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Research on Trajectory Tracking Method of Redundant Manipulator Based on PSO Algorithm Optimization 认领 引用 被引量：3
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作者 Shifu Xu Yanan Jiang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第10期401-415,共15页
Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PS... Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PSO algorithm optimization is studied.The kinematic diagram of redundant manipulator is created,to derive the equation of motion trajectory of redundant manipulator end.Pseudo inverse Jacobi matrix is used to solve the problem of manipulator redundancy.Based on the tracking ellipse of redundant manipulator,the tracking shape of redundant manipulator is determined with the overall tracking index as the second index,and the optimization method of tracking index is proposed.The redundant manipulator contour is located by active contour model,on this basis,combined with particle swarm optimization algorithm,the point coordinates on the circumference with the relevant joint point as the center and joint length as the radius are selected as the algorithm particles for iteration,and the optimal tracking results of the overall redundant manipulator trajectory are obtained.The experimental results show that under the proposed method,the tracking error of the redundant manipulator is low,and the error jump range is small.It shows that this method has high tracking accuracy and reliability. 展开更多
关键词 PSO algorithm optimization redundant manipulator trajectory tracking overall tracking index
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Path planning of unmanned surface vehicles based on improved particle swarm optimization algorithm with consideration of particle sight distance 认领 引用
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作者 WANG Cheng YANG Junnan +3 位作者 ZHANG Xinyang QIAN Zhong ZHU Ye LIU Hong 《上海海事大学学报》 北大核心 2026年第1期9-19,共11页
To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc... To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs. 展开更多
关键词 particle swarm optimization algorithm(PSO) sight distance unmanned surface vehicle(USV)
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Multi-platform collaborative MRC-PSO algorithm for anti-ship missile path planning 认领 引用 被引量：1
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作者 LIU Gang GUO Xinyuan +2 位作者 HUANG Dong CHEN Kezhong LI Wu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第2期494-509,共16页
To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO al... To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO algorithm utilizes a semi-rasterization environment modeling technique and inte-grates the geometric gradient law of ASMs which distinguishes itself from other collaborative path planning algorithms by fully considering the coupling between collaborative paths. Then, MRC-PSO algorithm conducts chunked stepwise recursive evo-lution of particles while incorporating circumvent, coordination, and smoothing operators which facilitates local selection opti-mization of paths, gradually reducing algorithmic space, accele-rating convergence, and enhances path cooperativity. Simula-tion experiments comparing the MRC-PSO algorithm with the PSO algorithm, genetic algorithm and operational area cluster real-time restriction (OACRR)-PSO algorithm, which demon-strate that the MRC-PSO algorithm has a faster convergence speed, and the average number of iterations is reduced by approximately 75%. It also proves that it is equally effective in resolving complex scenarios involving multiple obstacles. More-over it effectively addresses the problem of path crossing and can better satisfy the requirements of multi-platform collabora-tive path planning. The experiments are conducted in three col-laborative operation modes, namely, three-to-two, three-to-three, and four-to-two, and the outcomes demonstrate that the algorithm possesses strong universality. 展开更多
关键词 anti-ship missiles multi-platform collaborative path planning particle swarm optimization(PSO)algorithm
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Dynamic Multi-objective Optimization of Chemical Processes Using Modified BareBones MOPSO Algorithm 认领 引用
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作者 杜文莉 王珊珊 +1 位作者 陈旭 钱锋 《Journal of Donghua University(English Edition)》 EI CAS 2014年第2期184-189,共6页
Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is pro... Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems. 展开更多
关键词 dynamic multi-objective optimization bare-bones particle swarm optimization(PSO) algorithm chemical process
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Multi-Source Underwater DOA Estimation Using PSO-BP Neural Network Based on High-Order Cumulant Optimization 认领 引用 被引量：1
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作者 Haihua Chen Jingyao Zhang +3 位作者 Bin Jiang Xuerong Cui Rongrong Zhou Yucheng Zhang 《China Communications》 SCIE CSCD 2023年第12期212-229,共18页
Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be ma... Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm. 展开更多
关键词 gaussian colored noise higher-order cumulant multiple sources particle swarm optimization(PSO)algorithm PSO-BP neural network
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Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm 认领 引用 被引量：1
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作者 姜文英 林焰 +1 位作者 陈明 于雁云 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第5期565-570,共6页
Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the car... Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach. 展开更多
关键词 cargo oil tank optimization design nonlinear programming improved particle swarm optimization（PSO）algorithm fuzzy constraint construction feasibility degree
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Reinforcement Learning-Based Spectral Performance Optimization for UAV-Assisted MIMO Communication System 认领 引用
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作者 Lu Dong Hong-Wei Kong Xin Yuan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第6期1283-1285,共3页
Dear Editor,This letter is concerned with the problem of stable high-quality signal transmission of unmanned aerial vehicle(UAV)-assisted multiple-input multiple-output(MIMO)communication system.The particle swarm opt... Dear Editor,This letter is concerned with the problem of stable high-quality signal transmission of unmanned aerial vehicle(UAV)-assisted multiple-input multiple-output(MIMO)communication system.The particle swarm optimization(PSO)algorithm is used to achieve optimal beamforming and power allocation for this system.Additionally,sensitive particle(SP)and parameter adaptive adjustment are introduced into the traditional PSO algorithm,aiming to improve the performance of the PSO algorithm in dynamic environments with real-time changes in the UAV position.A reinforcement learning(RL)-based approach is proposed to obtain optimal UAV trajectory and adaptive adjustment strategy for PSO parameters,which combine with a specific obstacle avoidance scheme to achieve accurate UAV navigation while satisfying high-quality signal transmission.Simulation experiments show that our scheme provides higher and more stable spectral efficiency as well as more efficient UAV navigation than the currently commonly used scheme with a single RL approach. 展开更多
关键词 parameter adaptive adjustment spectral performance optimization particle swarm optimization pso algorithm UAV assisted MIMO beamforming power allocation particle swarm optimization reinforcement learning
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Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control 认领 引用 被引量：6
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作者 Zhaoyue XU Lin DU +1 位作者 Haopeng WANG Zichen DENG 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2019年第1期111-126,共16页
Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this pa... Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this paper, a particle swarm optimization(PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of applications.All the above verify the immense potential applications of the PSO method in multibody system dynamics. 展开更多
关键词 robotic dynamics multibody system symplectic method particle swarm optimization(PSO)algorithm instantaneous optimal control
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A comparative study of GA,PSO and SCE algorithms for estimating kinetics of biomass pyrolysis 认领 引用
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作者 Hongfang Wang Junhui Gong 《Emergency Management Science and Technology》 2023年第1期136-145,共10页
Optimization performances of three most frequently utilized optimization algorithms,GA(Genetic Algorithm),PSO(Particle Swarm Optimization),and SCE(Shuffled Complex Evolution),are compared to examine their accuracy,com... Optimization performances of three most frequently utilized optimization algorithms,GA(Genetic Algorithm),PSO(Particle Swarm Optimization),and SCE(Shuffled Complex Evolution),are compared to examine their accuracy,computation efficiency,and convergence efficiency.Micro scale TGA(thermogravimetric analysis)experiments of wood were conducted at three heating rates to collect the necessary data for analysis.Gauss multi-peak fitting method was first applied to identify the contribution of each component of wood to the mass loss rate(MLR)curves.Then the Kissinger method and three isoconversional methods,including KAS,Tang,and DAEM methods,were employed to extract kinetics of wood pyrolysis.The average values of the four sets of solutions were adopted to determine the search range in the following optimizations.A thermally thin numerical model was developed to inversely model the collected experimental data combining the three algorithms.The results showed that wood pyrolysis can be described by a four-component parallel reaction scheme.The four sets of kinetic parameters derived using different analytical methods are very close to each other.When extracting kinetics from experimental data using numerical model and optimization algorithms,the accuracies of the three algorithms are ranked as SCE>PSO>GA.While the computation efficiencies and convergency efficiencies are ranked as GA≈PSO>SCE and PSO>SCE>GA,indicating each algorithm has its inherent advantages and limits.In most optimization applications,PSO is more favorable considering its better overall performance. 展开更多
关键词 identify contribution each component genetic algorithm optimization algorithmsga genetic algorithm pso particle swarm optimization optimization algorithms shuffled complex evolution thermogravimetric analysis sce shuffled complex evolution particle swarm optimization
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Springback prediction for incremental sheet forming based on FEM-PSONN technology 认领 引用 被引量：8
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作者 韩飞 莫健华 +3 位作者 祁宏伟 龙睿芬 崔晓辉 李中伟 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2013年第4期1061-1071,共11页
In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath f... In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model. 展开更多
关键词 incremental sheet forming （ISF） springback prediction finite element method （FEM） artificial neural network （ANN） particle swarm optimization （PSO） algorithm
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A Discrete Bat Algorithm for Disassembly Sequence Planning 认领 引用 被引量：7
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作者 JIAO Qinglong XU Da 《Journal of Shanghai Jiaotong university(Science)》 EI 2018年第2期276-285,共10页
Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a produc... Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms. 展开更多
关键词 disassembly sequence planning(DSP) bat algorithm(BA) discrete BA(DBA) fitness function model(FFM) genetic algorithm(GA) particle swarm optimization(PSO) algorithm differential mutation BA(DMBA)
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Neural network hyperparameter optimization based on improved particle swarm optimization 认领 引用
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作者 谢晓燕 HE Wanqi +1 位作者 ZHU Yun YU Jinhao 《High Technology Letters》 EI CAS 2023年第4期427-433,共7页
Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimiza... Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimization(PSO),but its native defect may result in the local optima trapped and convergence difficulty.In this paper,the genetic operations are introduced to the PSO,which makes the best hyperparameter combination scheme for specific network architecture be located easier.Spe-cifically,to prevent the troubles caused by the different data types and value scopes,a mixed coding method is used to ensure the effectiveness of particles.Moreover,the crossover and mutation opera-tions are added to the process of particles updating,to increase the diversity of particles and avoid local optima in searching.Verified with three benchmark datasets,MNIST,Fashion-MNIST,and CIFAR10,it is demonstrated that the proposed scheme can achieve accuracies of 99.58%,93.39%,and 78.96%,respectively,improving the accuracy by about 0.1%,0.5%,and 2%,respectively,compared with that of the PSO. 展开更多
关键词 hyperparameter optimization particle swarm optimization(PSO)algorithm neu-ral network
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Optimal strategy of searching FPD weights scanning matrix using GA-PSO 认领 引用
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作者 严利民 顾裕灿 李建东 《Journal of Shanghai University(English Edition)》 2011年第4期292-296,共5页
This paper discusses a kind of optimal method used for searching flat panel display （FPD） scanning matrix. The method adopts bionic algorithm： genetic algorithm （GA） and particle swarm optimization （PSO） algori... This paper discusses a kind of optimal method used for searching flat panel display （FPD） scanning matrix. The method adopts bionic algorithm： genetic algorithm （GA） and particle swarm optimization （PSO） algorithm. The method using single GA is more time-consuming, and the search efficiency is low in later evolution; the PSO algorithm is easily falling into the local optimal solution and appears the premature convergent phenomenon. Hence, a hybrid approach of GAPSO is found to optimize the search for high grayscale weights scanning matrix. Finally in the acceptable time, it finds a weight scanning matrix （WSM） of 256 gray scales with Matlab, whose scanning efficiency reaches 94.73% and the linearity is very good. 展开更多
关键词 fiat panel display （FPD） weights scanning matrix （WSM） genetic algorithm （GA） particle swarm optimization （PSO） algorithm
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PSO Optimal Control of Model-free Adaptive Control for PVC Polymerization Process 认领 引用 被引量：2
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作者 Shu-Zhi Gao Xiao-Feng Wu +2 位作者 Liang-Liang Luan Jie-Sheng Wang Gui-Cheng Wang 《International Journal of Automation and computing》 CSCD 2018年第4期482-491,共10页
Polyvinyl chloride （PVC） polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete t... Polyvinyl chloride （PVC） polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete time system, a design scheme of model-free adaptive （MFA） controller is given. Then, particle swarm optimization （PSO） algorithm is applied to optimizing and setting the key parameters for controller tuning. After that, the MFA controller is used to control the system of polymerizing temperature. Finally, simulation results are given to show that the MAC strategy based on PSO obtains a good controlling performance index. 展开更多
关键词 Polyvinyl chloride（PVC） polymerization temperature model-free adaptive control particle swarm optimization（PSO）algorithm.
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Large Thinned Array Design Based on Multi-objective Cross Entropy Algorithm 认领 引用
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作者 边莉 边晨源 王书民 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第4期437-442,共6页
To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy(CE) algorithm is proposed by combining fuzzy c-mean clus... To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy(CE) algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flow of the algorithm is given.Using the algorithm, large thinned array(200 elements) given sidelobe level(-10,-19 and-30 d B) problem is solved successfully. Compared with the traditional statistical algorithms, the optimization results of the algorithm validate that the number of feed array elements reduces by 51%, 11% and 6% respectively. In addition, compared with the particle swarm optimization(PSO) algorithm, the number of feed array elements from the algorithm is more similar, but the algorithm is more efficient. 展开更多
关键词 thinned array multi-objective optimization cross entropy(CE) algorithm particle swarm optimization(PSO) algorithm
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Ultra-compact wavelength multiplexer/demultiplexer based on subwavelength grating-assisted multimode interference coupler by inverse design 认领 引用
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作者 WEN Jin PAN Yu +1 位作者 WU Zhengwei ZHANG Ying 《Optoelectronics Letters》 EI 2025年第9期513-519,共7页
We proposed and demonstrated the ultra-compact 1310/1550 nm wavelength multiplexer/demultiplexer assisted by subwavelength grating(SWG)using particle swarm optimization(PSO)algorithm in silicon-on-insulator(SOI)platfo... We proposed and demonstrated the ultra-compact 1310/1550 nm wavelength multiplexer/demultiplexer assisted by subwavelength grating(SWG)using particle swarm optimization(PSO)algorithm in silicon-on-insulator(SOI)platform.Through the self-imaging effect of multimode interference(MMI)coupler,the demultiplexing function for 1310 nm and 1550 nm wavelengths is implemented.After that,three parallel SWG-based slots are inserted into the MMI section so that the effective refractive index of the modes can be engineered and thus the beat length can be adjusted.Importantly,these three SWG slots significantly reduce the length of the device,which is much shorter than the length of traditional MMI-based wavelength demultiplexers.Ultimately,by using the PSO algorithm,the equivalent refractive index and width of the SWG in a certain range are optimized to achieve the best performance of the wavelength demultiplexer.It has been verified that the device footprint is only 2×30.68μm2,and 1 dB bandwidths of larger than 120 nm are acquired at 1310 nm and 1550 nm wavelengths.Meanwhile,the transmitted spectrum shows that the insertion loss(IL)values are below 0.47 dB at both wavelengths when the extinction ratio(ER)values are above 12.65 dB.This inverse design approach has been proved to be efficient in increasing bandwidth and reducing device length. 展开更多
关键词 subwavelength grating swg using effective refractive index mmi section subwavelength grating multimode interference coupler inverse design particle swarm optimization pso algorithm demultiplexing function
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Optimal integration of DGs into radial distribution network in the presence of plug-in electric vehicles to minimize daily active power losses and to improve the voltage profile of the system using bioinspired optimization algorithms 认领 引用 被引量：26
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作者 Satish Kumar Injeti Vinod Kumar Thunuguntla 《Protection and Control of Modern Power Systems》 SCIE EI 2020年第1期21-35,共15页
Purpose:The increase in plug-in electric vehicles(PEVs)is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior.To add... Purpose:The increase in plug-in electric vehicles(PEVs)is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior.To address this problem,the present work mainly focuses on optimal integration of distributed generators(DG)into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily(24 h)power loss and voltage improvement of the system as objectives for better system performance.Design/methodology/approach:To achieve the desired outcomes,an efficient weighted factor multi-objective function is modeled.Particle Swarm Optimization(PSO)and Butterfly Optimization(BO)algorithms are selected and implemented to minimize the objectives of the system.A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system.The simulations are carried out using MATLAB software.Findings:The simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects.Among PSO and BO,BO is comparatively successful in achieving the desired objectives.Originality/value:The main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential,industrial and commercial loads.Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios. 展开更多
关键词 Plug-in electric vehicles(PEVs) Distributed generators(DGs) Repetitive distribution power flow Particle swarm optimization algorithm(PSO) Butterfly optimization(BO) Daily active power loss
Key Technology Innovation Mode of New Energy Industry Ecological Integration System Based on Particle Swarm Optimization Algorithm 认领 引用 被引量：2
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作者 Shunjun Luo Xiaoge Zhu Jiasen Ran 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第6期1752-1762,共11页
The development of society is inseparable from the use of traditional burning energy.However,people’s excessive exploitation of fossil energy has led to the gradual shortage of fossil energy.It is essential to find N... The development of society is inseparable from the use of traditional burning energy.However,people’s excessive exploitation of fossil energy has led to the gradual shortage of fossil energy.It is essential to find New Energy(NE)and develop a new energy industry.The natural ecosystem has the characteristics of stable development.With the development of Artificial Intelligence(AI),the structure of the natural ecosystem has been applied to the NE industry,forming an NE industry ecological integration system.This paper uses Particle Swarm Optimization(PSO)algorithm to optimize the structure and resources of the NE industry,so that the NE industry has the capability of sustainable development.The traditional NE industry and the NE innovation industry ecological integration system based on PSO algorithm are compared.The experimental results show that in the NE vehicle industry,the average economic benefits of the traditional NE industry and the NE innovation industry ecosystem based on PSO algorithm are 63.6%and 77.2%,respectively.In the NE power generation industry,the average economic benefits of the traditional NE industry and the NE innovation industry ecosystem based on PSO algorithm are 67.6%and 80.4%,respectively.Therefore,in the context of AI,the application of PSO algorithm to the ecological integration system of NE industry could improve the economic benefits of NE industry. 展开更多
关键词 New Energy(NN) industrial ecology innovation model Particle Swarm Optimization(PSO)algorithm Artificial Intelligence(Al)
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Near Optimal PID Controllers for the Biped Robot While Walking on Uneven Terrains 认领 引用 被引量：2
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作者 Ravi Kumar Mandava Pandu Ranga Vundavilli 《International Journal of Automation and computing》 CSCD 2018年第6期689-706,共18页
The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the ... The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation （PID） controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization （MCIWO） and particle swarm optimization （PSO） algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains. 展开更多
关键词 Biped robot staircase sloping surface proportion integration differentiation （PID） controller modified chaotic invasive weed optimization （MCIWO） ＆ particle swarm optimization （PSO） algorithm.
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Study on Spot Addressing and Segmentation Method for Microarray Image 认领 引用
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作者 LIU Quan-jin ZHU Xing-yue ZHAO Zhi-min 《Chinese Journal of Biomedical Engineering(English Edition)》 CAS CSCD 2016年第4期139-148,共10页
This paper presents a novel method of spot addressing and segmentation about the foreground segmentation of microarray image. In this paper,a spot addressing method based on particle swarm optimization(PSO),algorithm ... This paper presents a novel method of spot addressing and segmentation about the foreground segmentation of microarray image. In this paper,a spot addressing method based on particle swarm optimization(PSO),algorithm is proposed to have a further search for the center coordinate and radius of the spot whose region is determined by the projection method. Then,a foreground segmentation method is put forward to make the spot foreground segmentation based on the center coordinate and radius of the spot. The spot addressing and segmentation experiments on synthetic and real microarray images show that the proposed method is effective and feasible for the foreground segmentation of microarray image. 展开更多
关键词 microarray image spot addressing foreground segmentation particle swarm optimization(PSO) algorithm
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