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Structural optimization and Characteristic Analysis of TMPS-HEG Based on particle swarm optimization algorithm

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年 1-15页

作者： Cao, Zhenghao Zhao, Chaohui Gao, Hangyu Shanghai Dianji Univ Sch Elect Engn Shanghai Peoples R China

This paper addresses the issue of poor magnetic performance in the tangential concentrated magnetic parallel structure hybrid excitation generator (TMPS-HEG). It proposes improving its excitation performance by adding magnetic barriers to the rotor core yoke. First, the influence of three different magnetic barrier structures on the generator's air-gap flux density is analyzed. The structure with higher air-gap flux density is selected for further analysis of the generator's characteristics and voltage regulation. Next, to achieve a higher three-phase no-load back electromotive force (EMF), a multi-objective optimization of the magnetic barrier parameters, the dimensions of the permanent magnets, and the stator slot parameters is carried out using particle swarm optimization (PSO). Finally, the generator characteristics and voltage regulation are compared before and after the addition of the magnetic barrier and optimization. Simulation results show that with an excitation current If = 0 A, the no-load back EMF increased by 44.71% after adding the magnetic barrier. Further optimization led to an additional 18.65% increase in the no-load back EMF. The optimized generator exhibits a more symmetric no-load characteristic curve, with significantly improved weak magnetic performance and a stiffer external characteristic. In terms of regulation, the optimized magnetic barrier reduces the required excitation current for the same load, and the voltage regulation remains below 35%. These results provide new insights for the optimization design of hybrid excitation generators.

关键词： Rotor core yoke Magnetic barrier Excitation characteristics particle swarm optimization algorithm No-load back EMF

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All-dielectric metasurface based on particle swarm optimization algorithm for generating high-purity vortex pulses

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OPTICS COMMUNICATIONS 2025年 584卷

作者： Xu, Jianghong Yuan, Huan Zhong, Zheqiang Zhang, Bin Sichuan Univ Coll Elect & Informat Engn Chengdu 610064 Sichuan Peoples R China Big Data Anal & Fus Applicat Technol Engn Lab Sich Chengdu 610065 Peoples R China

Ultra-short vortex pulses have attracted widespread attention due to their unique properties and extensive applications in fields such as high-field laser physics, high-energy density physics, micro-nano manipulation, and quantum entanglement. We propose a metasurface based on the particle swarm optimization algorithm for converting ultrashort gaussian pulses into high-purity ultrashort vortex pulses, operating within a broadband wavelength range of 750 nm-850nm. The results indicate that the mode purity of the vortex pulses generated by the designed metasurfaces with topological charges l = 2 and l = 10 is higher than 99.5 % and 98.0 %, respectively, within the bandwidth, and they exhibit good robustness. Furthermore, similar methods can be extended to design metasurfaces that generate other topological charges. The proposed metasurface design and optimization method are significant for reducing strong distortions caused by dispersion and for applications in ultra-short vortex pulse systems.

关键词： All-dielectric metasurface particle swarm optimization algorithm High-purity vortex pulses

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Improved particle swarm optimization algorithm with Logistic Function and Trigonometric Function for Three-dimensional Path Planning Problems

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ENGINEERING LETTERS 2025年第2期33卷 442-459页

作者： Wang, Yu-Bo Wang, Jie-Sheng Sui, Xiao-Fei Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

particle swarm optimization algorithm is a widely used swarm intelligence algorithm. Aiming at the path length, algorithm stability, running time and other problems obtained by the PSO algorithm for solving the three-dimensional path planning problem, an improved particle swarm optimization algorithm based on Logistic function and trigonometric function is proposed to solve the three-dimensional path planning problem. Two types and six modification strategies for the inertia weights and learning factors of PSO algorithm are proposed, the two types are PSO algorithm based on the change of inertia weights omega of different functions, and LWDPSO algorithm and TCPSO algorithm based on the change of C-1 and C-2 of different learning functions, and these two algorithms are combined into an Improved particle swarm optimization algorithm (IPSO). Finally, the feasibility and effectiveness of the proposed method is verified by simulating the UAV flying over the mountain peaks, and the effect of the change of inertia weight and learning factor on the PSO algorithm is discussed by classifying the IPSO algorithm with the LWDPSO algorithm and the TCPSO algorithm, and finally comparing the IPSO algorithm with the PSO algorithm, the genetic algorithm, and the artificial swarm algorithm, and the simulation results show that the proposed IPSO algorithm is more advantageous compared with the other algorithms. The simulation results show that the proposed IPSO algorithm is more advantageous than other algorithms and can better accomplish the path planning task.

关键词： 3D path planning particle swarm optimization algorithm Inertia weights Learning factors

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Research on a hybrid model for flood probability prediction based on time convolutional network and particle swarm optimization algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Yu, Qiying Liu, Chengshuai Li, Runxi Lu, Zhenlin Bai, Yungang Li, Wenzhong Tian, Lu Shi, Chen Xu, Yingying Cao, Biao Zhang, Jianghui Hu, Caihong Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Xinjiang Inst Water Resources & Hydropower Res Urumqi 830049 Xinjiang Peoples R China

Accurate flood forecasting in advance is crucial for planning and implementing watershed flood prevention measures. This study developed the PSO-TCN-Bootstrap flood forecasting model for the Tailan River Basin in Xinjiang by integrating the particle swarm optimisation (PSO) algorithm, temporal convolutional network (TCN), and Bootstrap probability sampling method. Evaluated on 50 historical flood events from 1960 to 2014 using observed rainfall-runoff data, the model showed, under the same lead time conditions, a higher Nash efficiency coefficient, along with lower root mean square and relative peak errors in flood forecasting. These results highlight the PSO-TCN-Bootstrap model's superior applicability and robustness for the Tailan River Basin. However, when the lead time exceeds 5 h, the model's relative peak error remains above 20%. Future work will focus on integrating flood generation mechanisms and enhancing machine learning models' generalisability in flood forecasting. These findings provide a scientific foundation for flood management strategies in the Tailan River Basin.

关键词： Flood forecasting Machine learning Temporal convolutional neural network particle swarm optimization algorithm Bootstrap probability sampling algorithm PSO-TCN model Tailan River Basin

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Cloud computing task scheduling based on particle swarm optimization algorithm

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Procedia Computer Science 2025年 261卷 1349-1355页

作者： Yan Han Department of Science and Research Chengdu Polytechnic Chengdu 610041 China

The task scheduling of cloud computing is the key to cloud services. It is an intelligent scheduling strategy that reasonably allocates tasks in the cloud computing platform to meet users’ resource requirements. A good cloud task scheduling strategy should not only meet the needs of users, but also improve the utilization of cloud computing resources and reduce energy consumption as much as possible. However, the traditional cloud task scheduling algorithm is mainly implemented through experience and manual intervention, and its efficiency and result quality are not ideal. In this paper, a cloud computing task scheduling method based on particle swarm optimization algorithm is proposed, and the influence of this algorithm on task completion time and energy consumption in cloud computing environment is analyzed through experiments.

关键词： particle swarm optimization algorithm Cloud computing Task scheduling Cloud technology

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particle swarm optimization algorithm based on parameter improvements

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2017年第3期17卷 557-568页

作者： Wu, Qinghua Song, Tao Liu, Hanmin Yan, Xuesong Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Hubei Peoples R China China Univ Geosci Ctr Network & Educ Technol Wuhan 430074 Hubei Peoples R China Wuhan Inst Ship Bldg Technol Wuhan 430050 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China

Through the researches on various item improvements of particle swarm optimization algorithm, the improved particle swarm optimization algorithm is proposed for the insufficient global searching ability. This paper has improved the parameters of velocity formula and introduced new distance calculation formula to modify various parameters in the velocity formula. The improved particle swarm optimization algorithm has been tested by adopting standard test function, and the result has been compared with other particle swarm optimization algorithms. The experimental result indicated that the proposed improved particle swarm optimization algorithm in this paper got the better result for function optimization problems.

关键词： particle swarm optimization algorithm parameter improvement particle learning factor inertia weight

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particle swarm optimization algorithm for Neuro-Fuzzy Prospectivity Analysis Using Continuously Weighted Spatial Exploration Data

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NATURAL RESOURCES RESEARCH 2019年第2期28卷 309-325页

作者： Roshanravan, Bijan Aghajani, Hamid Yousefi, Mahyar Kreuzer, Oliver Shahrood Univ Technol Fac Min Petr & Geophys Shahrood *** Iran Malayer Univ Fac Engn Malayer Iran Corp Geosci Grp POB 5128 Rockingham Beach WA 6969 Australia James Cook Univ Sch Earth & Environm Sci EGRU Townsville Qld 4811 Australia

Classification of spatial exploration data for exploration targeting using neuro-fuzzy models means that the many spatial values have to be simplified and assigned to a few classes. The simplification of complex geological information, which illustrates a high degree of variability, results in overly simplistic models based on the presumption of homogeneous earth. However, such an assumption is not valid. In this paper, we illustrate the superiority of using continuously weighted spatial evidence values compared to discretely weighted evidence data, and how continuously weighted spatial evidence values can increase the efficiency of neuro-fuzzy exploration targeting models. The results of this study demonstrate that neuro-fuzzy targeting model generated with continuously weighted spatial evidence values is superior to that of the neuro-fuzzy model generated with discretely weighted exploration evidence data.

关键词： Continuous weighting Exploration targeting Neuro-fuzzy particle swarm optimization algorithm

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particle swarm optimization algorithm with Variety Inertia Weights to Solve Unequal Area Facility Layout Problem 32

Particle Swarm Optimization Algorithm with Variety Inertia W...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhou, Jia-Li Wang, Jie-Sheng Zhang, Yong-Xin Guo, Qing-Sheng Li, Hui Lu, Yi-Xuan Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

ISBN: (纸本)9781728158556

The unequal area facility layout problem (UA-FLP) is to place some objects in a specified space according to certain requirements, which is a NP-hard problem in mathematics because of the complexity of its solution, the combination explosion and the complexity of engineering system. particle swarm optimization (PSO) algorithm is a kind of swarm intelligence algorithm by simulating the predatory behavior of birds. Aiming at the minimization of material handling cost and the maximization of workshop area utilization, the optimization mathematical model of UA-FLPP is established, and it is solved by the particle swarm optimization (PSO) algorithm which simulates the design of birds' predation behavior. The improved PSO algorithm is constructed by using nonlinear inertia weight, dynamic inertia weight and other methods to solve static unequal area facility layout problem. The effectiveness of the proposed method is verified by simulation experiments.

关键词： particle swarm optimization algorithm Inertia Weight Unequal Area Facility Layout Problem

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particle swarm optimization algorithm can Promote Cooperation in Public Goods Game with Costly Punishment 39

Particle Swarm Optimization Algorithm can Promote Cooperatio...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Zhao, Xiaowei Liu, Guangrui Xu, Xiujuan Ilan, Xu Dalian Inst Technol Sch Software Dalian Peoples R China Liaoning Normal Univ Sch Management Dalian Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Previous researches have well demonstrated the importance of costly punishment for promoting the evolution of cooperation in spatial public goods games, while it remains unconsidered sides about the role of strategy-updating mechanisms. Inspired by the algorithms in the field of Machine Learning, in this paper, we propose a game strategy updating mechanism based on particle swarm optimization algorithm for spatial public goods game with continuous strategies, and explore the impact of tolerance-based punishment mechanisms on the evolution of cooperation. The results of simulation experiments show that the particle swarm optimization algorithm can effectively promote cooperation under appropriate parameter settings. This result reveals that hybrid learning is more conducive to maintaining cooperation than a single learning mechanism (social learning or self-learning), and can prevent the spread of betrayal and maintain a high level of cooperation when betrayal strategies invade.

关键词： public goods game particle swarm optimization algorithm spatial reciprocity evolution of cooperation

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particle swarm optimization algorithm for Power Scheduling Problem Using Smart Battery

Particle Swarm Optimization Algorithm for Power Scheduling P...

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IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)

作者： Makhadmeh, Sharif Naser Khader, Ahamad Tajudin Al-Betar, Mohammed Azmi Naim, Syibrah Alyasseri, Zaid Abdi Alkareem Abasi, Ammar Kamal Univ Sains Malaysia Sch Comp Sci George Town Malaysia Al Balqa Appl Univ Dept Informat Technol Irbid Jordan Univ Kufa ECE Dept Fac Engn Najaf Iraq

ISBN: (纸本)9781538679425

Power Scheduling Problem (PSP) is a problem of schedule the smart home appliances at appropriate time period according to an electricity pricing scheme. The smart home appliances can be scheduled by shifting their time operations from period to another. The significant objective of the scheduling process is to reduce the electricity bill and Peak-to-average ratio (PAR) and improve the user comfort level. In this paper, particle swarm optimization (PSO) algorithm is adapted in order to handle the PSP and to obtain an optimal smart home appliances schedule. Smart battery (SB) is formulated and used in this work to enhance the schedule of the appliances by storing the power at low peak periods and use the stored power by the smart home appliances at peak periods. The simulation results proved the efficiency of using the proposed SB in terms of reducing electricity bill and improving the user comfort level. In addition, PSO is compared with genetic algorithm (GA) in order to evaluate its performance. PSO outperforms GA in terms of achieving the PSP objectives.

关键词： optimization particle swarm optimization algorithm Power Scheduling Problem Smart Battery

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