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Hybrid Improved pso algorithm for Soil Property Parameter Estimation

APPLIED SCIENCES-BASEL 2025年第8期15卷 4451-4451页

作者： Li, Mude Shi, Aiping Shi, Yefan Zhang, Tao Qu, Cu Ye, Lihua Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Energy & Power Engn Zhenjiang 212013 Peoples R China

This study proposes a hybrid pso-EDO algorithm, integrating Particle Swarm Optimization (pso) and the Exponential Distribution Optimizer (EDO) for efficient and accurate estimation of soil property parameters. The proposed algorithm combines the strengths of Standard pso (Spso) and the Exponential Distribution Optimizer (EDO). Three key innovations are introduced: (1) SPM chaotic mapping enhances initial population diversity;(2) dynamic inertia weight balances global exploration and local exploitation;(3) the memoryless property of EDO improves escape capability from local optima. Benchmark tests demonstrate that pso-EDO achieves near-theoretical optimal convergence errors (mean error <= 10-16 for unimodal functions such as F1 and F2) and reduces the computation time by 14.5% compared to EDO. For multimodal functions (e.g., F3), pso-EDO significantly outperforms pso-WOA (Particle Swarm Optimization-Whale Optimization algorithm) with a 22.3% reduction in error. Simulation experiments further validate its engineering practicality: in soil parameter estimation, pso-EDO completes 1000 iterations in just 1.95 s, with key parameters (e.g., sinkage coefficient n) controlled within a 7.32% error margin. This provides an efficient solution for real-time traversability assessment of autonomous vehicles on soft terrains.

关键词： pso algorithm EDO algorithm soil property estimation off-road vehicles terrain mechanics parameter optimization autonomous vehicles

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Neural emulator for nonlinear systems based on pso algorithm: real-time validation

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第4期27卷 5107-5119页

作者： Farhat, Yassin Atig, Asma Zribi, Ali Ben Abdennour, Ridha Univ Gabes Natl Engn Sch Gabes Res Lab Numer Control Ind Proc Omar Ibn Khattab St Gabes 6029 Tunisia Univ Tabuk King Faisal Rd Tabuk 47512 Saudi Arabia

Searching an optimal value of the neural emulator adaptive rate presents a great problem. Indeed, a new scheme of neural emulators based on the Particle Swarm Optimization (pso) algorithm for nonlinear systems is adopted in this paper. The main goal of this approach consists in adjusting effectively the neural emulator adaptive rate in order to accelerate the convergence speed and to improve the precision degree. The obtained results are compared with those reached with an intelligent tuning strategy. An experimental validation of the new emulator adaptation is carried on chemical reactor. Efficiency of the proposed method is proved according to the obtained performances.

关键词： Neural emulation pso algorithm Adaptive rate Nonlinear systems Experimental validation Chemical reactor

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Bayesian network structure learning based on HC-pso algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第2期46卷 4347-4359页

作者： Gao, Wenlong Zhi, Minqian Ke, Yongsong Wang, Xiaolong Zhuo, Yun Liu, Anping Yang, Yi Lanzhou Univ Sch Publ Hlth Inst Hlth Stat & Intelligent Anal Lanzhou Gansu Peoples R China Lanzhou Univ Sch Publ Hlth Dept Epidemiol & Hlth Stat Lanzhou Gansu Peoples R China Lanzhou Univ Sch Math & Stat Lanzhou Gansu Peoples R China

Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-pso combining the HC (Hill Climbing) algorithm and pso (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses pso algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-pso algorithm is superior in feasibility and accuracy.

关键词： Bayesian network structure learning HC algorithm pso algorithm DE algorithm

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Optimisation of decision making for construction projects of assembly buildings based on improved pso algorithm

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ARCHIVES OF CIVIL ENGINEERING 2025年第1期71卷 113-125页

作者： Wang, Peng Henan Finance Univ Fac Engn & Econ Zhengzhou 451464 Peoples R China

A cutting-edge construction method called fitted construction allows for several parallel lines of work to speed up construction and enhance building quality. However, achieving optimal project decisions for global construction projects demands a high level of objective decision-making. To enhance the decision- making process, this research utilizes particle swarm algorithms to optimize construction project decisions in assembled buildings. To tackle the issue of early convergence in particle swarm algorithms, three swarm enhanced particle swarm algorithms are proposed by merging the variational mechanism of the differential evolution algorithm and quantifying the decision making tasks for assembly building construction projects to be solved by the enhanced particle swarm algorithm. Regarding the research results, the upgraded particle swarm algorithm achieved a fundamental convergence in 20 iterations whilst resolving the Sphere, Rosebrock, Rastrigin, and Griewank functions. The improved particle swarm algorithm converges to an optimal solution of -19.208 within 20 iterations on the Holder function, with an optimal domain of [8.055, -9.665]. The results of the optimization study for the decision-making problem of the assembly building project demonstrate that implementing Sigmoid smoothing yields a minimum duration problem of 0.755 and a minimum duration of 45 days. The optimal cost and time required to solve the problem of economic maximisation strategy using the enhanced particle swarm method are 500,000 and 52 days, respectively. The results indicate that the improved particle swarm approach outperforms conventional algorithms in the decision-making process for assembly building projects, maintaining computational accuracy throughout.

关键词： assembly building DE algorithm global optimisation search project decision making pso algorithm

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Design of dual-band Wilkinson power divider based on novel stubs using pso algorithm

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INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2023年第9期15卷 1495-1506页

作者： Zonouri, Seyed Abed Hayati, Mohsen Bahrambeigi, Mehran Razi Univ Fac Engn Dept Elect Engn Kermanshah 67149 Iran

In this paper, a new dual-band Wilkinson power divider (WPD) is designed and fabricated using novel low and high impedance stubs instead of quarter-wavelength transmission lines. The proposed circuit was analyzed using odd and even mode analysis, and the optimal values of design parameters were obtained using the particle swarm optimization algorithm. The designed power divider has input reflection coefficients (S-11) of -22.1 and -17 dB at the first operating frequency of 2.2 GHz and the second operating frequency of 14.2 GHz, respectively. It also improves stop-band and fractional bandwidth (FBW) while maintaining a simple topology. The proposed WPD suppresses undesired harmonics from the second to the fifth with an attenuation level of less than -20 dB in the first band and generates a broad stop-band (4.4-11.5 GHz). In the first band, the FBW is 54.5%, and in the second band, it is 20.1%.

关键词： Dual-band low and high impedance stub pso algorithm rectangular-shaped resonators Wilkinson power divider

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Automatic back analysis of mechanical parameters using block discrete element method and pso algorithm

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2023年第7期27卷 2576-2586页

作者： Ma, Hang-Sheng Wang, Huan-Ling Wang, Ru-Bin Meng, Qing-Xiang Yang, Lan-Lan Hohai Univ Res Inst Geotech Engn Nanjing Peoples R China Hohai Univ Minist Educ Geomech & Embankment Engn Key Lab Nanjing Peoples R China Hohai Univ Key Lab Coastal Disaster & Def Minist Educ 1 Xikang Rd Nanjing Jiangsu Peoples R China Jiangnan Univ Sch Environm & Civil Engn Wuxi Jiangsu Peoples R China

This article presents a novel automatic pso-based back analysis method for mechanical parameters of block discrete element method. Incorporating particle swarm optimization (pso) algorithm and recursive algorithm, a parameter back-analysis program is developed. Two cases, a laboratory test of columnar jointed rock mass and a typical landslide, are implemented for validation. The results show that the proposed method is feasible and accurate. As an automatic solution for parameter back analysis, this work has broad application prospects in geotechnical engineering.

关键词： Automatic back analysis method pso algorithm discrete element method parameter back-analysis program

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A New Tree-Level Multi-Objective Forest Harvest Model (MO-pso): Integrating Neighborhood Indices and pso algorithm to Improve the Optimization Effect of Spatial Structure

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FORESTS 2023年第3期14卷 441页

作者： Qiu, Hanqing Zhang, Huaiqing Lei, Kexin Hu, Xingtao Yang, Tingdong Jiang, Xian Chinese Acad Forestry Inst Forest Resource Informat Tech Beijing 100091 Peoples R China NFGA Key Lab Forest Management & Growth Modelling Beijing 100091 Peoples R China Natl Long term Sci Res Base Huangfengqiao Forest M Beijing 100091 Peoples R China Guizhou Normal Univ Sch Geog & Environm Sci Guiyang 550025 Peoples R China

Accurate, efficient, impersonal harvesting models play a very important role in optimizing stand spatial structural and guiding forest harvest practices. However, existing studies mainly focus on the single-objective optimization and evaluation of forest at the stand- or landscape-level, lacking considerations of tree-level neighborhood interactions. Therefore, the study explored the combination of the pso algorithm and neighborhood indices to construct a tree-level multi-objective forest harvest model (MO-pso) covering multi-dimensional spatial characteristics of stands. Taking five natural secondary forest plots and thirty simulated plots as the study area, the MO-pso was used to simulate and evaluate the process of thinning operations. The results showed that the MO-pso model was superior to the basic pso model (pso) and random thinning model Monte Carlo-based (RD-TH), DBH dominance (DOMI), uniform angle (ANGL), and species mingling (MING) were better than those before thinning. The multi-dimensional stand spatial structure index (L-index) increased by 1.0%similar to 11.3%, indicating that the forest planning model (MO-pso) could significantly improve the spatial distribution pattern, increase the tree species mixing, and reduce the degree of stand competition. In addition, under the four thinning intensities of 0% (T1), 15% (T2), 30% (T3), and 45% (T4), L-index increased and T2 was the optimal thinning intensity from the perspective of stand spatial structure overall optimization. The study explored the effect of thinning on forest spatial structure by constructing a multi-objective harvesting model, which can help to make reasonable and scientific forest management decisions under the concept of multi-objective forest management.

关键词： forest planning stand spatial structure selection thinning priority pso algorithm neighborhood-based indices

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Texture shape optimization induced higher load carrying capacity of sealing ring based on improved pso algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY 2023年第3期237卷 469-483页

作者： Gong, Ran Cheng, Xing Jiao, Mei Jiangsu Univ Dept Automot Engn Zhenjiang 212013 Peoples R China China North Vehicle Res Inst Beijing Peoples R China

An improved particle swarm optimization (pso) algorithm is presented to optimize the texture shape of the sealing ring in order to enhance the load carrying capacity (LCC). The shape optimization uses the unrelated generation method of the texture curves, and two splines are applied to fit the boundary of the texture to improve the continuity. The inertia factor is adjusted through the differential strategy to enhance the local optimization capacity. The optimization model is verified by two optimization cases. The optimized texture is bullet-shaped. The increase of inlet oil pressure transforms the optimized texture from the bullet shape to the inner spiral shape. In addition, the increase of the texture depth leads to the gradual elongation of the texture shape. The increase in the number of textures flattens the seal texture. The results shows that the improved pso algorithm has high global optimization ability of the texture shape for the sealing ring in the vehicle transmission through appropriate parameter settings.

关键词： Sealing ring texture shape optimization load carrying capacity pso algorithm

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Towards alleviating operating conditions of MPC controlled VSC for DFIG-Based Wind Turbines using pso algorithm 6

Towards alleviating operating conditions of MPC controlled V...

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6th Global Power, Energy and Communication Conference (GPECOM)

作者： Ali, Youssef Ait Ouassaid, Mohammed Mohammed Univ Rabat Mohammadia Sch Engn Morocco Engn Smart & Sustainable Syst Res Ctr Rabat Morocco

ISBN: (纸本)9798350351088;9798350351095

In this paper, an enhanced FCS-MPC control scheme is introduced for DFIG-based wind turbines. Controllers employing FCS-MPC to govern power converters often exhibit elevated switching frequencies, leading to notable switching losses and the potential for damaging power converters. This paper addresses this issue by using the pso meta-heuristic algorithm to optimize switching frequencies. Notably, the optimization is performed while adhering to the IEEE Standards' waveform requirements for the current injected into the PCC. FCS-MPC with a single step prediction horizon performs well but leads to high switching frequencies for power converters. This issue persists when using a prediction horizon with dual step. To overcome this issue, a pso optimized penalty term is incorporated into the cost function to minimize the overall switching frequency. The obtained results demonstrates a significant reduction in the switching frequency of DFIG converters without compromising control. The average switching frequencies for RSC and GSC are decreased by 1,67 kHz and 14,98kHz, respectively, in comparison to conventional MPC. While the THD level of the grid current is evaluated at 3.98%, which is fully coplying with IEEE 519 Std.

关键词： FCS-MPC DFIG Wind Turbine Switching Frequency pso algorithm

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Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and pso algorithm

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2022年 9卷 817723页

作者： Liu, Ying Jiang, Du Yun, Juntong Sun, Ying Li, Cuiqiao Jiang, Guozhang Kong, Jianyi Tao, Bo Fang, Zifan Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Res Ctr Biomimet Robot & Intelligent Measurement Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmission & Mfg Engn Wuhan Peoples R China Wuhan Univ Sci & Technol Precis Mfg Res Inst Wuhan Peoples R China Three Gorges Univ Hubei Key Lab HydroElect Machinery Design & Maint Yichang Peoples R China

With the manipulator performs fixed-point tasks, it becomes adversely affected by external disturbances, parameter variations, and random noise. Therefore, it is essential to improve the robust and accuracy of the controller. In this article, a self-tuning particle swarm optimization (pso) fuzzy PID positioning controller is designed based on fuzzy PID control. The quantization and scaling factors in the fuzzy PID algorithm are optimized by pso in order to achieve high robustness and high accuracy of the manipulator. First of all, a mathematical model of the manipulator is developed, and the manipulator positioning controller is designed. A PD control strategy with compensation for gravity is used for the positioning control system. Then, the PID controller parameters dynamically are minute-tuned by the fuzzy controller 1. Through a closed-loop control loop to adjust the magnitude of the quantization factors-proportionality factors online. Correction values are outputted by the modified fuzzy controller 2. A quantization factor-proportion factor online self-tuning strategy is achieved to find the optimal parameters for the controller. Finally, the control performance of the improved controller is verified by the simulation environment. The results show that the transient response speed, tracking accuracy, and follower characteristics of the system are significantly improved.

关键词： manipulator pso algorithm quantization factor-proportion factor position control fuzzy-PID control

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