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Dynamic Optimization Method on Electromechanical Coupling System by Exponential Inertia Weight particle swarm algorithm

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Chinese Journal of Mechanical Engineering 2009年第4期22卷 602-607页

作者： LI Qiang WU Jianxin SUN Yan College of Mechanical and Electrical Engineering North China University of Technology Beijing 100144 China College of Mechanical Engineering lnner Mongolia University of Technology Hohhot 010051 China

Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm（EPSA）, is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimizati on. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.

关键词： particle swarm algorithm electromechanical coupling system dynamic optimization

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A composite particle swarm algorithm for global optimization of multimodal functions

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Journal of Central South University 2014年第5期21卷 1871-1880页

作者：谭冠政鲍琨 Richard Maina Rimiru School of Information Science and Engineering Central South University

During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.

关键词： particle swarm algorithm global numerical optimization novel learning strategy assisted search mechanism feedbackprobability regulation

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Control strategy of maglev vehicles based on particle swarm algorithm

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Journal of Modern Transportation 2014年第1期22卷 30-36页

作者： Hui Wang Gang Shen Jinsong Zhou Institute of Railway and Urban Mass Transit Tongji University

Taking a single magnet levitation system as theobject, a nonlinear numerical model of the vehicle–guidewaycoupling system was established to study the levitationcontrol strategies. According to the similarity in dynamics,the single magnet-guideway coupling system was simplifiedinto a magnet-suspended track system, and the correspondinghardware-in-loop test rig was set up usingdSPACE. A full-state-feedback controller was developedusing the levitation gap signal and the current signal, andcontroller parameters were optimized by particle swarmalgorithm. The results from the simulation and the test rigshow that, the proposed control method can keep the systemstable by calculating the controller output with the fullstateinformation of the coupling system, Step responsesfrom the test rig show that the controller can stabilize thesystem within 0.15 s with a 2 % overshot, and performswell even in the condition of violent external *** the linear quadratic optimal method, the particleswarm algorithm carries out the optimization with thenonlinear controlled object included, and its optimizedresults make the system responses much better.

关键词： Maglev control Vehicle–guideway couplingvibration particle swarm algorithm Full-state feedback

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A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar

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Frontiers of Information Technology & Electronic Engineering 2017年第11期18卷 1806-1816页

作者： Hao-wei ZHANG Jun-wei XIE Wen-long LU Chuan SHENG Bin-feng ZONG Air and Missile Defense College Air Force Engineering University Xi'an 710051 China Troop 94225 Rizhao 276000 China Troop 94710 Wuxi 214000 China

A hybrid optimization approach combining a particle swarm algorithm, a genetic algorithm, and a heuristic interleaving algorithm is proposed for scheduling tasks in the multifunction phased array radar. By optimizing parameters using chaos theory, designing the dynamic inertia weight for the particle swarm algorithm as well as introducing crossover operation and mutation operation of the genetic algorithm, both the efficiency and exploration ability of the hybrid algorithm are improved. Under the frame of the intelligence algorithm, the heuristic interleaving scheduling algorithm is presented to further use the time resource of the task waiting duration. A large-scale simulation demonstrates that the proposed algorithm is more robust and efficient than existing algorithms.

关键词： Phased array radar Scheduling particle swarm algorithm Genetic algorithm Pulse interleave

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Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

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Journal of Southeast University(English Edition) 2013年第3期29卷 328-335页

作者：赵泽辉康海贵李明伟大连理工大学建设工程学部大连116024

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient *** order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer *** volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

关键词： expressway traffic flow forecasting projectionpursuit regression particle swarm algorithm chaoticmapping cloud model

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Comprehensive Rainstorm Intensity Formula Based on particle swarm algorithm

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Meteorological and Environmental Research 2010年第9期1卷 1-3,14页

作者：赵吉武邹长武卢晓宁成都信息工程学院四川成都610225

[Objective] The research aimed to simplify the traditional method and gain the method which could directly construct the comprehensive rainstorm intensity formula.[Method] The particle swarm optimization was used to optimize the parameters of uniform comprehensive rainstorm intensity formula in every return period and directly construct the comprehensive rainstorm intensity ***,took the comprehensive rainstorm intensity formula which was established by the hourly precipitation data in wuhu City as an example,the calculation result compared with the computed result of traditional method.[Result] The calculation result precision of particle swarm algorithm was higher than the traditional method,and the calculation process was simpler.[Conclusion] The particle swarm algorithm could directly construct the comprehensive rainstorm intensity formula.

关键词： particle swarm algorithm Comprehensive rainstorm intensity formula Optimization China

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Optimization of Operational Route in AS/RS Based on particle swarm algorithm

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Journal of Southwest Jiaotong University(English Edition) 2008年第1期16卷 92-94页

作者：黄学飞刘云霞 Department of Mechanical and Electronic Engineering Panyu Polytechnic School of Economics and Management Zhejiang University of Science and Technology

Optimization of the operational route in the automated storage/retrieval system （AS/RS） is transformed into the traveling salesman problem, To make the moving distance of the storage/retrieval machine shortest, we carry out a group of tests where 20 goods locations are chosed. Using PSO for operational route of AS/RS, the operation time can be shortened by about 11%. The experiments indicate that under the same conditions, the more the goods locations are, the higher the operation efficiency of the storage/retrieval machine is.

关键词： particle swarm algorithm Traveling salesman problem Automated storage/retrieval system

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Study on Ice Regime Forecast Based on SVR Optimized by particle swarm Optimization algorithm

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Meteorological and Environmental Research 2012年第11期3卷 36-40页

作者： WANG Fu-qiang RONG Fei North China University of Water Resources and Electric Power

[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.

关键词： particle swarm algorithm Support vector machine SVR Ice regime forecast China

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A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

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Journal of Central South University 2016年第2期23卷 421-429页

作者：武善玉张平李方古锋潘毅 School of Computer Science and Engineering South China University of Technology Department of Computer Science College of Staten Island Department of Computer Science Georgia State University

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

关键词： service-oriented architecture (SOA) cyber physical systems (CPS) multi-task scheduling service allocation multi-objective optimization particle swarm algorithm

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Medical Image Compression Based on Wavelets with particle swarm Optimization

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Computers, Materials & Continua 2021年第5期67卷 1577-1593页

作者： Monagi H.Alkinani E.A.Zanaty Sherif M.Ibrahim Department of Computer Science and Artificial Intelligence Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabi Department of Computer Science Faculty of Computers and InformationSohag UniversitySohagEgypt Department of Computer Science and Mathematics Faculty of ScienceSouth Valley UniversityQenaEgypt

This paper presents a novel method utilizing wavelets with particle swarm optimization(PSO)for medical image *** method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using *** transfers images into subband details and approximations using a modified Haar wavelet(MHW),and then applies a *** is applied for selecting a particle assigned to the threshold values for the *** positions assigned to particles values are used to represent *** particle updates its position depending on the global best position(gbest)(for all details subband)and local best position(pbest)(for a subband).The fitness value is developed to terminate PSO when the difference between two local best(pbest)successors is smaller than a prescribe *** experiments are applied on five different medical image types,i.e.,MRI,CT,and *** show that the proposed algorithm can be more preferably to compress medical images than other existing wavelets techniques from peak signal to noise ratio(PSNR)and compression ratio(CR)points of views.

关键词： Image compression wavelets Haar wavelet particle swarm algorithm medical image compression PSNR and CR

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