (H-W) nonlinear systems which have unknown time delay. The linear variable weight particle swarm method is formulated for such time delay systems. This algorithm transforms the nonlinear system identification issue into a function optimization issue in the parameter space, then utilizes the parallel searching ability of the particle swarm optimization and the iterative identification technique to realize the simultaneous estimation of all parameters and the unknown time delay. Finally, parameters in the linear submodule, nonlinear submodule and the time delay are separated from the optimum parameter. Moreover, two illustrative examples are exhibited to evaluate the effectiveness of the proposed method. The simulation results demonstrate that the derived method has fast convergence speed and high estimation accuracy for estimating H-W systems with unknown time delay, and it is applied to the identification of the bed temperature systems. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Hammerstein-Wiener system Parameter estimation Time delay particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Lyapunov Control of High-Dimensional Closed Quantum Systems Based on particle swarm optimization

引用

IEEE ACCESS 2020年 8卷 49765-49774页

作者： Guan, Xiaoke Kuang, Sen Lu, Xiujuan Yan, Jiazhen Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China

For high-dimensional closed quantum systems, this paper proposes a novel quantum Lyapunov control scheme based on the particle swarm optimization algorithm and achieves a high-probability population transfer of the system to a non-isolated target eigenstate in the LaSalle invariant set under usual smooth Lyapunov control laws. Via a quadratic Lyapunov function with unknown parameters, a control law with the unknown parameters is designed;based on the LaSalle invariance principle and the energy-level connectivity graph, the stability of the system is analyzed;by using the particle swarm optimization algorithm, a set of optimal parameters is obtained to achieve the control goal. In particular, we propose a path planning method based on the energy-level connectivity graph to determine the initial values of the unknown parameters, which is such that the optimization algorithm can efficiently and conveniently find a set of optimal solutions of the unknown parameters. Numerical simulation experiments on a five-level quantum system and a three-qubit system demonstrate that the proposed Lyapunov control scheme based on the particle swarm optimization algorithm has a good control effect.

关键词： Lyapunov methods Sociology Statistics particle swarm optimization Control systems Quantum entanglement High-dimensional quantum systems Lyapunov control particle swarm optimization algorithm parameter optimization initial values energy-level connectivity graph

来源：评论

学校读者我要写书评

暂无评论

particle-swarm-optimization-Based 2D Output Feedback Robust Constraint Model Predictive Control for Batch Processes

引用

IEEE ACCESS 2022年 10卷 8409-8423页

作者： Zhang, Wangxi Ma, Jian Wang, Liming Jiang, Feng Hainan Normal Univ Sch Math & Stat Haikou 571158 Hainan Peoples R China Hainan Normal Univ Minist Educ Key Lab Data Sci & Intelligence Educ Haikou 571158 Hainan Peoples R China Key Lab Computat Sci & Applicat Hainan Prov Haikou 571158 Hainan Peoples R China Nanjing Univ Inst Philosophy Intelligence & Future Humanity Nanjing 210093 Peoples R China Nanjing Sport Inst Sch Phys Educ & Humanities Nanjing 210014 Peoples R China

For the input and output constraints and uncertainties in batch processes, a 2D output feedback robust constrained model predictive control (MPC) method is designed by combining iterative learning control (ILC), MPC and output feedback. Firstly, an equivalent 2D-FM closed-loop prediction model is established by combining with the proposed output feedback controller. Then an optimization performance index function with terminal constraint is constructed to study its control optimization. According to the designed optimization performance index and Lyapunov stability theory, the feasible MPC problem is obtained by solving the linear matrix inequalities (LMIs). At the same time, the gain of the new output feedback control law is given to ensure that the performance index reaches the minimum upper bound under the constraints of input and output. In order to solve the manual adjustment problem of some parameters in the performance index function, the particle swarm optimization (PSO) algorithm is introduced, and a better solution is found near the controller by using the search optimization method. Finally, taking the injection molding process as an example and comparing with the existing method without using PSO algorithms, it is proved that the above method is more feasible.

关键词： Output feedback Batch production systems Predictive control Performance analysis Uncertainty Prediction algorithms particle swarm optimization Batch processes input-output constraint iterative learning output feedback predictive control particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Proposed Feature Selection particle swarm optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

引用

Computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and Information Technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computers and Information Technology Future University in EgyptCairo11835Egypt

The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of *** of the most successful optimization algorithms is particle swarm optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search *** research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO *** the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain *** proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant *** confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve ***,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different *** proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

关键词： optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

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：