期刊文献+
共找到5,584篇文章
< 1 2 250 >
每页显示 20 50 100
Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor 被引量:16
1
作者 BOUKHALFA Ghoulemallah BELKACEM Sebti +1 位作者 CHIKHI Abdesselem BENAGGOUNE Said 《Journal of Central South University》 SCIE EI CAS CSCD 2019年第7期1886-1896,共11页
This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different he... This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance. 展开更多
关键词 dual star induction motor drive direct torque control particle swarm optimization (PSO) fuzzy logic control genetic algorithms
在线阅读 下载PDF
An estimation method for direct maintenance cost of aircraft components based on particle swarm optimization with immunity algorithm 被引量:3
2
作者 吴静敏 左洪福 陈勇 《Journal of Central South University》 SCIE EI CAS 2005年第S2期95-101,共7页
A particle swarm optimization (PSO) algorithm improved by immunity algorithm (IA) was presented. Memory and self-regulation mechanisms of IA were used to avoid PSO plunging into local optima. Vaccination and immune se... A particle swarm optimization (PSO) algorithm improved by immunity algorithm (IA) was presented. Memory and self-regulation mechanisms of IA were used to avoid PSO plunging into local optima. Vaccination and immune selection mechanisms were used to prevent the undulate phenomenon during the evolutionary process. The algorithm was introduced through an application in the direct maintenance cost (DMC) estimation of aircraft components. Experiments results show that the algorithm can compute simply and run quickly. It resolves the combinatorial optimization problem of component DMC estimation with simple and available parameters. And it has higher accuracy than individual methods, such as PLS, BP and v-SVM, and also has better performance than other combined methods, such as basic PSO and BP neural network. 展开更多
关键词 aircraft design maintenance COST particle swarm optimization IMMUNITY algorithm PREDICT
在线阅读 下载PDF
A composite particle swarm algorithm for global optimization of multimodal functions 被引量:7
3
作者 谭冠政 鲍琨 Richard Maina Rimiru 《Journal of Central South University》 SCIE EI CAS 2014年第5期1871-1880,共10页
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 qual... 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. 展开更多
关键词 particle swarm algorithm global numerical optimization novel learning strategy assisted search mechanism feedbackprobability regulation
在线阅读 下载PDF
A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems 被引量:4
4
作者 武善玉 张平 +2 位作者 李方 古锋 潘毅 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第2期421-429,共9页
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 establis... 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. 展开更多
关键词 service-oriented architecture (SOA) cyber physical systems (CPS) multi-task scheduling service allocation multi-objective optimization particle swarm algorithm
在线阅读 下载PDF
An extended particle swarm optimization algorithm based on coarse-grained and fine-grained criteria and its application 被引量:2
5
作者 李星梅 张立辉 +1 位作者 乞建勋 张素芳 《Journal of Central South University of Technology》 EI 2008年第1期141-146,共6页
In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using... In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO. 展开更多
关键词 particle swarm extended particle swarm optimization algorithm resource leveling
在线阅读 下载PDF
Improved wavelet neural network combined with particle swarm optimization algorithm and its application 被引量:1
6
作者 李翔 杨尚东 +1 位作者 乞建勋 杨淑霞 《Journal of Central South University of Technology》 2006年第3期256-259,共4页
An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learnin... An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function. 展开更多
关键词 artificial neural network particle swarm optimization algorithm short-term load forecasting WAVELET curse of dimensionality
在线阅读 下载PDF
Bacterial graphical user interface oriented by particle swarm optimization strategy for optimization of multiple type DFACTS for power quality enhancement in distribution system 被引量:3
7
作者 M.Mohammadi M.Montazeri S.Abasi 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第3期569-588,共20页
This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution syste... This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution systems.The proposed GUI based toolbox,allows the user to choose between single and multiple DFACTS allocations,followed by the type and number of them to be allocated.The EBFO is then applied to obtain optimal locations and ratings of the single and multiple DFACTS.This is found to be faster and provides more accurate results compared to the usual PSO and BFO.Results obtained with MATLAB/Simulink simulations are compared with PSO,BFO and enhanced BFO.It reveals that enhanced BFO shows quick convergence to reach the desired solution there by yielding superior solution quality.Simulation results concluded that the EBFO based multiple DFACTS allocation using DSSSC,APC and DSTATCOM is preferable to reduce power losses,improve load balancing and enhance voltage deviation index to 70%,38% and 132% respectively and also it can improve loading factor without additional power loss. 展开更多
关键词 distribution system power quality single type and multiple type DFACTS BFO algorithm particle swarm optimization(PSO)
在线阅读 下载PDF
A new support vector machine optimized by improved particle swarm optimization and its application 被引量:3
8
作者 李翔 杨尚东 乞建勋 《Journal of Central South University of Technology》 EI 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
在线阅读 下载PDF
Momentum particle swarm optimizer
9
作者 Liu Yu Qin Zheng +1 位作者 Wang Xianghua He Xingshi 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2005年第4期941-946,共6页
The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the orig... The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the original particle swarm optimizer to resolve this problem. Furthermore, in order to accelerate convergence, a new strategy about updating velocities is given. The resulting approach is mromentum-PSO which guarantees that particles are never beyond predefined search space without checking boundary in every iteration. In addition, linearly decreasing wight PSO (LDW-PSO) equipped with a boundary checking strategy is also discussed, which is denoted as LDWBC-PSO. LDW-PSO, LDWBC-PSO and momentum-PSO are compared in optimization on five test functions. The experimental results show that in some special cases LDW-PSO finds invalid solutions and LDWBC-PSO has poor performance, while momentum-PSO not only exhibits good performance but also reduces computational cost for updating velocities. 展开更多
关键词 evolutionary computation particle swarm optimization optimization algorithm.
在线阅读 下载PDF
Optimal Planning of Charging Station for Electric Vehicle Based on Quantum PSO Algorithm 被引量:9
10
作者 LIU Zifa ZHANG Wei WANG Zeli 《中国电机工程学报》 EI CSCD 北大核心 2012年第22期I0006-I0006,共1页
关键词 电动汽车 粒子群算法 充电站 规划 优化 量子 能源 EV
在线阅读 下载PDF
Hybrid anti-prematuration optimization algorithm
11
作者 Qiaoling Wang Xiaozhi Gao +1 位作者 Changhong Wang Furong Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第3期503-508,共6页
Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artifici... Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system(AIS) and particle swarm optimization(PSO),together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem. 展开更多
关键词 hybrid optimization algorithm artificial immune system(AIS) particle swarm optimization(PSO) clonal selection anti-prematuration.
在线阅读 下载PDF
基于LMD-QPSO-LSTM的离散再制造系统动态瓶颈预测方法
12
作者 汪家炜 王艳 +1 位作者 纪志成 刘相 《现代制造工程》 北大核心 2025年第6期150-160,57,共12页
离散再制造业普遍存在影响生产效率的瓶颈问题,传统的静态瓶颈识别方法难以有效解决复杂再制造环境中的动态瓶颈漂移问题。针对这一现象,提出了一种基于局部均值分解(Local Mean Decomposition, LMD)方法结合长短期记忆(Long Short-Term... 离散再制造业普遍存在影响生产效率的瓶颈问题,传统的静态瓶颈识别方法难以有效解决复杂再制造环境中的动态瓶颈漂移问题。针对这一现象,提出了一种基于局部均值分解(Local Mean Decomposition, LMD)方法结合长短期记忆(Long Short-Term Memory, LSTM)网络并利用改进量子粒子群(Quantum Particle Swarm Optimization, QPSO)算法优化的LMD-QPSO-LSTM动态瓶颈预测模型。首先,采用机器能耗属性定义动态瓶颈指数,并基于LMD方法分解瓶颈序列以降低数据的波动性。其次,引入注意力机制(Attention Mechanism, AM)来增强LSTM网络的学习能力,同时采用改进的QPSO算法优化LSTM网络选取最优参数。最后,对瓶颈指数的分量进行预测,并将预测结果重构。仿真实验结果表明,基于LMD-QPSO-LSTM的动态瓶颈预测方法可以有效提高预测精度,且能够准确地跟踪瓶颈位置的变化。与其他模型相比,所提方法至少将平均绝对误差(Mean Absolute Error, MAE)降低了52.63%,平均百分比误差(Mean Absolute Percentage Error, MAPE)降低了25.14%,均方根误差(Root Mean Square Error, RMSE)降低了45.78%。 展开更多
关键词 局部均值分解 长短期记忆网络 改进量子粒子群算法 动态瓶颈预测 瓶颈漂移
在线阅读 下载PDF
融合多维时间序列分析的IQPSO-GRU综采工作面瓦斯浓度软测量模型
13
作者 付华 刘雨竹 +1 位作者 徐楠 张俊男 《传感技术学报》 北大核心 2025年第5期877-885,共9页
为快速、准确预测煤矿综采工作面瓦斯浓度,建立基于多维时间序列(MTS)分析的改进量子粒子群算法(IQPSO)优化门控循环单元网络(GRU)的综采工作面瓦斯浓度软测量模型。首先利用拉索(Lasso)算法对煤矿监测系统采集到的MTS数据进行特征提取... 为快速、准确预测煤矿综采工作面瓦斯浓度,建立基于多维时间序列(MTS)分析的改进量子粒子群算法(IQPSO)优化门控循环单元网络(GRU)的综采工作面瓦斯浓度软测量模型。首先利用拉索(Lasso)算法对煤矿监测系统采集到的MTS数据进行特征提取,得出影响瓦斯浓度演化趋势的主要因素作为模型输入量;其次在GRU网络单元中引入ReLU激活函数及Dropout丢弃层解决模型训练梯度问题并增强其泛化能力;提出IQPSO优化GRU网络超参数,提升模型预测准确率及运算效率,得到参数最优的瓦斯浓度软测量模型;最后将所提出的软测量模型与GRU、LSTM、决策树模型进行对比分析,结果表明:所建模型的相对均方根误差值可缩小至0.1021,平均绝对百分比误差可缩小至0.892,而拟合优度决定系数可优化至0.9881,说明其预测结果拟合度更佳、测量性能更优。 展开更多
关键词 瓦斯浓度 软测量 Lasso特征提取 改进的量子粒子群算法 门控循环单元网络
在线阅读 下载PDF
Multi-platform collaborative MRC-PSO algorithm for anti-ship missile path planning
14
作者 LIU Gang GUO Xinyuan +2 位作者 HUANG Dong CHEN Kezhong LI Wu 《Journal of Systems Engineering and Electronics》 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
在线阅读 下载PDF
钻孔瞬变电磁法扫描探测RCQPSO-LMO组合算法2.5D反演 被引量:5
15
作者 程久龙 焦俊俊 +1 位作者 陈志 董毅 《地球物理学报》 SCIE EI CAS CSCD 北大核心 2024年第2期781-792,共12页
利用钻孔进行超前探测地质构造及含水体是地下开挖工程中的常规手段,如何利用这些钻孔进行钻孔瞬变电磁法扫描探测,从而实现钻孔孔壁外围地质异常体的精细探测,对实现地下工程地质透明化具有重要的指导意义.本文提出钻孔瞬变电磁法扫描... 利用钻孔进行超前探测地质构造及含水体是地下开挖工程中的常规手段,如何利用这些钻孔进行钻孔瞬变电磁法扫描探测,从而实现钻孔孔壁外围地质异常体的精细探测,对实现地下工程地质透明化具有重要的指导意义.本文提出钻孔瞬变电磁法扫描探测2.5D反演的数据解译方法,首先针对随机性反演算法时效性低,易陷入局部最优解,而确定性反演算法依赖初始模型的问题,提出了组合策略的量子粒子群优化算法用来随机搜索最优初始模型.在此基础上,利用Levenberg-Marquarat方法求解Occam反演的目标函数,形成了RCQPSO-LMO组合算法进行2.5D反演,通过对比组合算法和单一算法,验证了组合算法具有更精确的反演结果.其次结合屏蔽条件下扫描探测,对比分析了有无屏蔽的2.5D反演结果,通过设定屏蔽系数对非探测方向信号进行部分压制,可以较好地解决钻孔径向扫描探测中对非探测方向信号部分屏蔽下的反演及成像.最后建立三组理论模型进行组合算法2.5D反演,结果表明:组合算法反演结果与理论模型的一致性较好,对低阻异常体的反演精度较高,验证了组合算法对钻孔孔壁外围低阻异常体具有较高的反演精度和分辨能力. 展开更多
关键词 钻孔瞬变电磁法 扫描探测 量子粒子群优化算法 组合算法 2.5D反演
在线阅读 下载PDF
基于IQPSO-EKF的多传感器融合姿态测量方法研究 被引量:1
16
作者 胡启国 王磊 +1 位作者 马鉴望 任渝荣 《机电工程》 CAS 北大核心 2024年第2期353-363,共11页
为解决自动化竖井掘进设备的定位调姿精度对竖井、孔桩挖掘效率与质量的影响,提出了一种基于改进量子粒子群(IQPSO)-扩展卡尔曼滤波(EKF)的姿态测量算法,以提高微机电系统(MEMS)传感器测量精度。首先,对MEMS传感器数据进行了预处理(除... 为解决自动化竖井掘进设备的定位调姿精度对竖井、孔桩挖掘效率与质量的影响,提出了一种基于改进量子粒子群(IQPSO)-扩展卡尔曼滤波(EKF)的姿态测量算法,以提高微机电系统(MEMS)传感器测量精度。首先,对MEMS传感器数据进行了预处理(除噪、滤波、校准等);然后,参考现有飞行器的坐标系,建立了姿态解算模型,通过姿态角数学模型及运动学分析,构建了EFK状态方程,针对EKF方法参数估计不准确的问题,以分段混沌映射优化初始种群,引入平均位置最优值来避免陷入局部最优的IQPSO-EFK算法,优化EKF的系统、测量噪声的协方差参数;最后,对改进算法和三组姿态误差估计进行了对比实验。研究结果表明:对比三种典型目标函数,IQPSO-EFK相较于普通粒子群算法(QPSO-EFK)具有更强的寻优能力与收敛精度;对比三组旋转速度姿态测量误差,基于IQPSO-EKF算法的姿态测量方法在测量误差时比真实测量误差减少了约86.3%,比扩展卡尔曼滤波减少了约68.7%,比普通粒子群算法减少了约28.2%,证明该算法有效地提高了MEMS传感器测量精度。 展开更多
关键词 竖井掘进 角度测量仪器 姿态测量 微机电系统传感器 多传感器融合 改进量子粒子群-扩展卡尔曼滤波
在线阅读 下载PDF
QPSO-based algorithm of CSO joint infrared super-resolution and trajectory estimation 被引量:5
17
作者 Liangkui Lin Hui Xu +2 位作者 Dan Xu Wei An Kai Xie 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第3期405-411,共7页
The midcourse ballistic closely spaced objects(CSO) create blur pixel-cluster on the space-based infrared focal plane,making the super-resolution of CSO quite necessary.A novel algorithm of CSO joint super-resolutio... The midcourse ballistic closely spaced objects(CSO) create blur pixel-cluster on the space-based infrared focal plane,making the super-resolution of CSO quite necessary.A novel algorithm of CSO joint super-resolution and trajectory estimation is presented.The algorithm combines the focal plane CSO dynamics and radiation models,proposes a novel least square objective function from the space and time information,where CSO radiant intensity is excluded and initial dynamics(position and velocity) are chosen as the model parameters.Subsequently,the quantum-behaved particle swarm optimization(QPSO) is adopted to optimize the objective function to estimate model parameters,and then CSO focal plane trajectories and radiant intensities are computed.Meanwhile,the estimated CSO focal plane trajectories from multiple space-based infrared focal planes are associated and filtered to estimate the CSO stereo ballistic trajectories.Finally,the performance(CSO estimation precision of the focal plane coordinates,radiant intensities,and stereo ballistic trajectories,together with the computation load) of the algorithm is tested,and the results show that the algorithm is effective and feasible. 展开更多
关键词 SUPER-RESOLUTION trajectory estimation closely spaced object(CSO) midcourse ballistic infrared focal plane quantumbehaved particle swarm optimization(qpso).
在线阅读 下载PDF
An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks 被引量:1
18
作者 刘继忠 王保磊 +1 位作者 敖俊宇 Q.M.Jonathan WU 《Journal of Central South University》 SCIE EI CAS 2012年第11期3154-3161,共8页
A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the... A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters. 展开更多
关键词 wireless sensor network deterministic area coverage immune-swarm algorithm particle swarm optimization artificialimmune system
在线阅读 下载PDF
基于QPSO的密闭空间混叠冲击波的分离解算方法
19
作者 张恒冉 李剑 +3 位作者 徐利娜 魏交统 潘晋孝 孔庆珊 《火力与指挥控制》 CSCD 北大核心 2024年第9期32-39,共8页
针对在密闭空间中爆炸产生的冲击波导致混叠冲击波对内壁毁伤较为复杂的问题,提出一种分离混叠冲击波的解算方法。通过正演模拟分析密闭空间内混叠冲击波对内壁面的毁伤,得到壁面上的冲击波是多次混叠的。建立独立冲击波的全波形模型,... 针对在密闭空间中爆炸产生的冲击波导致混叠冲击波对内壁毁伤较为复杂的问题,提出一种分离混叠冲击波的解算方法。通过正演模拟分析密闭空间内混叠冲击波对内壁面的毁伤,得到壁面上的冲击波是多次混叠的。建立独立冲击波的全波形模型,并采用量子粒子群优化(QPSO)算法,对多次混叠冲击波进行分解和拟合。QPSO算法拟合出独立的冲击波,其仿真结果相较于遗传算法,均方根误差从0.2506降至0.1216,平均相对误差从0.1079%降至0.1059%。 展开更多
关键词 群智能优化算法 密闭空间 数值模拟 冲击波超压 qpso
在线阅读 下载PDF
Application of improved PSO to power transmission congestion management optimization model
20
作者 李翔 刘预胜 杨淑霞 《Journal of Central South University》 SCIE EI CAS 2008年第S2期347-351,共5页
The parameters of particles were encoded firstly, then the constraint conditions and fitness degree were processed, and the calculation steps of the improved PSO algorithm were presented. Finally, the issues with the ... The parameters of particles were encoded firstly, then the constraint conditions and fitness degree were processed, and the calculation steps of the improved PSO algorithm were presented. Finally, the issues with the adoption of the improved PSO algorithm were solved and the results were analyzed. The results show that it is beneficial to obtaining the optimal solution by increasing the number of particles but that will also increase the operation time. On the aspects of solving continuous differentiable non-linear optimization model with equality and inequality constraints, the optimization result of PSO algorithm is the same as that of the interior point method. Compared with genetic algorithms (GA), PSO algorithm is more effective in the local optimization, and unlike GA, it will not be early maturity. Meanwhile, PSO algorithm is also more effective in the boundary optimization than genetic algorithm. 展开更多
关键词 CONGESTION management particle swarm optimization (PSO) algorithm double FITNESS DEGREE
在线阅读 下载PDF
上一页 1 2 250 下一页 到第
使用帮助 返回顶部