期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到1,664篇文章
< 1 2 … 84 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm 认领 引用 被引量：4
1
作者 Danlei Chen Yiqing Luo Xigang Yuan 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2023年第6期244-255,共12页
Cascade refrigeration system(CRS)can meet a wider range of refrigeration temperature requirements and is more energy efficient than single-refrigerant refrigeration system,making it more widely used in low-temperature... Cascade refrigeration system(CRS)can meet a wider range of refrigeration temperature requirements and is more energy efficient than single-refrigerant refrigeration system,making it more widely used in low-temperature industry processes.The synthesis of a CRS with simultaneous consideration of heat integration between refrigerant and process streams is challenging but promising for significant cost saving and reduction of carbon emission.This study presented a stochastic optimization method for the synthesis of CRS.An MINLP model was formulated based on the superstructure developed for the CRS,and an optimization framework was proposed,where simulated annealing algorithm was used to evolve the numbers of pressureemperature levels for all sub-refrigeration systems,and particle swarm optimization algorithm was employed to optimize the continuous variables.The effectiveness of the proposed methodology was verified by a case study of CRS optimization in an ethylene plant with 21.89%the total annual cost saving. 展开更多
关键词 Optimal design Process systems Particle swarm optimization Simulated annealing Mathematical modeling
暂未订购 下载PDF
Dependent task assignment algorithm based on particle swarm optimization and simulated annealing in ad-hoc mobile cloud 认领 引用 被引量：3
2
作者 Huang Bonan Xia Weiwei +4 位作者 Zhang Yueyue Zhang Jing Zou Qian Yan Feng Shen Lianfeng 《Journal of Southeast University(English Edition)》 EI CAS 2018年第4期430-438,共9页
In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on pa... In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution. 展开更多
关键词 ad-hoc mobile cloud task assignment algorithm directed acyclic graph particle swarm optimization simulated annealing
暂未订购 下载PDF
Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm 认领 引用 被引量：4
3
作者 顾文斌 唐敦兵 郑堃 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第5期559-567,共9页
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ... An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms. 展开更多
关键词 job-shop scheduling problem(JSP) hormone modulation mechanism improved adaptive particle swarm optimization(IAPSO) algorithm minimum makespan
暂未订购 下载PDF
Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm 认领 引用 被引量：1
4
作者 Huanan Yu Hangyu Li +1 位作者 He Wang Shiqiang Li 《Energy Engineering》 EI 2024年第6期1535-1555,共21页
The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optim... The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach. 展开更多
关键词 Optimal allocation improved particle swarm algorithm fault location compressed sensing DC distribution network
暂未订购 下载PDF
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
5
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
暂未订购 下载PDF
Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization 认领 引用 被引量：4
6
作者 Jiulong Sun Yanbo Che +2 位作者 Ting Yang Jian Zhang Yibin Cai 《Energy Engineering》 EI 2023年第2期367-384,共18页
As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of ... As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence. 展开更多
关键词 Electric vehicle charging station location selection and capacity configuration loss of distribution system simulated annealing immune particle swarm optimization Voronoi diagram
暂未订购 下载PDF
Angular insensitive nonreciprocal ultrawide band absorption in plasma-embedded photonic crystals designed with improved particle swarm optimization algorithm 认领 引用
7
作者 Yi-Han Wang Hai-Feng Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第4期352-363,共12页
Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded p... Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized scale.The effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these parameters.Compared with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute bandwidth.Under further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and discussed.IPSO is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed structure.The impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are investigated.Additionally,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm. 展开更多
关键词 magnetized plasma photonic crystals improved particle swarm optimization algorithm nonreciprocal ultra-wide band absorption angular insensitivity
暂未订购 下载PDF
Improved Gain Shared Knowledge Optimizer Based Reactive Power Optimization for Various Renewable Penetrated Power Grids with Static Var Generator Participation 认领 引用
8
作者 Xuan Ruan HanYan +4 位作者 DonglinHu Min Zhang YingLi DiHai Bo Yang 《Energy Engineering》 EI 2026年第3期23-56,共34页
An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale... An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10−4 p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach. 展开更多
关键词 Gained-sharing knowledge improved algorithm adaptive parameter adjustment simulated annealing local search algorithms diversity enhancement mechanisms wind and solar new energy static var generator reactive power optimization
暂未订购 下载PDF
Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power 认领 引用 被引量：6
9
作者 WANG Bing ZHANG Pengfei +2 位作者 HE Yufeng WANG Xiaozhi ZHANG Xianxia 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2022年第5期1143-1150,共8页
An economic dispatch problem for power system with wind power is discussed.Using discrete scenario to describe uncertain wind powers,a threshold is given to identify bad scenario set.The bad-scenario-set robust econom... An economic dispatch problem for power system with wind power is discussed.Using discrete scenario to describe uncertain wind powers,a threshold is given to identify bad scenario set.The bad-scenario-set robust economic dispatch model is established to minimize the total penalties on bad scenarios.A specialized hybrid particle swarm optimization(PSO)algorithm is developed through hybridizing simulated annealing(SA)operators.The SA operators are performed according to a scenario-oriented adaptive search rule in a neighborhood which is constructed based on the unit commitment constraints.Finally,an experiment is conducted.The computational results show that the developed algorithm outperforms the existing algorithms. 展开更多
关键词 wind power robust economic dispatch scenario simulated annealing(SA) particle swarm optimization(PSO)
暂未订购 下载PDF
A new support vector machine optimized by improved particle swarm optimization and its application 认领 引用 被引量：5
10
作者 李翔 杨尚东 乞建勋 《Journal of Central South University of Technology》 2006年第5期568-572,共5页
A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, ... A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM. 展开更多
关键词 support vector machine particle swarm optimization algorithm short-term load forecasting simulated annealing
暂未订购 下载PDF
Structural optimization of Au–Pd bimetallic nanoparticles with improved particle swarm optimization method 认领 引用 被引量：1
11
作者 邵桂芳 朱梦 +4 位作者 上官亚力 李文然 张灿 王玮玮 李玲 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第6期131-139,共9页
Due to the dependence of the chemical and physical properties of the bimetallic nanoparticles（NPs） on their structures,a fundamental understanding of their structural characteristics is crucial for their syntheses a... Due to the dependence of the chemical and physical properties of the bimetallic nanoparticles（NPs） on their structures,a fundamental understanding of their structural characteristics is crucial for their syntheses and wide applications. In this article, a systematical atomic-level investigation of Au–Pd bimetallic NPs is conducted by using the improved particle swarm optimization（IPSO） with quantum correction Sutton–Chen potentials（Q-SC） at different Au/Pd ratios and different sizes. In the IPSO, the simulated annealing is introduced into the classical particle swarm optimization（PSO） to improve the effectiveness and reliability. In addition, the influences of initial structure, particle size and composition on structural stability and structural features are also studied. The simulation results reveal that the initial structures have little effects on the stable structures, but influence the converging rate greatly, and the convergence rate of the mixing initial structure is clearly faster than those of the core-shell and phase structures. We find that the Au–Pd NPs prefer the structures with Au-rich in the outer layers while Pd-rich in the inner ones. Especially, when the Au/Pd ratio is 6：4, the structure of the nanoparticle（NP） presents a standardized Pd（core） Au（shell） structure. 展开更多
关键词 bimetallic nanoparticles stable structures particle swarm optimization （PSO） simulated annealing
暂未订购 下载PDF
Improved Particle Swarm Optimization Algorithm to Solve the Problem of Layout Optimization of Electric Vehicle Charging Stations 认领 引用
12
作者 QI Lin YAO Jian WANG Xin-yue 《Journal of Highway and Transportation Research and Development(English Edition)》 2018年第2期96-103,共8页
In charging station services,user charging requirements limit the layout optimization of charging stations to realize total cost minimization.By combining the k-center algorithm and cloud model particle swarm algorith... In charging station services,user charging requirements limit the layout optimization of charging stations to realize total cost minimization.By combining the k-center algorithm and cloud model particle swarm algorithm,this study puts forward a method to improve the global search ability of the adaptive parameter adjustment strategy.A simulation is performed accordingly.Results show that when solving the layout optimization problem for charging stations,the improved adaptive hybrid algorithm that combines the k-center and cloud model particle swarm algorithm outperforms the original cloud model particle swarm algorithm and the basic particle swarm optimization algorithm.The improved algorithm is thus effective. 展开更多
关键词 Traffic engineering layout optimization of electric vehicle charging station improved particle swarm optimization algorithm particle swarm optimization algorithm cloud model Voronoi diagram
暂未订购 下载PDF
Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem 认领 引用 被引量：28
13
作者 CHEN Ai-ling YANG Gen-ke WU Zhi-ming 《Journal of Zhejiang University-SCIENCE A》 EI CAS 2006年第4期607-614,共8页
Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational comp... Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid ap- proximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimiza- tion (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems. 展开更多
关键词 Capacitated routing problem Discrete particle swarm optimization （DPSO） Simulated annealing （SA）
暂未订购 下载PDF
Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation 认领 引用
14
作者 Yan Wu Genqin Sun +4 位作者 Keming Su Liang Liu Huaijin Zhang Bingsheng Chen Mengshan Li 《Journal of Computer and Communications》 2017年第13期9-20,共12页
In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based o... In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems. 展开更多
关键词 Improved Particle Swarm Optimization Algorithm Double Populations Multi-Objective Adaptive Strategy Chaotic Sequence
暂未订购 下载PDF
APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM 认领 引用 被引量：6
15
作者 XiaWeijun WuZhiming ZhangWei YangGenke 《Chinese Journal of Mechanical Engineering》 EI CAS 2004年第3期437-441,共5页
A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a ... A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem. 展开更多
关键词 Job-shop scheduling problem Particle swarm optimization Simulated annealingHybrid optimization algorithm
暂未订购 下载PDF
Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm 认领 引用
16
作者 Xiaokan Wang Qiong Wang 《Journal on Internet of Things》 2021年第1期1-9,共9页
A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of op... A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect. 展开更多
关键词 Multi-objective improved genetic algorithm urban rail train train operation simulation multi particle optimization model
暂未订购 下载PDF
Error modeling and flexure-based calibration of large-aperture optical adjustment mechanisms using an improved particle swarm optimization algorithm 认领 引用
17
作者 Kaiqi Zhang Quantang Fan +4 位作者 Zhigang Liu Pengqian Yang Ze Zhang Siyu Xu Jianqiang Zhu 《High Power Laser Science and Engineering》 SCIE EI CAS CSCD 2025年第6期256-270,共15页
Meter-scale large-aperture gratings are essential in petawatt-class picosecond laser systems.Their grating mounts must support heavy-load arrays and high alignment accuracy due to high energy density and long beam pat... Meter-scale large-aperture gratings are essential in petawatt-class picosecond laser systems.Their grating mounts must support heavy-load arrays and high alignment accuracy due to high energy density and long beam paths.However,nonlinear errors from parasitic motions and transmission gaps can significantly degrade precision.This study presents a kinetostatic modeling and error calibration framework for the grating mount,incorporating an improved particle swarm optimization(PSO) algorithm.The nonlinear error model combines energy-based and pseudo-rigid-body methods,with equivalent representations of structural gaps and parasitic motions.To capture multi-source nonlinear interactions,a global-dynamic multi-subgroup PSO enhances calibration via coordinated global exploration and local refinement.Experiments indicate that,compared with conventional models,first-round compensation reduces average errors by over65.4%,79.8% and 74.8% in rotation,tip and tilt,respectively.The method integrates nonlinear pose modeling,unified gap representation and an enhanced PSO strategy,offering an effective solution for error compensation in meter-scale,heavy-load compliant mechanisms. 展开更多
关键词 identification algorithm improved particle swarm optimization inertial confinement fusion kinematic calibration parallel compliant mechanisms
暂未订购 下载PDF
An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering 认领 引用 被引量：11
18
作者 Taher NIKNAM Babak AMIRI +1 位作者 Javad OLAMAEI Ali AREFI 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第4期512-519,共8页
The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper prop... The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley's Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms. 展开更多
关键词 Simulated annealing （SA） Data clustering Hybrid evolutionary optimization algorithm K-means clustering Parti-cle swarm optimization （PSO）
暂未订购 下载PDF
Improved algorithms to plan missions for agile earth observation satellites 认领 引用 被引量：3
19
作者 Huicheng Hao Wei Jiang Yijun Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第5期811-821,共11页
This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell... This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective. 展开更多
关键词 mission planning immune clone algorithm hybrid genetic algorithm （EA） improved ant colony algorithm general particle swarm optimization （PSO） agile earth observation satellite （AEOS）.
暂未订购 下载PDF
Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm 认领 引用 被引量：24
20
作者 Leijiao Ge Yuanliang Li +2 位作者 Jun Yan Yuqian Wang Na Zhang 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2021年第6期1490-1499,共10页
To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)mo... To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN. 展开更多
关键词 Integrated energy system(IES) load prediction chaos optimization algorithm(COA) improved particle swarm optimization(IPSO) Pearson correlation coefficient wavelet neural network(WNN)
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 84 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈