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Optimization of jamming formation of USV offboard active decoy clusters based on an improved pso algorithm

Defence Technology（防务技术） 2024年第2期32卷 529-540页

作者： Zhaodong Wu Yasong Luo Shengliang Hu College of Weapons Engineering PLA Naval University of EngineeringWuhan430033China

Offboard active decoys(OADs)can effectively jam monopulse ***,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and *** improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is *** formation of the cluster determines the effectiveness of ***,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is ***,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-Apso)is ***,the formation optimization problem is solved and analyzed using the 3M-Apso algorithm under specific *** results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-pso *** with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.

关键词： Electronic countermeasure Offboard active decoy USV cluster Jamming formation optimization improved pso algorithm

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An Anomaly Data Mining Method for Mass Sensor Networks Using improved pso algorithm Based on Spark Parallel Framework

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JOURNAL OF GRID COMPUTING 2020年第2期18卷 251-261页

作者： Yuan, Jingzhen Hanshan Normal Univ Sch Phys & Elect Engn Chaozhou 521041 Guangdong Peoples R China

Accurate detection and capture of anomaly data in complex network data stream is an important part of ensuring network security. Traditional methods cannot adapt to the high dynamic changes of abnormal data characteristics in complex network. Thus, the detection accuracy is reduced. In this paper, a k-means parallel clustering algorithm is proposed. It is optimized by particle swarm optimization with dynamic adaptive inertia weight (dspsoK-means). And it is used to mine the anomaly data for mass sensor networks. The inertia weight is dynamically adjusted through the fitness function, so that the dspso algorithm has the adaptive characteristics. Then, the output of the dspso algorithm is taken as the input of the k-means algorithm. Thus, the intelligence and self-adaptability of the k-means algorithm in selecting the initial center point is improved. Finally, with the help of Spark platform, the parallelization of dspsoK-means clustering algorithm in the clustering environment is designed and implemented. It is shown by the experimental results that the traffic among nodes in the execution process can be effectively reduced by the dspsoK-means algorithm. And the accuracy of abnormal data mining in complex network data flow is 5% higher than that of the comparison algorithm on average.

关键词： Spark parallel framework Anomaly data mining Fitness function improved pso algorithm Sensor network Mass data

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Robust optimization for a wing at drag divergence Mach number based on an improved pso algorithm

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AEROSPACE SCIENCE AND TECHNOLOGY 2019年 92卷 653-667页

作者： Tao, Jun Sun, Gang Wang, Xinyu Guo, Liqiang Fudan Univ Dept Aeronaut & Astronaut Shanghai 200433 Peoples R China Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA

In this study, an improved pso (particle swarm optimization) algorithm is proposed and applied to the robust optimization of a wing at drag divergence Mach number. In order to reduce the number of design variables, a six-order CST (class/shape function transformation) method is employed for airfoil parameterization. For the purpose of improving the optimization efficiency, Delaunay graph mapping method is adopted for mesh deformation in each iteration of the airfoil optimization, and NURBS (non-uniform rational B-splines)-FFD (free-form deformation) method is employed for mesh deformation in each iteration of the wing optimization. For improving the standard pso algorithm, CVTs (centroidal Voronoi tessellations) method is introduced to generate original positions of the particles more dispersedly, a second-order oscillating scheme is used and an FDR (fitness distance ratio) item is added for updating velocities and positions of the particles. By virtue of the improved pso algorithm, single point optimization and robust optimization are conducted for both airfoil and wing. The results indicate that, comparing with the single point optimizations, the robust optimizations not only reduce drag coefficients of the airfoil and the wing at cruise Mach numbers, but also attenuate the drag increments as the Mach number increases up to drag divergence Mach numbers. (C) 2019 Elsevier Masson SAS. All rights reserved.

关键词： Robust optimization CST method NURBS-FFD method improved pso algorithm Drag reduction

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Optimized trajectory tracking control of the clearance manipulator based on improved pso algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第12期236卷 6424-6439页

作者： Yang, Lirong Li, Jiajun Zhou, Xiaoyun Jiang, Chuan JiangXi Univ Sci & Technol 86 Hongqi Ave Ganzhou 341000 Peoples R China

Aiming at the problems of low joint trajectory tracking accuracy and poor anti-interference ability caused by parameter uncertainty, nonlinearity, and external interference in the hydraulic servo system of the clearance manipulator, an active disturbance rejection servo control method optimized based on the improved Particle Swarm Optimization algorithm is proposed. First, through the kinematic and dynamic analysis of the clearance manipulator, the general solutions of its forward and inverse kinematics are derived, and the relationship expression between each joint angle and the extension of the piston rod of the driving cylinder is obtained according to the relationship between the joint space and the driving mechanism space;Second, the state space differential equations of the hydraulic system of the working device are derived using mechanistic modeling, and this is used to design an active disturbance rejection servo controller for each joint, and the parameters of the active disturbance rejection controller are optimized using an improved pso algorithm to improve the control performance. Simulation and experimental results show that compared with classical PID control and conventional Active Disturbance Rejection Control, the improved ADRC in this paper has higher trajectory tracking control accuracy and the ability to suppress external disturbances.

关键词： clearance manipulator trajectory tracking ADRC improved pso algorithm

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Research on an improved pso algorithm with dual self-adaptation and dual variation 19

Research on an improved PSO algorithm with dual self-adaptat...

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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Chen, Mingjie Wang, Yuchao Li, Peng Fu, Huixuan Harbin Engn Univ Coll Intelligent Syst & Engn Harbin Heilongjiang Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

Particle swarm optimization (pso) algorithm is a global optimization evolutionary algorithm which simulates the foraging behavior of birds. In view of the problems of premature convergence and prematurity of standard pso algorithm, an improved particle swarm optimization algorithm with dual self-adaptation and dual variation is proposed, to overcome the shortcomings of the existing improved pso algorithm with linear inertia weight and motion direction variation. A nonlinear selfadaptive improvement of inertia weight and a linear self-adaptive improvement of learning factor are presented to improve the scope and precision of the optimal search for the balanced population, to strengthen the global search ability in the initial stage of optimization and to enhance the local search ability in the later stage of optimization. Moreover, in the later period of motion, the variation from the motion directions of some particles and the variation from initial values of a few particles are used to increase the diversity of the population and enlarge the searching range of the particles, so as to prevent the particles from falling into the local optimal solution. The improved pso algorithm with dual self-adaptation and dual variation is applied to function optimization. The simulation results show that the algorithm can effectively improve the convergence rate and precision of the algorithm, and avoid premature convergence.

关键词： Nonlinear self-adaptation of inertia weight and linear self-adaptation of learning factors variation from motion direction and initial value of particles improved pso algorithm function optimization

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Optimal Phasor Measurement Unit Placement by an improved pso algorithm

Optimal Phasor Measurement Unit Placement by an Improved PSO...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Wang, Jian-ming Li Chuandong Zhang, Jian China Elect Power Res Inst Power Syst Dept Beijing Peoples R China

ISBN: (纸本)9781457705472;9781457705465

This paper presents an improved pso algorithm (Ipso) to solve the problem of optimal Phasor Measurement Unit (PMU) placement. The aim of Optimal PMU Placement problem is to guarantee both full observability of the power grid and minimal number of PMU. In the improved pso algorithm, the point of genetic algorithm and simulated annealing process is involved into basic particle swarm optimization. To deal with the constraints, an improved algorithm is developed and it can avoid costing much time and trapping local optimal solution. IEEE systems are tested to show the feasibility and effectiveness of the algorithm.

关键词： improved pso algorithm phasor measurement unit optimal placement

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Optimizing Control of Multiobjective Based on improved pso algorithm for Excavator 8

Optimizing Control of Multiobjective Based on Improved PSO A...

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8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

作者： Bi, Bo Li, Lei Chongqing Jiaotong Univ Int Sch Chongqing Peoples R China Chongqing Univ Coll Automat Chongqing Peoples R China

ISBN: (纸本)9783642549274;9783642549267

Different controller is radically different in control effect. For the same controlled process, aimed at the puzzle of being difficult to select the controller for the incompatibility among control performance index, this paper proposes a sort of improved pso algorithm. Based on the construction of objective function in multi-performance index parameter, the algorithm could quickly search and converge to optimizing control parameter in global optimal extremum, and single out the controller through performance comparison excellently. In the paper, it took the controller selection of excavator system as an example, designed the algorithm of multi-modal HSIC controller for excavator, and the simulation demonstrated that the HSIC-based controller could be stronger in robustness and better in dynamical and steady control quality compared with improved PID controller. The research result shows that it is reasonable and applicable to optimizing selection of controller.

关键词： Excavator Control parameter improved pso algorithm Optimizing selection of controller

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Research on Day-ahead Dispatch of Electricity-heat Integrated Energy System Based on improved pso algorithm

Research on Day-ahead Dispatch of Electricity-heat Integrate...

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International Conference of Recent Trends in Environmental Sustainability and Green Technologies (ICRTEG)

作者： Shuai, Xuanyue Wang, Xiuli Li, Kaiying Wang, Bangyan Yuan, Shengqi Zhang, Liang Sch Xian Jiaotong Univ Xian Peoples R China

With the increasingly serious environmental pollution, it is of great significance to construct a comprehensive day-ahead dispatch model of the integrated energy system. For the regional electricity-heat integrated energy system, firstly analyze the operating characteristics and dispatch costs of all units in the system, and then establish the day-ahead economic dispatch model of the system. Furthermore, an improved pso algorithm is designed based on the idea of adaptive weight and genetic algorithm, and the appropriate algorithm is used to solve the work schedule of each unit in the system through a calculation example. Then the sensitivity analysis of the electric boiler capacity is carried out. Finally, the feasibility of the proposed model is verified through the results analysis, which provides a reference scheme for the electricity-heat integrated energy system including electric vehicles.

关键词： electricity-heat integrated energy system economic dispatch improved pso algorithm electric vehicles

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Research on system of ultra-flat carrying robot based on improved pso algorithm

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FRONTIERS IN NEUROROBOTICS 2023年 17卷 1294606页

作者： Zhu, Jinghao Wu, Jun Chen, Zhongxiang Cao, Libo Yang, Minghai Xu, Wu Hunan Univ State Key Lab Adv Design & Mfg Technol Vehicle Changsha Peoples R China Hunan Normal Univ Coll Engn & Design Changsha Peoples R China Hunan Lizhong Technol Ltd Co Changsha Peoples R China

Ultra-flat carrying robots (UCR) are used to carry soft targets for functional safety road tests of intelligent driving vehicles and should have superior control performance. For the sake of analyzing and upgrading the motion control performance of the ultra-flat carrying robot, this paper develops the mathematical model of its motion control system on the basis of the test data and the system identification method. Aiming at ameliorating the defects of the standard particle swarm optimization (pso) algorithm, namely, low accuracy, being susceptible to being caught in a local optimum, and slow convergence when dealing with the parameter identification problems of complex systems, this paper proposes a refined pso algorithm with inertia weight cosine adjustment and introduction of natural selection principle (IWCNS-pso), and verifies the superiority of the algorithm by test functions. Based on the IWCNS-pso algorithm, the identification of transfer functions in the motion control system of the ultra-flat carrying robot was completed. In comparison with the identification results of the standard pso and linear decreasing inertia weight (LDIW)-pso algorithms, it indicated that the IWCNS-pso has the optimal performance, with the number of iterations it takes to reach convergence being only 95 and the fitness value being only 0.117. The interactive simulation model was constructed in MATLAB/Simulink, and the critical proportioning method and the IWCNS-pso algorithm were employed respectively to complete the tuning and optimization of the Proportional-Integral (PI) controller parameters. The results of simulation indicated that the PI parameters optimized by the IWCNS-pso algorithm reduce the adjustment time to 7.99 s and the overshoot to 13.41% of the system, and the system is significantly improved with regard to the control performance, which basically meets the performance requirements of speed, stability, and accuracy for the control system. In conclusion, the IWCNS-

关键词： ultra-flat carrying robot improved pso algorithm system identification critical proportioning method optimization of PI parameters

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Precision measurement and evaluation of flatness error for the aero-engine rotor connection surface based on convex hull theory and an improved pso algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2020年第8期31卷 085006-085006页

作者： Zhang, Maowei Liu, Yongmeng Sun, Chuanzhi Wang, Xiaoming Tan, Jiubin Harbin Inst Technol Ctr Ultraprecis Optoelect Instrument Engn Harbin 150080 Peoples R China Harbin Inst Technol Minist Ind & Informat Technol Key Lab Ultraprecis Intelligent Instrumentat Harbin 150080 Peoples R China

The precision measurement and evaluation of the geometry form error for high-end precision components is one of the most important tasks for metrologists. In this paper, a novel hybrid flatness error evaluation method is proposed to measure and evaluate the flatness error for large-scale point cloud data. This method is based on the convex hull algorithm and the improved particle swarm optimization (Ipso) algorithm. Not only are a large number of redundant measurement data points effectively removed through the convex hull algorithm, but also this operation does not lose any valuable information for flatness error evaluation. Then an accurate mathematical model for flatness error evaluation is established based on the minimum zone criteria recommended by ISO, and then the Ipso algorithm is adopted to solve this complex non-linear optimization problem. In particular, the non-linear dynamic inertia weights w and learning factors c(1), c(2) are introduced to improve the calculation accuracy. Finally, the proposed method is verified through an experiment measuring and evaluating the flatness error of an aero-engine rotor connection surface. The effective measurement data points are reduced from 18 000 to 856, or 4.76%, which is significant compared to the original data points. The flatness error evaluation result is 369 mu m according to the Ipso method that we propose. Last but not least, we compare our method with the traditional least squares method and unmodified particle swarm optimization algorithm. The calculation results indicate that the evaluation accuracy is increased by 21 mu m and 44 mu m, respectively.

关键词： flatness error precision measurement form error evaluation convex hull improved pso algorithm

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