期刊文献+
共找到1,193篇文章
< 1 2 60 >
每页显示 20 50 100
Dependent task assignment algorithm based on particle swarm optimization and simulated annealing in ad-hoc mobile cloud 被引量:3
1
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
下载PDF
Hybrid Strategy of Particle Swarm Optimization and Simulated Annealing for Optimizing Orthomorphisms 被引量:2
2
作者 Tong Yan Zhang Huanguo 《China Communications》 SCIE CSCD 2012年第1期49-57,共9页
Orthomorphism on F2^n is a kind of elementary pemmtation with good cryptographic properties. This paper proposes a hybrid strategy of Particle Swarm Optimization (PSO) and Sirrmlated Annealing (SA) for finding ort... Orthomorphism on F2^n is a kind of elementary pemmtation with good cryptographic properties. This paper proposes a hybrid strategy of Particle Swarm Optimization (PSO) and Sirrmlated Annealing (SA) for finding orthomorphisrm with good cryptographic properties. By experiment based on this strategy, we get some orthorrorphisrm on F2^n = 5, 6, 7, 9, 10) with good cryptographic properties in the open document for the first time, and the optirml orthorrrphism on F found in this paper also does better than the one proposed by Feng Dengguo et al. in stream cipher Loiss in difference uniformity, algebraic degree, algebraic irrarnity and corresponding pernmtation polynomial degree. The PSOSA hybrid strategy for optimizing orthomerphism in this paper makes design of orthorrorphisrm with good cryptographic properties automated, efficient and convenient, which proposes a new approach to design orthornorphisrm. 展开更多
关键词 synanetric cryptography orthon-orphism particle swarm optintion simulated annealing
下载PDF
Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm 被引量:1
3
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 pressure/temperature 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
下载PDF
Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization 被引量:2
4
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
下载PDF
Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem 被引量:27
5
作者 CHEN Ai-ling YANG Gen-ke WU Zhi-ming 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 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)
下载PDF
APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM 被引量:5
6
作者 XiaWeijun WuZhiming ZhangWei YangGenke 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 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
下载PDF
A new support vector machine optimized by improved particle swarm optimization and its application 被引量:3
7
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
下载PDF
Structural optimization of Au–Pd bimetallic nanoparticles with improved particle swarm optimization method 被引量:1
8
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
下载PDF
Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power
9
作者 WANG Bing ZHANG Pengfei +2 位作者 HE Yufeng WANG Xiaozhi ZHANG Xianxia 《Journal of Systems Engineering and Electronics》 SCIE EI 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)
下载PDF
基于SA-PSO算法优化CNN的电能质量扰动分类模型 被引量:2
10
作者 肖白 李道明 +2 位作者 穆钢 高文瑞 董光德 《电力自动化设备》 EI CSCD 北大核心 2024年第5期185-190,共6页
针对传统电能质量扰动分类模型中扰动特征复杂、识别步骤繁琐的问题,提出了一种通过模拟退火(SA)算法与粒子群优化(PSO)算法相结合来优化卷积神经网络(CNN)的电能质量扰动分类模型。将CNN卷积层中的二维卷积核替换成一维卷积核;采用SA... 针对传统电能质量扰动分类模型中扰动特征复杂、识别步骤繁琐的问题,提出了一种通过模拟退火(SA)算法与粒子群优化(PSO)算法相结合来优化卷积神经网络(CNN)的电能质量扰动分类模型。将CNN卷积层中的二维卷积核替换成一维卷积核;采用SA算法对PSO算法进行改进,规避PSO算法陷入局部最优的困境;采用改进后的PSO算法对CNN进行参数寻优;利用优化CNN提取和筛选合适的特征,根据这些特征利用分类器得到最终分类结果。通过算例分析得出,使用基于SA-PSO算法优化的CNN的电能质量扰动分类模型能精确地识别出电能质量扰动信号。 展开更多
关键词 电能质量 扰动分类 卷积神经网络 粒子群优化算法 模拟退火算法 特征提取
下载PDF
Optimization on the Impeller of a Low-specific-speed Centrifugal Pump for Hydraulic Performance Improvement 被引量:14
11
作者 PEI Ji WANG Wenjie +1 位作者 YUAN Shouqi ZHANG Jinfeng 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2016年第5期992-1002,共11页
In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the bla... In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the blade outlet width b2, blade outlet angle β2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0Qd and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations. 展开更多
关键词 low-specific-speed centrifugal pump optimization optimal Latin hypercube sampling surrogate model particle swarm optimization algorithm numerical simulation
下载PDF
Resource allocation optimization of equipment development task based on MOPSO algorithm 被引量:8
12
作者 ZHANG Xilin TAN Yuejin and YANG Zhiwei 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2019年第6期1132-1143,共12页
Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees ... Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees of uncertainty.This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks.Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks.By considering the uncertainties,such as fluctuations in the sub-task duration and cost,rework iterations,and random overlaps,the tasks are simulated for various resource allocation schemes.The shortest duration and the minimum cost of the development task are first formulated as the objective function.Based on a multi-objective particle swarm optimization(MOPSO)algorithm,a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task.Finally,an uninhabited aerial vehicle(UAV)is considered as an example of a development task to test the algorithm,and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-II(NSGA-II),non-dominated sorting differential evolution(NSDE)and strength pareto evolutionary algorithm-II(SPEA-II).The proposed method is verified for its scientific approach and effectiveness.The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively. 展开更多
关键词 resource allocation equipment development task multi-objective particle swarm optimization(MOPSO) develop ment task simulation.
下载PDF
Hybrid Optimization Based PID Controller Design for Unstable System 被引量:1
13
作者 Saranya Rajeshwaran C.Agees Kumar Kanthaswamy Ganapathy 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期1611-1625,共15页
PID controllers play an important function in determining tuning para-meters in any process sector to deliver optimal and resilient performance for non-linear,stable and unstable processes.The effectiveness of the pre... PID controllers play an important function in determining tuning para-meters in any process sector to deliver optimal and resilient performance for non-linear,stable and unstable processes.The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller.The Direct Multi Search(DMS)algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model.A Metaheuristics Algorithm such as,SA(Simulated Annealing),MBBO(Modified Biogeography Based Opti-mization),BBO(Biogeography Based Optimization),PBIL(Population Based Incremental Learning),ES(Evolution Strategy),StudGA(Stud Genetic Algo-rithms),PSO(Particle Swarm Optimization),StudGA(Stud Genetic Algorithms),ES(Evolution Strategy),PSO(Particle Swarm Optimization)and ACO(Ant Col-ony Optimization)are used to tune the PID controller and Smith predictor design.The effectiveness of the suggested algorithms DMS-SA,DMS-BBO,DMS-MBBO,DMS-PBIL,DMS-StudGA,DMS-ES,DMS-ACO,and DMS-PSO for a class of dead-time structures employing PID controller and Smith predictor design controllers is illustrated using unit step set point response.When compared to other optimizations,the suggested hybrid metaheuristics approach improves the time response analysis when extended to the problem of smith predictor and PID controller designed tuning. 展开更多
关键词 Direct multi search simulated annealing biogeography-based optimization stud genetic algorithms particle swarm optimization SmithPID controller
下载PDF
Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation
14
作者 Xinyu Zhang Yang Li Genshen Chu 《Data Intelligence》 EI 2023年第4期904-922,共19页
Parameter calibration is an important part of hydrological simulation and affects the final simulation results.In this paper,we introduce heuristic optimization algorithms,genetic algorithm(GA)to cope with the complex... Parameter calibration is an important part of hydrological simulation and affects the final simulation results.In this paper,we introduce heuristic optimization algorithms,genetic algorithm(GA)to cope with the complexity of the parameter calibration problem,and use particle swarm optimization algorithm(PsO)as a comparison.For large-scale hydrological simulations,we use a multilevel parallel parameter calibration framework to make full use of processor resources,and accelerate the process of solving high-dimensional parameter calibration.Further,we test and apply the experiments on domestic supercomputers.The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above,with PSO achieving a speedup of 58.52 on TianHe-2 supercomputer.The experimental results indicate that using a parallel implementation on multicore CPUs makes high-dimensional parameter calibration in large-scale hydrological simulation possible.Moreover,our comparison of the two algorithms shows that the GA obtains better calibration results,and the PSO has a more pronounced acceleration effect. 展开更多
关键词 Hydrologic simulation Parameter calibration Genetic algorithm particle swarm optimization
原文传递
基于SA-PSO混合算法的微震定位研究 被引量:10
15
作者 张院生 高永涛 +2 位作者 王喆 柴金飞 李健 《现代隧道技术》 EI CSCD 北大核心 2016年第3期137-145,共9页
在综合分析模拟退火法和粒子群算法各自搜寻能力和特点的基础上,结合模拟退火法具有较强跳出局部最优的特性和粒子群算法的全局寻优能力,得到一种搜寻能力更强的SA-PSO混合算法,并将该混合算法应用于微震定位领域。研究结果表明,SA-PSO... 在综合分析模拟退火法和粒子群算法各自搜寻能力和特点的基础上,结合模拟退火法具有较强跳出局部最优的特性和粒子群算法的全局寻优能力,得到一种搜寻能力更强的SA-PSO混合算法,并将该混合算法应用于微震定位领域。研究结果表明,SA-PSO混合算法比SA和PSO算法的定位误差小。在模型计算中采用SA-PSO混合算法,不论波速已知或未知,在非检波器阵列对称面上震源的空间定位误差均在1 m以内;当波速以±1%,±3%,±5%随机浮动时,除距离较远的震源点M6外,定位误差均在50 m以内。最后以建有微震监测系统的冬瓜山铜矿进行工程验证,定位精度约为30 m,满足工程需求。 展开更多
关键词 微震 模拟退火法 粒子群算法 定位精度
下载PDF
基于SA-PSO的电力系统无功优化 被引量:6
16
作者 何佳 吴耀武 +1 位作者 娄素华 熊信艮 《电力系统及其自动化学报》 CSCD 北大核心 2007年第5期114-118,共5页
粒子群优化算法是一种简便易行,收敛快速的演化计算方法。但该算法也存在收敛精度不高,易陷入局部极值的缺点。针对这些缺点,对原算法加以改进,引入了自适应的惯性系数和模拟退火算法的思想,提出了一种新的模拟退火粒子群优化(simulated... 粒子群优化算法是一种简便易行,收敛快速的演化计算方法。但该算法也存在收敛精度不高,易陷入局部极值的缺点。针对这些缺点,对原算法加以改进,引入了自适应的惯性系数和模拟退火算法的思想,提出了一种新的模拟退火粒子群优化(simulated annealing particle swarm optimization,SA-PSO)算法,并将其应用于电力系统无功优化。对IEEE14节点系统进行了仿真计算,并与PSO算法作了比较,结果表明SA-PSO算法全局收敛性能及收敛精度均较PSO算法有了较大提高。 展开更多
关键词 电力系统 无功优化 模拟退火粒子群优化算法 自适应
下载PDF
改进SA-PSO在系统误差配准中的应用 被引量:2
17
作者 周林 潘泉 梁彦 《光电工程》 CAS CSCD 北大核心 2010年第9期27-31,38,共6页
针对融合系统中系统误差未固定的情况,将模拟退火算法SA(Simulated Annealing)引入到改进的粒子群优化算法PSO(Particle Swarm Optimization)中来解决系统误差配准问题。该方法结合了改进PSO的全面、快速寻优能力和SA的概率突跳特性,解... 针对融合系统中系统误差未固定的情况,将模拟退火算法SA(Simulated Annealing)引入到改进的粒子群优化算法PSO(Particle Swarm Optimization)中来解决系统误差配准问题。该方法结合了改进PSO的全面、快速寻优能力和SA的概率突跳特性,解决了PSO容易陷入局部最优的缺点,也保证了群体的多样性,避免了种群的退化。仿真结果表明,改进的SA-PSO方法较PSO、GA方法在系统误差配准精度上得到了提高。 展开更多
关键词 系统误差 误差配准 粒子群优化(PSO) 模拟退火(SA)
下载PDF
基于SA-PSO的多态路径测试数据生成方法 被引量:1
18
作者 曾一 蔡森虎 +2 位作者 覃钊璇 周吉 许林 《计算机应用研究》 CSCD 北大核心 2011年第8期3034-3036,共3页
目前测试数据生成方法多数未考虑到面向对象软件的多态特性,无法运用生成的测试数据对程序的多态信息进行充分的测试。根据多态路径测试数据生成的要求,提出了一种应用模拟退火—粒子群优化(simula-ted annealing-particle swarm optimi... 目前测试数据生成方法多数未考虑到面向对象软件的多态特性,无法运用生成的测试数据对程序的多态信息进行充分的测试。根据多态路径测试数据生成的要求,提出了一种应用模拟退火—粒子群优化(simula-ted annealing-particle swarm optimization,SA-PSO)混合算法在多态路径测试中生成测试数据的方法,并通过多态性实例对基本粒子群算法、遗传算法、PSO-GA(particle swarm optimization-genetic algorithm)和SA-PSO算法在相同条件下进行了比较,结果表明SA-PSO算法具有更强的搜索能力,可以更快地发现全局最优解,能更好地为包含多态信息的测试路径生成测试数据。 展开更多
关键词 粒子群优化算法 模拟退火算法 多态 测试路径 测试数据
下载PDF
基于灰色关联分析与SA-PSO-Elman结合的地震直接经济损失评估 被引量:3
19
作者 宗学军 李强 +2 位作者 杨忠君 何戡 Dimiter Velev 《安全与环境工程》 CAS 2016年第2期19-22,共4页
对地震灾害造成的损失进行评估是国家采取应急救援和灾后援建工作的重要依据。为快速评估地震灾害引起的直接经济损失,提出一种基于灰色关联分析与模拟退火-粒子群-Elman神经网络(SA-PSO-Elman)结合的地震灾害直接经济损失评估模型。该... 对地震灾害造成的损失进行评估是国家采取应急救援和灾后援建工作的重要依据。为快速评估地震灾害引起的直接经济损失,提出一种基于灰色关联分析与模拟退火-粒子群-Elman神经网络(SA-PSO-Elman)结合的地震灾害直接经济损失评估模型。该模型先采用灰色关联分析方法客观地选出地震灾害直接经济损失的主要影响因素,即为Elman神经网络的输入,然后将全局寻优能力强及收敛速度快的粒子群算法与能跳出局部极值的模拟退火算法相结合来优化Elman神经网络的权值和阀值,最后将训练好的Elman神经网络运用到地震灾害直接经济损失评估中。通过仿真试验结果表明:该混合算法优化的Elman神经网络模型比Elman神经网络模型和PSOElman神经网络模型具有更高的预测精度和收敛速度。 展开更多
关键词 地震灾害 直接经济损失评估 灰色关联分析 模拟退火算法 粒子群算法 ELMAN神经网络
下载PDF
SA-PSO算法在智能组卷中的应用研究 被引量:2
20
作者 盛魁 马健 董辉 《长春师范大学学报》 2016年第10期42-46,共5页
通用、健壮、高效的组卷算法是实现考试智能化、规范化、科学化的重要保障。本文在分析智能组卷数学模型及各种组卷算法的基础上,针对目前组卷算法存在组卷时间长、组卷效率低等缺陷,提出了一种基于模拟退火粒子群优化算法(SA-PSO)。将... 通用、健壮、高效的组卷算法是实现考试智能化、规范化、科学化的重要保障。本文在分析智能组卷数学模型及各种组卷算法的基础上,针对目前组卷算法存在组卷时间长、组卷效率低等缺陷,提出了一种基于模拟退火粒子群优化算法(SA-PSO)。将其应用于智能组卷实验,并与RANDOM和PSO算法作比较。实验结果表明,SA-PSO算法使得组卷效率、组卷成功率和组卷满足度等方面均有显著的提高,并且保证了组卷的质量。 展开更多
关键词 模拟退火法 粒子群 智能组卷
下载PDF
上一页 1 2 60 下一页 到第
使用帮助 返回顶部