检索结果-内蒙古大学图书馆

International Conference on Intelligent Computation Technology and Automation

作者： Ye Zhiwei Chen Hongwei Liu Wei Zhang Jinping Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Univ Sch Remote Sens & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)9780769533575

Image segmentation is a long-term difficult problem, which hasn't been fully solved. Thresholding is one of the most popular algorithms. particle swarm Optimization (PSO) was recent v proposed algorithm, which has been successfully applied to solve many optimization problems. Based on the analysis of Otsu threshold selection can be viewed as a continuous optimization problem. Thus, a new method to select image threshold automatically based on PSO algorithm is employed in the paper. The performance of this algorithm is compared with Otsu, and experimental results show that PSO algorithm can reveal very encouraging results in terms of the quality of solution found and the processing time required.

关键词： image thresholding parametric subharmonic oscillator particle swarm optimization algorithms particle swarm algorithm image segmentation AUTOMATIC processing time

来源：评论

学校读者我要写书评

暂无评论

Research on Coverage algorithm for Wireless Sensor Networks based on improved particle swarm optimization algorithm

Research on Coverage algorithm for Wireless Sensor Networks ...

引用

International Conference on Computer Systems, Electronics and Control (ICCSEC)

作者： Yin, Xiaoqi Guo, Yizhuo Li, Xiaofeng Wang, Xuemei Heilongjiang Coll Business & Technol Dept Comp Sci & Technol Harbin 150025 Heilongjiang Peoples R China Heilongjiang Int Univ Dept Informat Engn Harbin 150025 Heilongjiang Peoples R China

ISBN: (纸本)9781538635735

In view of the data acquisition oriented coverage and energy consumption problems in wireless sensor network, a coverage optimization method based on improved particle swarm algorithm is proposed. The algorithm is based on the improved particle swarm algorithm to compute the location of the nodes, and according to the coverage quality of network monitoring area, the reasonable mobile nodes are selected. Simulation experiments show that this algorithm uses less work nodes and can satisfy the demands of network coverage, while reduce the blind area, it also can effectively reduce the energy consumption of the whole network, prolong the survival time of the network.

关键词： Wireless sensor network particle swarm algorithm the network survival time

来源：评论

学校读者我要写书评

暂无评论

Freight Railroad Network Blocking Problem: Modeling, Formulation and Improved particle swarm Optimization algorithm

Freight Railroad Network Blocking Problem: Modeling, Formula...

引用

International Conference on Intelligent Rail Transportation (ICIRT)

作者： Zhao, Hanqing Yue, Yixiang Liu, Xiang Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China Rutgers State Univ Dept Civil & Environm Engn Newark NJ USA

ISBN: (纸本)9781538675281

In this paper, we introduce Railroad Blocking Problem (RBP) for network. Then we propose a model formulation and an improved algorithm for RBP. The objective function of the model is to minimize the total time costs of freight trains operation, including trains running time in section, accumulation and resorting time at station. The constraints include resorting capacity of stations, carrying capacity of sections, the balance of flow, etc. To solve the model for real world railroad networks, an improved hybrid particle swarm Optimization and Lagrange Relaxation (PSO-LR) algorithm is implemented. Finally, the computation results on a case of simplified China's railroad network demonstrate the effectiveness and validation of the proposed method, which shows the potential application on railroad engineering industry.

关键词： Railroad network Railroad Blocking Problem Train formation plan particle swarm algorithm Lagrange Relaxation algorithm Optimization

来源：评论

学校读者我要写书评

暂无评论

Application of particle swarm Optimization algorithm in Computer Neural Network

Application of Particle Swarm Optimization Algorithm in Comp...

引用

International Conference on Computer Systems, Electronics and Control (ICCSEC)

作者： Li, Xueyan Wuhan Univ Sci & Technol City Coll Wuhan Hubei Peoples R China

ISBN: (纸本)9781538635735

particle swarm optimization algorithm is a new intelligent optimization algorithm, which is based on the social activities of biological groups. Because the particle swarm optimization algorithm is random and uncertain, the theoretical basis of the algorithm is still not complete, for example, there are some problems, such as the premature convergence, slow convergence speed and so on. particle swarm optimization algorithm theory analysis and improvement has become the focus and hotspot of the algorithm. Therefore, based on the theory of the traditional PSO algorithm analysis based on, combined with algorithm for biological prototype features, improved PSO algorithm is proposed for the convergence of the algorithm. Because the traditional BP artificial neural network learning algorithm is an optimization algorithm based on gradient information, algorithm is easy to fall into local optimum defects. Therefore, in this paper, the improved particle swarm optimization algorithm is used in neural network training. This study not only can improve the theoretical basis of the PSO algorithm, but also provide a reference for the application of PSO algorithm in artificial neural network.

关键词： particle swarm algorithm convergence bacteria BP neural network

来源：评论

学校读者我要写书评

暂无评论

Dynamic relationship network and international management of enterprise supply chain by particle swarm optimization algorithm under deep learning

引用

EXPERT SYSTEMS 2024年第5期41卷 e13081-e13081页

作者： Chen, Min Du, Wenhu Wenzhou Univ Sch Business Wenzhou Peoples R China

The traditional enterprise decision evaluation model based on neural network has the problems of mismatch with the optimal solution and slow convergence speed. In order to enable companies to make decisions that are in line with changes in the market, the particle swarm optimization (PSO) algorithm is used to optimize deep learning neural networks. Firstly, the model parameter setting is improved, and the inertia weight strategy of normal distribution attenuation is combined. On this basis, a normal distribution decay inertial weight particle swarm optimization (NDPSO) is proposed. The inertia weight of the optimized algorithm maintains a large value in the initial stage, which makes the PSO algorithm maintain a large step size in the optimization process and a small value in the later stage. Through experimental analysis, the trend parameter of the best normal distribution of the algorithm is obtained as 0.4433 and then using the detection function, the NDPSO algorithm is tested by two types of test functions. The NDPSO algorithm is compared with the optimization results of other algorithms which are optimized on the Sphere function. The minimum value of 554.29, the average value of 2032.11, and the standard deviation of 918.47, all of them are at the leading level. Taking into account other experimental results, it is proved that the normal distribution decay inertia weight can balance the global search and local development capabilities from the perspective of parameter improvement. It can speed up the convergence with ensuring the convergence accuracy. The improved PSO algorithm has certain optimization capabilities for neural network models. The use of optimized neural network models can enable companies to make decisions in line with changes in the market and optimize the dynamic relationship network of the company's supply chain, which is of great significance to the implementation of the company's international management.

关键词： deep learning nationalized management neural networks particle swarm algorithm supply chain of enterprises

来源：评论

学校读者我要写书评

暂无评论

Anti-malicious Attack of Wireless Sensor Hospital Networks Based on Improved particle swarm Optimization algorithm

引用

JOURNAL OF TESTING AND EVALUATION 2022年第1期51卷 6-6页

作者： Liu, Wei Yin, Fei Jinling Inst Technol Sch Networking & Commun Engn Nanjing 211169 Peoples R China Water Conservancy Bur Lianyungang City Water Conservancy Project Management Off Linhong Lianyungang 222006 Peoples R China

In view of the problem that the current wireless sensor network is vulnerable to malicious at-tacks, mainly in hospitals where it is very important for data about each person to be secure, the security and stability of the wireless sensor network can be better guaranteed. An im -proved anti-malicious attack method for wireless sensors in hospital networks based on improved particle swarm optimization is proposed. The distributed network attack detection algorithm is used to calculate the trust value of the data packet in the wireless network. By setting the threshold value of the trust value, when the wireless network data packet trust value is less than the set threshold, a network malicious attack occurs. In order to protect the security of the network, it is necessary to block the malicious intrusion of the network with the lowest cost. The improved particle swarm optimization algorithm is used to solve the at-tack model, and the optimized solution of the minimum key strategy set of the attack graph is obtained, which successfully blocks the attack of the malicious network. The experimental re-sults show that the proposed method has a long duration of anti-malicious attacks on wireless sensor networks, and the network vulnerability after anti-malware attacks is low.

关键词： wireless sensing network anti-malware attack particle swarm algorithm trust value threshold

来源：评论

学校读者我要写书评

暂无评论

Optimization of Operational Route in AS/RS Based on particle swarm algorithm

引用

Journal of Modern Transportation 2008年第1期000卷

作者： HUANG Xue-fei LIU Yun-xia Department of Mechanical and Electronic Engineering Panyu Polytechnic Guangzhou 511483 China School of Economics and Management Zhejiang University of Science and Technology Hangzhou 310023 China

Optimization of the operational route in the automated storage/retrieval system (AS/RS) is transformed into the traveling salesman problem. To make the moving distance of the storage/retrieval machine shortest, we carry out a group of tests where 20 goods locations are chosed. Using PSO for operational route of AS/RS, the operation time can be shortened by about 11%. The experiments indicate that under the same conditions, the more the goods locations are, the higher the operation efficiency of the storage/retrieval machine is.

关键词： particle swarm algorithm Traveling salesman problem Automated storage/retrieval system

来源：评论

学校读者我要写书评

暂无评论

An improved competitive particle swarm optimization algorithm based on de-heterogeneous information

引用

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第6期35卷

作者： Fu, Baojun He, Yu Guo, Qiannan Zhang, Jianpei Harbin Normal Univ Coll Comp Sci & Informat Engn Harbin Peoples R China Technol Harbin Engn Univ Coll Comp Sci Harbin Peoples R China

The problem of influence dissemination under competitive conditions can truly reflect the form of information in social networks, make reasonable decisions, and analyze the impact gain of different types of messages on users, so as to maximize the benefits of users. How to accurately simulate the spread of competitive influence is still a challenging problem. In such conditions to carry out the information dissemination, viral marketing, disease prevention and other works has a more reasonable significance. However, it is NP-hard to find the most influential node set in a competitive environment. Although an approximate optimal solution can be obtained by using a greedy algorithm to simulate influence propagation under competitive conditions, the time efficiency of the process is very low. Therefore, this paper designs a bionic optimized solution to explore the above problems. Firstly, We set optimization measures to balance the convergence and diversity of the optimization algorithm in the target space, and based on this, we propose a local impact ability evaluation metric based on the de-heterogeneous information within the 2-hop neighborhood of the node set to measure the propagation ability of the node set under competitive conditions. Secondly, the proposed evaluation index is applied to the improved particle swarm optimization algorithm to realize the screening of global optimal particles. Finally, we build a game model to simulate the interest-driven information ownership and dissemination of nodes. The effectiveness and efficiency of our method are verified by comparing the propagation range of competitive influence of different methods and the time complexity of each algorithm on real data sets. & COPY;2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Maximizing competitive influence De-heterogeneous information particle swarm algorithm Game model

来源：评论

学校读者我要写书评

暂无评论

Two Kinds of Classifications Based on Improved Gravitational Search algorithm and particle swarm Optimization algorithm

引用

ADVANCES IN MATHEMATICAL PHYSICS 2017年第Pt.2期2017卷 2131862-1-2131862-7页

作者： Hu, Hongping Cui, Xiaxia Bai, Yanping North Univ China Sch Sci Taiyuan 030051 Shanxi Peoples R China

Gravitational Search algorithm (GSA) is a widely used metaheuristic algorithm. Although fewer parameters in GSA were adjusted, GSA has a slow convergence rate. In this paper, we change the constant acceleration coefficients to be the exponential function on the basis of combination of GSA and PSO (PSO-GSA) and propose an improved PSO-GSA algorithm (written as I-PSOGSA) for solving two kinds of classifications: surface water quality and the moving direction of robots. I-PSO-GSA is employed to optimize weights and biases of backpropagation (BP) neural network. The experimental results show that, being compared with combination of PSO and GSA (PSO-GSA), single PSO, and single GSA for optimizing the parameters of BP neural network, IPSO- GSA outperforms PSO-GSA, PSO, and GSA and has better classification accuracy for these two actual problems.

关键词： Two Kinds Classifications BP neural networks SEARCHING algorithm GSA particle swarm algorithm parametric subharmonic oscillator

来源：评论

学校读者我要写书评

暂无评论

Study on Ice Regime Forecast Based on SVR Optimized by particle swarm Optimization algorithm

引用

Meteorological and Environmental Research 2012年第11期3卷 36-40页

作者： WANG Fu-qiang RONG Fei North China University of Water Resources and Electric Power

[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.

关键词： particle swarm algorithm Support vector machine SVR Ice regime forecast China

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：