检索结果-内蒙古大学图书馆

An improved reactive force field parameter optimization framework based on simulated annealing and particle swarm optimization algorithms

引用

COMPUTATIONAL MATERIALS SCIENCE 2025年 251卷

作者： Sun, Qinhao Zhong, Jinhuan Shi, Pengfei Xu, Huajie Wang, Yang Southwest Jiaotong Univ Res Inst Frontier Sci Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China

Atomic-scale simulations are important tools for microscopic phenomena study and material design, especially the cost-effective and large-scale reactive force field (ReaxFF). However, the poor transferability and tedious training process of ReaxFF parameters constrain its accuracy and application, urgently requiring more efficient automatic optimization methods. In this study, we propose a multi-objective optimization method that combines simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to optimize the ReaxFF parameters. Moreover, we innovatively introduce a concentrated attention mechanism (CAM) to improve the accuracy of parameter optimization. Finally, this study selects the H/S system as the testing target to evaluate the accuracy and efficiency of the above algorithm. It is found that our algorithm is faster and more accurate than traditional metaheuristic methods. Our automated optimization scheme efficiently optimizes ReaxFF parameters, providing crucial support for atomic-scale simulations.

关键词： ReaxFF Molecular dynamics Simulated annealing algorithm particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Power stabilization method based on particle swarm optimization for electric vehicle dynamic wireless charging systems

引用

JOURNAL OF POWER ELECTRONICS 2025年 1-12页

作者： Zhang, Ming Tao, Weiye Zhang, Hantao Huaiyin Inst Technol Fac Automat Huaian Peoples R China

In the process of the dynamic wireless charging (DWC) of an electric vehicle (EV), the relative position of the coupling coil changes, causing the problem of constant fluctuations of the charging power, which affects the service life of the battery and bring safety problems. Therefore, a power fluctuation suppression method based on the particle swarm optimization (PSO) algorithm is proposed in this paper. First, based on the LCC-S compensation topology, the transmission characteristics of an EV DWC system are analyzed. Then, a DC-DC conversion circuit is added to the rectifier and voltage regulation part of the receiver, and the double closed-loop control parameters are optimized by the PSO algorithm to achieve stable charging power for EV. Finally, the effectiveness of the proposed method is verified by simulations, and compared with the coupling mechanism design and other control strategies to solve the power fluctuation problem of EV dynamic wireless charging. Moreover, experimental results show that the double closed-loop control optimized by the PSO algorithm in this paper can restore balance within 0.08 s, and the fluctuation range is controlled to within +/- 1% when the coupling coefficient changes continuously.

关键词： Electric vehicle Dynamic wireless charging particle swarm optimization algorithm Power stabilization

来源：评论

学校读者我要写书评

暂无评论

Improved Multi-Objective Strategy Diversity Chaotic particle swarm optimization of Ordered Charging Strategy for Electric Vehicles Considering User Behavior

引用

ENERGIES 2025年第3期18卷 690-690页

作者： Zhao, Shuyi Ma, Chenshuo Cao, Zhiao Northeastern Univ Sydney Smart Technol Coll Qinhuangdao 066004 Peoples R China Taylors Univ Sch Accounting & Finance Selangor 47500 Malaysia Natl Univ Def Technol Sch Elect Sci Changsha 410073 Peoples R China Guangdong Commun Polytech Sch Intelligent Transportat Engn Guangzhou 510800 Peoples R China

With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user satisfaction to solve the charging and discharging scheduling problem of EVs. This article adds an objective function to quantify user satisfaction and addresses the issues of premature local optima and insufficient diversity in the MOPSO algorithm. Based on the performance of different particles, the algorithm assigns elite particle, general particle, and learning particle roles to the particles and assigns strategies for maintaining search, developing search, and learning search, respectively. In order to avoid falling into local optima, chaotic sequence perturbations are added during each iteration process avoiding premature falling into local optima. Finally, case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results show that the proposed algorithm is capable of achieving up to 23% microgrid load reduction and up to 20% improvement in convergence speed compared to other algorithms. It is superior to other algorithms in solving the problem of orderly charging and discharging of electric vehicles and has strong usability and feasibility.

关键词： electric vehicles orderly charging and discharging tent chaotic sequence perturbation particle swarm optimization algorithm multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Image Multi-Threshold Segmentation Based on Variable Precision Rough Set and K-L Roughness particle swarm optimization

引用

TEHNICKI VJESNIK-TECHNICAL GAZETTE 2025年第2期32卷 704-712页

作者： She, Zhiyong Song, Tao Zhang, Dongpo Feng, Yueping Xinjiang Univ Polit Sci & Law Informat Network Secur Coll Tumxuk 844000 Xinjiang Peoples R China 52 Qianhai East St Tumushuk City Xinjiang Peoples R China Geely Univ China Sch Intelligence Technol Chengdu 610000 Sichuan Peoples R China 123 Sect 2Chengjian Ave Chengdu Sichuan Peoples R China Jilin Univ Sch Comp Sci & Technol Changchun 130012 Peoples R China 2699 Qianjin St Changchun Jilin Peoples R China

This paper proposes an image multi-threshold segmentation algorithm based on variable precision rough sets and K-L roughness particle swarm optimization. The algorithm does not require a priori knowledge outside the image and employs variable precision rough sets to address the uncertainty problem in image segmentation. The optimal segmentation threshold is obtained by combining K-L divergence and roughness, and an improved particle swarm optimization algorithm is used to enhance segmentation efficiency. Experimental results demonstrate that the proposed algorithm effectively solves the uncertainty problem in segmentation and achieves better segmentation performance compared to other algorithms.

关键词： K-L roughness divergence multi threshold segmentation particle swarm optimization algorithm variable precision rough set

来源：评论

学校读者我要写书评

暂无评论

A particle swarm optimization and constraint programming-based approach for integrated process planning and scheduling with lot streaming problem

引用

APPLIED SOFT COMPUTING 2025年 174卷

作者： Zhang, Mengya Li, Xinyu Gao, Liang Liu, Qihao Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

This paper studies the integrated process planning and scheduling with lot streaming (IPPS-LS) problem, which consists of lot splitting, process planning, and shop scheduling. Although the IPPS-LS problem is common in the manufacturing of flexible process products, it has not been extensively studied due to its high complexity. Hence, this study develops an enhanced particle swarm optimization algorithm based on constraint programming (CP) to minimize makespan. The proposed algorithm employs finite condition and relaxation models for particle reconfiguration and re-optimization. To achieve it, two types of relaxation models are constructed by decomposing the multiple constraints of the CP model. The algorithm dynamically updates particle encoding sequences based on model accuracy, effectively reducing invalid searches and accelerating the search process. The proposed algorithm is compared with models and other metaheuristic algorithms on 120 test instances. The impact of the relaxed CP strategy and particle swarm optimization algorithm on the proposed algorithm performance is also analyzed. Finally, a significance of difference validation is performed. Computational experiments demonstrate the efficiency of the proposed algorithm in solving the IPPS-LS problem of varying scales. In addition, the relaxed CP strategy exhibits a more significant improvement effect for medium-scale problems compared to small and large-scale problems.

关键词： Integrated process planning and scheduling Lot streaming Constraint programming particle swarm optimization algorithm Relaxation model

来源：评论

学校读者我要写书评

暂无评论

CMPSO: A novel co-evolutionary multigroup particle swarm optimization for multi-mission UAVs path planning

引用

ADVANCED ENGINEERING INFORMATICS 2025年 63卷

作者： Hu, Gang Cheng, Mao Houssein, Essam H. Jia, Heming Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Minia Univ Fac Comp & Informat Al Minya Egypt Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China

To cope with the situation where an unmanned aerial vehicle (UVA) needs to perform missions to multiple locations, this paper presents a new multi-mission UAVs path planning model and proposes a novel co-evolutionary multigroup particle swarm optimization (CMPSO) for solving this complex model. In this model, a new ball curve, the ball lambda-Bezier curve (B lambda B), is used to represent the path of UAVs. In particular, UAV needs to satisfy G 0 and G 1 continuity at the must-pass points. Using this as a basis, a new model is built to generate a feasible path that is safe, smooth and constrained by the angle of climb and flight altitude. To solve this model efficiently, CMPSO framed by two novel different grouping learning mechanisms is proposed. Two different group learning mechanisms, grouping based on fitness values and activity level, replace the original speed and position update methods in PSO. The grouping mechanism based on the activity level uses the median of the velocity vector modes as a criterion to divide the whole population into two. They effectively facilitate the transfer of information between particles. In addition, a mutation mechanism based on the activity level is introduced to address the defect of PSO's proneness to converge to local optima. By comparing CMPSO with 15 excellent meta- heuristics at CEC 2017, CMPSO is ranked first with an average ranking of 3.72. Also, CMPSO has the best and most stable performance on 18 of the 21 engineering application problems. Finally, CMPSO is applied to three different environments of the path planning model. CMPSO outperforms the other compared algorithms in all three environments with a success rate of 100. This shows the efficiency and practicality of CMPSO in facing complex path planning problems.

关键词： UAVs path planning particle swarm optimization algorithm Group learning mechanisms

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Scheduling Method for Standalone Microgrids Based on Deep Q-Network and particle swarm optimization

引用

ENERGIES 2025年第8期18卷 2133-2133页

作者： Zhang, Borui Liu, Bo Hunan Agr Univ Coll Informat & Intelligence Changsha 410128 Peoples R China

Standalone wind-solar-diesel-storage microgrids serve as a crucial solution for achieving energy self-sufficiency in remote and off-grid areas, such as rural regions and islands, where conventional power grids are unavailable. Addressing scheduling optimization challenges arising from the intermittent nature of renewable energy generation and the uncertainty of load demand, this paper proposes an adaptive optimization scheduling method (DQN-PSO) that integrates Deep Q-Network (DQN) with particle swarm optimization (PSO). The proposed approach leverages DQN to assess the operational state of the microgrid and dynamically adjust the key parameters of PSO. Additionally, a multi-strategy switching mechanism, incorporating global search, local adjustment, and reliability enhancement, is introduced to jointly optimize both clean energy utilization and power supply reliability. Simulation results demonstrate that, under typical daily, high-volatility, and low-load scenarios, the proposed method improves clean energy utilization by 3.2%, 4.5%, and 10.9%, respectively, compared to conventional PSO algorithms while reducing power supply reliability risks to 0.70%, 1.04%, and 0.30%, respectively. These findings validate the strong adaptability of the proposed algorithm to dynamic environments. Further, a parameter sensitivity analysis underscores the significance of the dynamic adjustment mechanism.

关键词： standalone microgrids adaptive method reinforcement learning particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

High performance deep violet InGaN TQW laser diode through multi-layer thickness optimization using particle swarm optimization method

引用

OPTICS AND LASER TECHNOLOGY 2025年 181卷

作者： Edalati, Batool Goharrizi, Azita Zandi Alahyarizadeh, Ghasem Kargaran, Hamed Grad Univ Adv Technol Dept Photon Kerman Iran Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Semicond Grp Kerman Iran Shahid Beheshti Univ Fac Engn Tehran Iran Shahed Univ Fac Engn Tehran Iran

The paper details the optimization of efficiency parameters for a InGaN TQW laser diode using the particle swarm optimization algorithm through SILVACO software. By adjusting layer thickness based on output power, slope efficiency, and threshold current, significant enhancements were achieved. Three optimized laser diodes (LD1, LD2, LD3) were compared with a primary LD, revealing notable improvements in output power and overall performance. Analysis of carrier recombination rates in the active region highlighted increased stimulated recombination and decreased non-radiative recombination, particularly in the n-side well. The study also showed a more uniform radiative recombination in the quantum wells of the optimized LDs, especially in LD3. Investigation of electron and hole concentrations, current densities, and energy levels demonstrated higher electron and hole current density in the optimized LDs, with LD3 exhibiting substantial improvements over the primary LD. Notably, the optimized LDs displayed reduced threshold current and improved slope efficiency compared to the primary LD. The study identified optimal layer thicknesses based on various cost functions, resulting in significant enhancements in output power (0.561 W), slope efficiency (2.515 W/A), and reduced threshold current (less than 0.029 A), indicative of enhanced TQW laser diode performance. Overall, the research showcases the effectiveness of the particle swarm optimization approach in optimizing GaN-based TQW laser diodes, leading to improved efficiency characteristics and demonstrating the potential for enhanced performance in practical applications.

关键词： InGaN TQW laser diode Numerical Simulation particle swarm optimization algorithm Layer thickness Performance characteristics

来源：评论

学校读者我要写书评

暂无评论

A new particle swarm optimization-enhanced deep neural network for automatic ECG arrhythmias classification

引用

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2025年 1-15页

作者： Kahlessenane, Yaaqoub Bouaziz, Fatiha Siarry, Patrick Jijel Univ Elect Dept BP 98 Ouled Aissa Jije 18000 Algeria Jijel Univ Mechatron Lab LMT BP 98 Ouled Aissa Jijel 18000 Algeria Paris East Creteil Univ Lab Images Signals & Intelligent Syst LISSI Paris France

This study proposes an ECG classification system using particle swarm optimization (PSO) for automated deep neural network hyperparameter tuning. PSO optimizes five key parameters: neuron counts in two fully connected layers, dropout rate, learning rate, and optimizer selection. ECG signals undergo wavelet decomposition for feature extraction, with classification performed on the MIT-BIH Arrhythmia Database across five heartbeat classes. The PSO-optimized model achieves superior performance with 99.76% accuracy, 99.34% precision, and 99.21% F1 score, demonstrating PSO's effectiveness in improving model reliability while reducing manual effort.

关键词： Classification approach deep neural network particle swarm optimization algorithm feature extraction hyperparameters

来源：评论

学校读者我要写书评

暂无评论

An IoT-based English translation and teaching using particle swarm optimization and neural network algorithm

引用

SOFT COMPUTING 2023年第19期27卷 14431-14450页

作者： Zhang, Lili Anhui Univ Finance & Econ Sch Languages & Media Bengbu 233000 Anhui Peoples R China

English language translation teaching plays a crucial role in today's globalized world, particularly in countries like China, which has seen an increased emphasis on English language proficiency. However, effective teaching and assessment of students' translation abilities remain significant challenges for educators. To address the challenges of English language translation and teaching, this paper proposes a novel approach that integrates Internet of Things (IoT), particle swarm optimization (PSO), and Neural Network algorithms to assist teachers in evaluating and assessing students' English translation abilities by enabling them to better fulfill their teaching responsibilities. This paper first describes the computational mechanism of Neural Network algorithm using PSO, which includes the particle coding approach. An application model is designed to assess students' capacity for learning using English translational instruction. The PSO algorithm gathers information on their learning progress, and fully utilize the analyzed results to develop learning strategies and create teaching materials for various learning types, which is helpful for the quick advancement of English translation teaching. Secondly, this paper develops a translational application model for English language as a practical framework. The proposed method utilizes the upgraded PSO-enabled Neural Network to process English language translation and teaching data by achieving network training in a global optimal state of PSO by reducing training errors. Thirdly, this paper evaluates the effectiveness of the model by comparing the average errors of the training and test samples with different numbers of particles such as 5, 10, and 20. Finally, the results demonstrate high accuracy in measuring students' translation abilities. Furthermore, the study collects and examines three data sets, specifically English-Foreign Language Translation Corpus (EFLT), English Language Learners' Corpus (ELLC), and Engl

关键词： Internet of Things particle swarm optimization algorithm Neural network Network training English translation Smart classrooms Smart services

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：