检索结果-内蒙古大学图书馆

The Method to Integrate Species Explode and Deracinate algorithm With particle swarm optimization algorithm

IEEE ACCESS 2024年 12卷 52439-52451页

作者： Yang, Yongjian Deng, Youwei Xiao, Bingsong Zhao, Xiaohong Air Force Engn Univ Aviat Engn Sch Xian 710038 Peoples R China

Effectively combining various evolutionary computing algorithms and leveraging the advantages of each can significantly enhance the convergence speed and solution quality of the algorithm. However, a mere combination of evolutionary computing algorithms may not comprehensively improve optimization performance and may even lead to poorer performance in certain optimization problems. The aim of the paper is to provide a fundamental integrating platform and method based on species explode and deracinate algorithm. Utilizing the species explode and deracinate algorithm as a foundation, this study presents a hybrid algorithm named SED-PSO algorithm by utilizing the particle swarm optimization algorithm as an exemplar. The outcomes of the simulations conducted on 27 benchmark functions published by the Competition on Evolutionary Constrained demonstrate that the SED-PSO algorithm exhibits exceptional convergence accuracy, robust stability, and rapid convergence speed. The simulation results comprehensively illustrate that the species explode and deracinate algorithm serves as a fundamental integrating platform for diverse evolutionary computing algorithms, while also incorporating the strengths of each algorithm. Additionally, the outcomes of the optimization of sensor network coverage reveal that the SED-PSO algorithm exhibits superior solution quality, minimal occurrence of local extremum, and enhanced stability and efficacy.

关键词： Evolutionary computing algorithm species explode and deracinate algorithm particle swarm optimization algorithm competition on evolutionary constrained optimization of sensor network coverage

来源：评论

学校读者我要写书评

暂无评论

Implementation of genetic and particle swarm optimization algorithm for voltage profile improvement and loss reduction using capacitors in 132 kV Manipur transmission system

引用

ENERGY REPORTS 2023年 9卷 738-746页

作者： Bidyanath, Khaidem Singh, Sanasam Dhanabanta Adhikari, Shuma NIT Manipur Imphal 795004 Manipur India

Complex power plays a key role in maintaining and sustaining the fields i.e. magnetic and electric fields. The efficiency of the power system depends entirely on the loss and voltage profile level of the system. The lower the level of loss and the higher the voltage profile, the higher the system efficiency. Installation of the capacitors on a system greatly helps in controlling and reducing levels of the reactive energy of the system. The location and size of the capacitors that need to be installed are of great importance in the installation of the bank of capacitors. The idea of a Genetic algorithm and also a particle swarm algorithm are deployed in this paper for calculating the optimal location and size of the capacitor. In this paper, the system of the 132 KV Manipur Transmission System is designed in ETAP considering a radial distribution part and the optimization method of a genetic method, and also the particle swarm algorithm is utilized for finding the optimal location and size of the capacitor. The system is further optimized by reducing the loss and improving the voltage profile of the system. And the comparison of both the optimization techniques is presented. The results show that PSO provides optimal solutions while GA is simpler in the optimization process. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Etap Matlab Reactive power Genetic algorithm particle swarm optimization algorithm Optimal capacitor placement Power loss

来源：评论

学校读者我要写书评

暂无评论

Sequence Transfer-Based particle swarm optimization algorithm for Irregular Packing Problems

引用

IEEE ACCESS 2021年 9卷 131223-131235页

作者： Fang, Jie Rao, Yunqing Liu, Pan Zhao, Xusheng Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The two-dimensional (2D) irregular packing problem is a classical optimization problem with NP-hard characteristics and has high computational complexity. To date, packing problems have generally been solved by artificial experience and heuristic algorithms. However, these algorithms are not highly efficient and the excellent cases cannot be preserved, which both time and economic costs are high. Inspire by transfer learning and considering the characteristics of 2D irregular packing problems, we propose a sequence transfer-based particle swarm optimization algorithm (ST-PSO) to solve the multi-constraint packing problem. A piece-matching strategy based on an improved shape context algorithm, and a piece-sequencing generation strategy for transferring the packing sequence are developed for particle swarm optimization(PSO) initialization. In the process of PSO, an adaptive adjustment strategy is used with an improved positioning strategy to adjust the packing position of the pieces. The results indicate that this method can robustly, quickly, and efficiently achieve the packing of 2D irregular pieces. Compared with the data prior to transfer, the ST-PSO can inherit and transfer the historical packing sequence in less time and retain or exceed the actual packing data onto the samples. This algorithm could be applied to industrial applications to reduce waste, packing time, and production costs.

关键词： Heuristic algorithms Task analysis Licenses Transfer learning Shape Feature extraction optimization Irregular pieces multiple constraints particle swarm optimization algorithm sequence transfer two-dimensional packing optimization

来源：评论

学校读者我要写书评

暂无评论

Analysis of uniform circular arrays for adaptive beamforming applications using particle swarm optimization algorithm

引用

INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2008年第1期18卷 42-52页

作者： Mahmoud, Korany R. Eladawy, Mohamed I. Bansal, Rajeev Zainud-Deen, Saber H. Ibrahem, Sabry M. M. Helwan Univ Dept Elect Commun & Comp Fac Engn Cairo Egypt Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Menoufia Univ Dept Elect & Elect Commun Engn Fac Elect Engn Menoufia Egypt

In this article, the particle swarm optimization algorithm is used to calculate the complex excitations, amplitudes and phases, of the adaptive circular array elements. To illustrate the performance of this method for steering a signal in the desired direction and imposing nulls in the direction of interfering signals by controlling the complex excitation of each array element, two types of arrays are considered. A uniform circular array (UCA) and a planar uniform circular array (PUCA) with 16 elements of half-wave dipoles are examined. Also, the performance of an adaptive array using 3-bit amplitude and 4-bit phase shifters are studied. In our analysis, the method of moments is used to estimate the response of the dipole UCAs in a mutual coupling environment.

关键词： smart antennas adaptive beamforming method of moments mutual coupling uniform circular arrays particle swarm optimization algorithm adaptive array

来源：评论

学校读者我要写书评

暂无评论

Inverse calculation of vessel emission source intensity based on optimized Gaussian puff model and particle swarm optimization algorithm

引用

MARINE POLLUTION BULLETIN 2024年第Pt A期209卷 117117页

作者： Wu, Hao Li, Xueyao Wang, Chao Ye, Zhirui Jinling Inst Technol Sch Network &Commun Engn Nanjing Peoples R China Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Wuhan Hubei Peoples R China Southeast Univ Sch Transportat Nanjing Peoples R China Southeast Univ Sch Econ & Management Nanjing Peoples R China

Maritime transportation has significantly contributed to global economic development but is also a major source of air pollution. This study aims to provide an inverse calculation framework of vessel emission source intensity for emission monitoring. This research enhanced the traditional Gaussian diffusion model by specific characteristics of ship emissions and various influencing factors identified through simulation experiments. An inverse model is developed using pattern search and particle swarm optimization (PSO) algorithms to estimate marine exhaust source strengths. Results indicate that the PSO algorithm is the most accurate and efficient, especially with an iteration step size of 0.1 s. Practical application using data from 86 monitored ships revealed that 76 had fuel sulfur content exceeding the 0.1 % threshold, achieving an accuracy rate of 88.37 %. These findings are crucial for improving the understanding of marine exhaust dispersion and advancing remote monitoring technologies, contributing to better environmental management of maritime transport emissions.

关键词： Gaussian model Inverse calculation particle swarm optimization algorithm Vessel emission

来源：评论

学校读者我要写书评

暂无评论

An extension theory-based maximum power tracker using a particle swarm optimization algorithm

引用

ENERGY CONVERSION AND MANAGEMENT 2014年 86卷 435-442页

作者： Chao, Kuei-Hsiang Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan

The aim of this work is to present an adaptive maximum power point tracking (MPPT) approach for photovoltaic (PV) power generation system. Integrating the extension theory as well as the conventional perturb and observe method, an maximum power point (MPP) tracker is made able to automatically tune tracking step size by way of the category recognition along a P-V characteristic curve. Accordingly, the transient and steady state performances in tracking process are improved. Furthermore, an optimization approach is proposed on the basis of a particle swarm optimization (PSO) algorithm for the complexity reduction in the determination of weighting values. At the end of this work, a simulated improvement in the tracking performance is experimentally validated by an MPP tracker with a programmable system-on-chip (PSoC) based controller. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Maximum power point tracking Extension theory particle swarm optimization algorithm Programmable system-on-chip

来源：评论

学校读者我要写书评

暂无评论

An Improved DV-Hop Scheme Based on Path Matching and particle swarm optimization algorithm

引用

WIRELESS PERSONAL COMMUNICATIONS 2019年第4期104卷 1301-1320页

作者： Shi, Qinqin Xu, Qiang Zhang, Jianping Shanghai Inst Technol Sch Comp Sci & Informat Engn Shanghai 201418 Peoples R China Suzhou Zhongke Adv Technol Res Inst Co Ltd Suzhou 215123 Peoples R China Sino Parking Tech Co Ltd Shanghai 200127 Peoples R China

Distance vector hop (DV-Hop) is a frequently-used localization technology for wireless sensor networks. The traditional DV-Hop scheme estimates the node-anchor distance depending on the hop-count between the network nodes. It is the advantage of the scheme because no costive direct range finding is needed, but it still is the disadvantage of the scheme because the heterogeneity of network topology will make the node-anchor distance estimation precision poor and the localization precision unstable. Since the heterogeneity of network topology is very common due to random node deployment in real applications, the effectiveness of DV-Hop scheme in these applications becomes difficult to confirm and the algorithm needs applicability improvement. Focusing on above problem of the traditional DV-Hop, improved strategies are provided.A path matching algorithm is presented to find out the optimal anchor-anchor shortest path, which is used to determine the average hop distance between anunknown node and its target anchor independently, aiming at making the estimated node-anchor distance as close as possible to the real distance;furtherly, a modified particle swarm optimization algorithm is presented to optimize the initial position of each unknown node, aiming at improving the whole node localization accuracy of the network. Simulations are carried out on different network topologies both in square area and in C-shaped area, and comparisonsaremade for our scheme with the traditional DV-Hop and the other three existed representative improved schemes. Results show that our scheme has better performance both on distance estimation accuracy and on average nodelocalization accuracy.

关键词： DV-Hop Node localization Path matching Trilateration particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Sensorless rotor field direct orientation controlled induction motor drive with particle swarm optimization algorithm flux observer

引用

JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2019年第2期38卷 692-705页

作者： Luo, Yung-Chang Huang, Wei-An Natl Chin Yi Univ Technol Dept Elect Engn 57Sec 2Zhongshan Rd Taichung 41170 Taiwan

A speed estimation scheme based on the particle swarm optimization algorithm flux observer is proposed for a sensorless rotor field direct orientation controlled induction motor drive. The stator current and rotor flux was used to establish both the rotor field direct orientation controlled induction motor drive and the rotor-flux observer. The estimated synchronous angle position was acquired from a current-and-voltage parallel-model rotor estimator for implementation of the exact coordinate transformation to achieve a perfect rotor field direct orientation controlled induction motor drive. The rotor-flux observer was designed using the Lyapunov stability theory, and the estimated rotor speed was derived from the developed the rotor-flux estimator;this estimated speed was unaffected by the slip speed. The gain matrix of this flux observer was obtained using the particle swarm optimization algorithm because it is simple, achieves rapid convergence, and is suitable for a variety of conditions. This system was simulated using the MATLAB/Simulink (R) toolbox, and all the control algorithms were realized by a TI DSP 6713-and-F2812 control card. Both simulation and experimental results confirmed the effectiveness of the proposed approach.

关键词： Sensorless rotor field direct orientation control flux observer flux estimator particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved particle swarm optimization algorithm and its application in text feature selection

引用

APPLIED SOFT COMPUTING 2015年 35卷 629-636页

作者： Lu, Yonghe Liang, Minghui Ye, Zeyuan Cao, Lichao Sun Yat Sen Univ Sch Informat Management Guangzhou 510275 Guangdong Peoples R China

Text feature selection is an importance step in text classification and directly affects the classification performance. Classic feature selection methods mainly include document frequency (DF), information gain (IG), mutual information (MI), chi-square test (CHI). Theoretically, these methods are difficult to get improvement due to the deficiency of their mathematical models. In order to further improve effect of feature selection, many researches try to add intelligent optimization algorithms into feature selection method, such as improved ant colony algorithm and genetic algorithms, etc. Compared to the ant colony algorithm and genetic algorithms, particle swarm optimization algorithm (PSO) is simpler to implement and can find the optimal point quickly. Thus, this paper attempt to improve the effect of text feature selection through PSO. By analyzing current achievements of improved PSO and characteristic of classic feature selection methods, we have done many explorations in this paper. Above all, we selected the common PSO model, the two improved PSO models based respectively on functional inertia weight and constant constriction factor to optimize feature selection methods. Afterwards, according to constant constriction factor, we constructed a new functional constriction factor and added it into traditional PSO model. Finally, we proposed two improved PSO models based on both functional constriction factor and functional inertia weight, they are respectively the synchronously improved PSO model and the asynchronously improved PSO model. In our experiments, CHI was selected as the basic feature selection method. We improved CHI through using the six PSO models mentioned above. The experiment results and significance tests show that the asynchronously improved PSO model is the best one among all models both in the effect of text classification and in the stability of different dimensions. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Text classification Text feature selection particle swarm optimization algorithm Constriction factor

来源：评论

学校读者我要写书评

暂无评论

Software cost estimation predication using a convolutional neural network and particle swarm optimization algorithm

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Draz, Moatasem. M. Emam, Osama Azzam, Safaa. M. Kafrelsheikh Univ Fac Comp & Informat Software Engn Dept Kafrelsheikh Egypt Helwan Univ Fac Comp & Artificial Intelligence Informat Syst Dept Helwan Egypt

Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation.

关键词： Software cost estimation Software effort estimation Promise database SCE Convolutional neural network particle swarm optimization algorithm PSO CNN

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：