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PGA: A new particle swarm optimization algorithm based on genetic operators for the global optimization of clusters

JOURNAL OF COMPUTATIONAL CHEMISTRY 2024年第32期45卷 2764-2770页

作者： Wang, Kai Henan Univ Urban Construct Henan Engn Res Ctr Bldg Photovolta Sch Math & Phys Pingdingshan 467036 Peoples R China

We have developed a global optimization program named PGA based on particle swarm optimization algorithm coupled with genetic operators for the structures of atomic clusters. The effectiveness and efficiency of the PGA program can be demonstrated by efficiently obtaining the tetrahedral Au-20 and double-ring tubular B-20, and identifying the ground state ZrSi17-20- clusters through the comparison between the simulated and the experimental photoelectron spectra (PESs). Then, the PGA was applied to search for the global minimum structures of Mg-n(-) (n = 3-30) clusters, new structures have been found for sizes n = 6, 7, 12, 14, and medium-sized 21-30 were first determined. The high consistency between the simulated spectra and the experimental ones once again demonstrates the efficiency of the PGA program. Based on the ground-state structures of these Mg-n(-) (n = 3-30) clusters, their structural evolution and electronic properties were subsequently explored. The performance on Au-20, B-20, ZrSi17-20-, and Mgn- (n = 3-30) clusters indicates the promising potential of the PGA program for exploring the global minima of other clusters. The code is available for free upon request.

关键词： clusters genetic operators global optimization particle swarm optimization algorithm

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Comparing the performance of genetic algorithm and particle swarm optimization algorithm in allocating and scheduling fire stations

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2025年第1期22卷 445-458页

作者： Kheirdast, A. Jozi, S. A. Rezaian, S. Tehrani, M. M. E. Islamic Azad Univ Fac Marine Sci & Technol Dept Environm Management North Tehran Branch Tehran Iran Islamic Azad Univ Dept Environm North Tehran Branch Tehran Iran Islamic Azad Univ Dept Environm Shahrood Branch Shahrood Iran Islamic Azad Univ Dept Environm North Tehran Banch Tehran Iran

Genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) have positive effects on the allocation and scheduling of the stations, this research seeks to find which one of these two methods is more appropriate to shorten the time to reach fire/incident site in the Region 19 of Tehran. This is an applied type of research. Data analysis was carried out using NFPA standards and MATLAB software. The statistical population includes 8 fire stations and 250 personnel of the stations, and sampling volume was obtained using Morgan's table (n = 148). In order to efficiently assign and schedule fire stations to arrive at the site, a linear numerical programming model was presented with the aim of minimizing the arrival time and taking into account the effect of firemen's fatigue (alpha = 0.1). Findings of the research showed that the operation processing time (of fire extinguishing) had a normal distribution with a mean of 40 min and a variance of 10 min, independent of the severity of the incident. Also, fatigue coefficient was calculated 0.1 by analyzing the sensitivity of the solution time of the algorithm with changes [0-1]. Initial standard travel time, with an average speed of 47 km/h and a density factor of 1.24, was 5min:20s. Solving the problem in large and small dimensions showed that the initial power effect of each fire station is 0.36 according to the fatigue level of the forces. Based on the obtained results, GA performs better in terms of problem solution time, and the improved PSOA also has higher quality answers.

关键词： Genetic algorithm particle swarm optimization algorithm Firefighting Fire Incident The region 19 of Tehran city

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

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年第5期20卷 3319-3333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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Low Carbon optimization Scheduling of Micro Grid Based on Improved particle swarm optimization algorithm

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IEEE ACCESS 2024年 12卷 76432-76441页

作者： Sang, Yingjun Zhang, Wenzhi Ma, Jing Chen, Quanyu Tao, Jinglei Fan, Yuanyuan Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Fac Math & Phys Huaian 223003 Peoples R China

This article proposes a low-carbon operation analysis method for micro grids based on improved particle swarm optimization algorithm. Corresponding improvements have been made to the inertia weight, learning factor, and individual extreme of the algorithm, depicting the comprehensive low-carbon operation information of micro grids under the influence of carbon emission quotas and carbon trading mechanisms from the perspective of data visualization. The low-carbon scheduling of micro grids is carried out from three perspectives: environmental protection, economy, and comprehensiveness, which compensates for the limitations of focusing on traditional low-carbon operation and provides a powerful tool for analyzing low-carbon operation of micro grids. Firstly, establish the energy consumption cost and carbon emission cost functions of the micro grid system, add the two cost functions together and take the minimum sum to form the objective function of this article. Then, based on the characteristics of each unit, a low-carbon model is constructed to constrain the carbon emissions of each unit. Finally, simulation analysis was conducted on the micro grid system based on the improved particle swarm optimization algorithm, verifying the effectiveness and practicality of the proposed algorithm. The simulation results show that the improved particle swarm optimization algorithm can quickly and effectively reduce energy consumption and carbon emission costs, and improve the comprehensive efficiency of micro grid systems.

关键词： Costs Carbon dioxide Turbines Photovoltaic systems State of charge Generators Wind power generation Microgrids particle swarm optimization Carbon emissions Emissions trading Micro grid low carbon particle swarm optimization algorithm carbon emissions carbon trading mechanism

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Multi-feature fusion sonar image target detection evaluation based on particle swarm optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 739-751页

作者： Lei, Hongquan Li, Diquan Jiang, Haidong Cent South Univ Sch Geosci & Infophys Changsha Hunan Peoples R China Guizhou Inst Technol Inst Resources & Environm Engn Guiyang 550003 Guizhou Peoples R China

Traditional sonar image target detection analysis has problems such as long detection time, low detection accuracy and slow detection speed. To solve these problems, this paper will use the multi-feature fusion sonar image target detection algorithm based on the particle swarm optimization algorithm to analyze the sonar image. This algorithm uses the particle swarm algorithm to optimize the combination of multiple feature vectors and realizes the adaptive selection and combination of features, thus improving the accuracy and efficiency of sonar image target detection. The results show that: when other conditions are the same, under the particle group optimization algorithm, the sonar image multiple feature detection algorithm for three sonar image detection time between 4s-9.9s, and the sonar image single feature detection algorithm of three sonar image detection time between 12s-20.9s, shows that the PSO in multiple feature fusion sonar image target detection with better performance and practicability, can be effectively applied to the sonar image target detection field.

关键词： Sonar images particle swarm optimization algorithm target detection multi-feature fusion single multi-feature fusion

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Hybrid particle swarm optimization algorithm for text feature selection problems

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NEURAL COMPUTING & APPLICATIONS 2024年第13期36卷 7471-7489页

作者： Nachaoui, Mourad Lakouam, Issam Hafidi, Imad Natl Sch Appl Sci ENSA Khouribga 25000 Morocco Univ Sultan Moulay Slimane Fac Sci & Tech Equipe Math & Interact Beni Mellal Morocco

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set.

关键词： Text feature selection particle swarm optimization algorithm Genetic algorithm Constriction factor Chaotic map K-mean text clustering algorithm

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

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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

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

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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

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State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine

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Green Energy and Intelligent Transportation 2024年第1期3卷 46-54页

作者： Kui Chen Jiali Li Kai Liu Changshan Bai Jiamin Zhu Guoqiang Gao Guangning Wu Salah Laghrouche School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China FEMTO-ST UMR CNRS 6174and FCLABFR CNRS 3539UniversitéBourgogne Franche-ComtéBelfortUTBM90000France

Lithium-ion battery State of Health(SOH)estimation is an essential issue in battery management *** order to better estimate battery SOH,Extreme Learning Machine(ELM)is used to establish a model to estimate lithium-ion battery *** swarm optimization algorithm(PSO)is used to automatically adjust and optimize the parameters of ELM to improve estimation ***,collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity *** Grey Relation Analysis(GRA)method to analyze the correlation between battery capacity and five characteristic ***,an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics,and a PSO is introduced to optimize the parameters of the capacity estimation *** proposed method is validated by the degradation experiment of the lithium-ion battery under different *** results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation,and the average absolute percentage error is less than 1%.

关键词： Lithium-ion battery State of health estimation Grey relation analysis method particle swarm optimization algorithm Extreme learning machine

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