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On the Effectiveness of Optimisation algorithms for Hydrodynamic Lubrication Problems

LUBRICANTS 2025年第5期13卷 207-207页

作者： Kocman, Frantisek Novotny, Pavel Brno Univ Technol Inst Automot Engn Fac Mech Engn Tech 2896-2 Brno 61669 Czech Republic

In many applications, it is necessary to optimise the performance of hydrodynamic (HD) bearings. Many studies have proposed different strategies, but there remains a lack of conclusive research on the suitability of various optimisation methods. This study evaluates the most commonly used algorithms, including the genetic (GA), particle swarm (PSWM), pattern search (PSCH) and surrogate (SURG) algorithms. The effectiveness of each algorithm in finding the global minimum is analysed, with attention to the parameter settings of each algorithm. The algorithms are assessed on HD journal and thrust bearings, using analytical and numerical solutions for friction moment, bearing load-carrying capacity and outlet lubricant flow rate under multiple operating conditions. The results indicate that the PSCH algorithm was the most efficient in all cases, excelling in both finding the global minimum and speed. While the PSWM algorithm also reliably found the global minimum, it exhibited lower speed in the defined problems. In contrast, genetic algorithms and the surrogate algorithm demonstrated significantly lower efficiency in the tested problems. Although the PSCH algorithm proved to be the most efficient, the PSWM algorithm is recommended as the best default choice due to its ease of use and minimal sensitivity to parameter settings.

关键词： journal bearing thrust bearing hydrodynamic lubrication particle swarm algorithm pattern search surrogate algorithm genetic algorithm

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Shape optimization of the fillet under a bolt's head

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JOURNAL OF STRAIN ANALYSIS FOR ENGINEERING DESIGN 2019年第4期54卷 247-253页

作者： Sorrentino, Andrea Castagnetti, Davide Spaggiari, Andrea Dragoni, Eugenio Univ Modena & Reggio Emilia Dept Sci & Methods Engn Via G Amendola 2 I-42122 Reggio Emilia Italy

The stress concentration in the fillet under a bolt's head is a critical issue for the fatigue life of this component. Remembering the variable notch radius solution observed in many biological structures to lower stress concentrations due to normal loads, this work proposes, examines and optimizes a double circular arc fillet, not re-entering the head. The work implements an ad hoc shape optimization procedure that combines a genetic algorithm (the particle swarm optimization) and a parametric, axisymmetric finite-element model of the bolt: by focusing on an M12 bolt, the analysis focuses on two issues: first, the optimization of the radius of each arc in the fillet;second, the optimization of the bolt's head height and head's diameter in combination with the fillet radiuses. By comparing the proposed solution with the fillet geometries for the bolt's heads from the literature, it appears a noticeable stress reduction, about 14% lower than the standard circular fillet, combined with an easy manufacturing.

关键词： Bolt stress concentrations optimization particle swarm algorithm finite element

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Research on Optimization of Nutritional Meal Based on particle swarm Optimization

International Core Journal of Engineering

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International Core Journal of Engineering 2020年第12期6卷 271-275页

作者： Wei Wang Xiaodong Hu Li Wang

Nutritional catering is of great significance to ensure that customers absorb enough nutrition and satisfy people's nutritional diversity. This article analyzes the basic requirements and rules of nutritional catering, constructs a mathematical model of the catering problem, optimizes the nutritional catering system with the help of particle swarm algorithm, gives the results of nutritional catering, and verifies the feasibility of the system model.

关键词： particle swarm algorithm Mathematical model of nutritional catering Optimal solution

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Wind Power Prediction Based on PSO-SVR and Grey Combination Model

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IEEE ACCESS 2019年 7卷 136254-136267页

作者： Zhang, Yi Sun, Hexu Guo, Yingjun Hebei Univ Technol Sch Artificial Intelligence Tianjin 300130 Peoples R China Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang 050018 Hebei Peoples R China

As a kind of green, clean and renewable energy, wind power generation has been widely utilized in various countries in the world. With the rapid development of wind energy, it is also facing prominent problems. Because wind power generation is intermittent, unstable and stochastic, it has caused serious difficulties for power grid dispatch. At present, the important method to solve this problem is to predict wind speed and wind power. Grey model is suitable for uncertain systems with poor information and needs less operation data, so it can be used for wind speed and wind power prediction. However, the traditional grey system model has the disadvantage of low prediction accuracy. Therefore, firstly the GM (1,1) for wind speed prediction is improved by background value optimization in this paper. In order to comprehensively reveal the inherent uncertainty of wind speed random series, the fractional order grey system models with different orders are constructed. Secondly, in order to overcome the shortcoming of single grey model, each grey model is effectively united, and a combination prediction model based on neural network is presented. The two NWP outputs, i.e. ECMWF and GRAPES-MESO, have been added to the prediction model for reducing the uncertainty. The structure parameters of the neural network are optimized by trial and error. Thirdly, the support vector regression model is established to fit the scatter operation data of wind speed-power, and the parameters of the model are optimized by the particle swarm algorithm. Then the power prediction value is obtained by the fitted wind speed-power relationship and the corresponding result of the grey combination model for wind speed prediction. Finally, wind speed and wind power are predicted based on the actual operation data. In addition, the prediction model based on ARIMA is also constructed as a benchmark model. The results show that the proposed grey combination model improves the prediction accuracy.

关键词： Predictive models Wind power generation Wind speed Data models Computational modeling Support vector machines Prediction algorithms Grey model background value support vector regression fractional order particle swarm algorithm wind speed prediction wind power prediction combination model

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Optimizing Node Localization in Wireless Sensor Networks Based on Received Signal Strength Indicator

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IEEE ACCESS 2019年 7卷 73880-73889页

作者： Wang, Wei Liu, Xuming Li, Maozhen Wang, Zhaoba Wang, Cunhua North Univ China Sch Informat Sci & Engn Taiyuan 030051 Shanxi Peoples R China Brunel Univ London Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England

In order to improve the precision of inside localization and optimize the allocation of node resources in wireless sensor networks (WSNs), an equal-arc trilateral localization algorithm based on received signal strength indicator (RSSI) is proposed from the perspective of increasing measurement precision and bettering beacon nodes layout. The algorithm adopts Kalman filter to filter the data collected from the best communication range, thus the disturbance problem of RSSI value can be tackled easily. By analyzing the changeable relationship between the communication distance and the RSSI, an optimal communication distance can be identified to satisfy the application requirements. In this paper, an equal-arc triangular beacon node layout model is established to ensure that the motion tracks of unknown nodes are always situated within an optimal communication distance to improve the measurement precision. The experimental results show that the proposed work increases the average location accuracy of the equal-arc triangulation layout model by 81%, 54%, and 48%, respectively, compared with the traditional square beacon model, the traditional equilateral triangle beacon model, and the improved equilateral triangle beacon model. In comparison with the traditional triangular layout, the equal-arc layout area coverage is increased by 23%.

关键词： Wireless sensor networks RSSI Kalman filtering equal-arc triangular particle swarm algorithm

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A simulation analysis method based on PSO-RBF model and its application

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 2255-2261页

作者： Xi Fei Sun Youfu Ruan Xuejun Nanjing Forestry Univ Coll Mat Sci & Engn Dept Timber Struct Bldg Nanjing 210037 Jiangsu Peoples R China Coll Nanjing Electromech Vocat & Tech Dept Humanities & Social Sci Nanjing 210000 Jiangsu Peoples R China

In order to improve the effectiveness of thermal and heat insulation property of building wall of light wood structure, a research method of heat insulation property of building wall of light wood structure based on PSO-RBF model is proposed. Firstly, the construction and parameter of the adopted wall in the research of the Thesis is given and the corresponding heat insulation physical model and problem are described;secondly, the thermal and heat insulation property of building wall of light wood structure is researched and analyzed by using the proposed particle swarm optimization BP network algorithm;finally, the effectiveness of the proposed algorithm is verified through simulation experiment.

关键词： particle swarm algorithm RBF neural network Light wood Thermal insulation

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Through-the-wall radar imaging algorithm for moving target under wall parameter uncertainties

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IET IMAGE PROCESSING 2019年第11期13卷 1903-1908页

作者： Sun, Yanpeng Chen, Li Qu, Lele Shenyang Aerosp Univ Sch Elect & Informat Engn Daoyi South St 37 Shenyang 110136 Liaoning Peoples R China

In order to solve the problems of slow imaging speed and poor reconstruction accuracy of wall parameters under the condition of wall parameter fuzziness, an improved limited Broyden-Fletcher-Goldfarb-Shanno-particle swarm optimisation (LBFGS-PSO) algorithm was proposed. The LBFGS-PSO algorithm model solves the problems of slow calculation speed and large errors of the traditional quasi-Newton algorithm and particle swarm algorithm. The algorithm combined with block orthogonal matching pursuit algorithm can not only accurately reconstruct the position of the sidewall, but also can use the multi-path information to accurately reconstruct the moving target and the stationary target. Compared with the traditional BFGS algorithm and PSO algorithm, the proposed algorithm can reduce the calculation time and provide more accurate estimation results. Simulation results and data analysis verify the performance of the proposed algorithm.

关键词： Newton method particle swarm optimisation gradient methods radar imaging iterative methods object detection image motion analysis image reconstruction data analysis through-the-wall radar imaging algorithm traditional BFGS algorithm stationary target block orthogonal matching pursuit algorithm particle swarm algorithm quasiNewton algorithm slow calculation speed LBFGS-PSO algorithm model improved limited Broyden-Fletcher-Goldfarb-Shanno-particle swarm optimisation algorithm wall parameter fuzziness wall parameters poor reconstruction accuracy slow imaging speed wall parameter uncertainties moving target

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Comparative study on different neural networks for network security situation prediction

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SECURITY AND PRIVACY 2021年第1期4卷

作者： Wang, Gang Shaanxi Police Coll 1 Qiyuan 2nd Rd Xian 710021 Shaanxi Peoples R China

This article mainly studied the performance of different neural networks in the processing network security situation prediction (NSSP). Radial basis function (RBF) and back propagation neural network (BPNN) models were optimized by particle swarm optimization (PSO) algorithm and seeker optimization algorithm (SOA), respectively. Then the PSO-RBF model and SOA-BPNN model were obtained, and comparative experiments were carried out on CNCERT/CC data set. The results suggested that the improved models were more accurate in predicting the situation value compared with RBF and BPNNmodels;the PSO-RBF mode had three prediction errors, with 0.05 mean square error (MSE) and 0.05 mean absolute error (MAE), and the SOA-BPNNmodel had six prediction errors, with 0.2 MSE and 0.13 MAE, which showed that the PSO-RBF model had better performance. The experimental results show that the PSO-RBF model has an excellent performance in processing NSSP and can be promoted and applied in practice.

关键词： comparative study network security situation prediction neural network particle swarm algorithm radial basis function

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Optimal Design of Electromagnetic Force of Hidden-Pole Magnetic Suspension Linear Motor

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IEEE ACCESS 2019年 7卷 153675-153682页

作者： Sun, Yunpeng Lan, Yipeng Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China

In order to eliminate the frictional resistance of CNC machine feed platform and improve the machining accuracy and surface roughness, the hidden - pole magnetic suspension linear synchronous motor used to drive the CNC machine tool feed platform was studied. According to the special structure and operation mechanism of the hidden - pole magnetic suspension linear motor, the mathematical model of the electromagnetic force of the motor was established firstly. Then the finite element calculation for the air gap magnetic density, back electromotive force and electromagnetic force of two different structures of the motor of the salient pole and hidden pole was carried out by Finite element software, and the harmonic analysis for air gap magnetic density was done. By comparing the electromagnetic forces of the two motors, it is found that the hidden pole has less fluctuation of the electromagnetic force, so it is more suitable for feed platform as a driving component. Finally, particle swarm optimization is used to optimize the electromagnetic force of the hidden-pole controlled-magnet linear magnetic suspension synchronous motor, which further reduces the fluctuation of the electromagnetic force, and also improves the efficiency of the motor. The results of the experimental prove the effectiveness of the control method.

关键词： Shidden pole linear synchronous motor magnetic suspension platform optimization particle swarm algorithm

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Multi-Controller Resource Management for Software-Defined Wireless Networks

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IEEE COMMUNICATIONS LETTERS 2019年第3期23卷 506-509页

作者： Li, Feixiang Xu, Xiaobin Yao, Haipeng Wang, Jingjing Jiang, Chunxiao Guo, Song Beijing Univ Technol Beijing Adv Innovat Ctr Future Internet Technol Beijing 100124 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Space Ctr Beijing 100084 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Given decoupling the control layer and the infrastructure layer, the software-defined wireless networks (SDWNs) is beneficial in terms of providing both low-latency and low-energy consumption services for mobile users, where multicontroller placement and resource management become a pair of bottlenecks. In this letter, we propose an energy-aware multicontroller placement scheme as well as a latency-aware resource management model for the SDWN. Moreover, the particle swarm optimization is invoked for solving the multi-controller placement problem, and a deep reinforcement learning algorithm-aided resource allocation strategy is conceived. Finally, experimental results show that our proposed schemes are conducive to reducing both the execution time and the energy consumption of each task.

关键词： Multi-controller placement resource management particle swarm algorithm deep reinforcement learning

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