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

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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Enhanced nanolaser power for metamaterial waveguide using Bragg reflector and pso algorithm

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PHYSICA SCRIPTA 2025年第3期100卷 035532-035532页

作者： Kadhim, Nawres M. Vahed, Hamid Soofi, Hadi Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

This work enhanced the laser output power from a nanolaser device utilizing an InGaAsP waveguide. We added the DBR structure to the meta-waveguide gain strips with 14 waveguides. Then, we used the particle swarm optimization (pso) algorithm to improve the dimensions, especially the meta-WG's width and the DBR structure's x, y, span, and period dimensions. From this new design, we obtained an increase in the output laser power in both the backward and forward directions, respectively, as the value of the increase in the backward direction reached ten orders of magnitude, meaning an output power of 2 mW. In comparison, it increased the laser output power in the forward direction by 2.61 orders of magnitude, indicating an output power value of 0.7 mW. This increase was achieved while maintaining the original device dimensions and operating at room temperature.

关键词： DBR structure FDTD metamaterial waveguide nanolaser pso algorithm SOI

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Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using pso algorithm

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ENERGY 2024年 296卷

作者： Sun, Yue Luo, Zhiwen Li, Yu Zhao, Tianyi Dalian Univ Technol Inst Bldg Energy Dalian Peoples R China Cardiff Univ Welsh Sch Architecture Cardiff Wales Donghua Univ Coll Environm Sci & Engn Shanghai Peoples R China

As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or winter, by flexibly responding to the grid demands. Indoor air temperature set-point resetting is a typical passive demand response (DR) control strategy that have potentials to be widely implemented by inverter air conditioners in residential buildings. However, in the existing small and medium-size public buildings, the compressor motor of HVAC systems mostly does not allow for stepless speed changes. This means that the indoor temperature set-point resetting strategy may results in frequent on/off switching of the chiller plant, leading to a compromised lifespan. Although rooftop photovoltaic systems (PVs) are expected to be widely adopted in new buildings, their potential for peak load shaving or valley load filling has received limited investigation. This study proposes a grey-box model-based demand side management (DSM) method for variable air volume systems (VAVs) and rooftop PVs in public buildings. The DSM method utilizes the particle swarm optimization (pso) algorithm to determine optimal indoor air temperature and/or evaporator outlet water temperature set-point schedules for the VAVs, as well as optimal charging/discharging schedules for the storage battery of rooftop PVs. This approach aims to reduce the peak power demands of the integrated system without causing severe thermal discomfort, increasing chiller mechanical costs (refers to frequent on/off switching of the chiller plant in this paper), or overcharging/over-discharging of storage batteries. The results of case studies demonstrate that the strategy of co-resetting indoor air temperature - evaporator outlet water temperature set-points outperforms the indoor air temperature set-point resetting strategy in terms of both peak power demands reduction and mechanical cost saving when applie

关键词： Grey-box model Demand side management VAV system Rooftop PV system pso algorithm

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Hybrid Improved pso algorithm for Soil Property Parameter Estimation

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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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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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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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Research on path planning of continuous control UAV based on improved pso algorithm 5

Research on path planning of continuous control UAV based on...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Li, Siyang Liu, Congjian Zheng, Xiaojun Bian, Jinxin Dalian Jiaotong Univ Sch Mech Engn Dalian Peoples R China

ISBN: (纸本)9798350386783;9798350386776

The introduction of intelligent search algorithms provides an excellent solution to the global and local path planning problems of UAVs. The most classic and representative is Particle Swarm Optimization (pso). However, when applying pso algorithm to UAV path planning, it is easy to be constrained by factors such as environmental changes and fail to achieve the expected results. In order to solve the communication delay and communication reliability problems in wireless communication, a path planning study under successive control of UAVs is proposed. In this study, the defects of pso algorithm and the problems in UAV path planning are improved. The improved algorithm introduces the adaptive weight into the update mechanism, and combines the Levy flight strategy to improve the global search strategy to reduce the impact of environmental changes on the algorithm update, so as to ensure the optimal solution. It can be seen from the simulation results that the improved pso algorithm has achieved significant improvement in data update accuracy, local and overall search capabilities. Compared with genetic algorithm, grey wolf optimization algorithm and pso algorithm, the improved pso algorithm has obvious advantages such as fast planning speed and short planning path.

关键词： Consecutive Control Unmanned Air Vehicle pso algorithm Levey Flight Strategy Path Optimization

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