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Feature Selection Method by Applying Parallel Collaborative Evolutionary Genetic Algorithm 被引量:1
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作者 Hao-Dong Zhu Hong-Chan Li +1 位作者 Xiang-Hui Zhao Yong Zhong 《Journal of Electronic Science and Technology》 CAS 2010年第2期108-113,共6页
Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature ... Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy. 展开更多
关键词 Index Terms-Feature selection genetic algorithm parallel collaborative evolutionary text mining.
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Forward and backward models for fault diagnosis based on parallel genetic algorithms 被引量:10
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作者 Yi LIU Ying LI +1 位作者 Yi-jia CAO Chuang-xin GUO 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2008年第10期1420-1425,共6页
In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of faul... In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems. 展开更多
关键词 Forward and backward models Fault diagnosis Global single-population master-slave genetic algorithms (GPGAs) parallel computation
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Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing
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作者 Jiabao Wen Jiachen Yang +2 位作者 Tianying Wang Yang Li Zhihan Lv 《Digital Communications and Networks》 SCIE CSCD 2023年第2期473-482,共10页
To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel c... To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge computing.Wireless Sensor Network(WSN)is a typical application of parallel computation.To achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be extended.Therefore,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network.This paper proposes a task model and a cluster-based WSN model in edge computing.In our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more practical.Then we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)algorithm.The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended.The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing. 展开更多
关键词 Wireless sensor network parallel computation Task allocation genetic algorithm Ant colony optimization algorithm ENERGY-EFFICIENT Load balancing
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Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions 被引量:4
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作者 谭冠政 周代明 +1 位作者 江斌 DIOUBATE Mamady I 《Journal of Central South University of Technology》 EI 2008年第6期845-852,共8页
A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism (IGAE). In IGAE, the new methods for computing antibody s... A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism (IGAE). In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism. 展开更多
关键词 immune genetic algorithm multi-modal function optimization evolutionary computation elitist selection elitist crossover
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A Hybrid Algorithm Based on PSO and GA for Feature Selection 被引量:1
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作者 Yu Xue Asma Aouari +1 位作者 Romany F.Mansour Shoubao Su 《Journal of Cyber Security》 2021年第2期117-124,共8页
One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection... One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset. 展开更多
关键词 Evolutionary computation genetic algorithm hybrid approach META-HEURISTIC feature selection particle swarm optimization
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An optimizing algorithm of static task scheduling problem based on hybrid genetic algorithm 被引量:3
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作者 柳玉 Song Jian Wen Jiayan 《High Technology Letters》 EI CAS 2016年第2期170-176,共7页
To reduce resources consumption of parallel computation system, a static task scheduling opti- mization method based on hybrid genetic algorithm is proposed and validated, which can shorten the scheduling length of pa... To reduce resources consumption of parallel computation system, a static task scheduling opti- mization method based on hybrid genetic algorithm is proposed and validated, which can shorten the scheduling length of parallel tasks with precedence constraints. Firstly, the global optimal model and constraints are created to demonstrate the static task scheduling problem in heterogeneous distributed computing systems(HeDCSs). Secondly, the genetic population is coded with matrix and used to search the total available time span of the processors, and then the simulated annealing algorithm is introduced to improve the convergence speed and overcome the problem of easily falling into local minimum point, which exists in the traditional genetic algorithm. Finally, compared to other existed scheduling algorithms such as dynamic level scheduling ( DLS), heterogeneous earliest finish time (HEFr), and longest dynamic critical path( LDCP), the proposed approach does not merely de- crease tasks schedule length, but also achieves the maximal resource utilization of parallel computa- tion system by extensive experiments. 展开更多
关键词 genetic algorithm simulated annealing algorithm parallel computation directedacyelic graph
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A Neurocomputing Model for Binary Coded Genetic Algorithm
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作者 GongDaoxiong RuanXiaogang 《工程科学(英文版)》 2004年第3期85-91,共7页
A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint o... A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint of parallel GA (PGA) and those of hardware GA (HGA). Moreover a new crossover operator named universe crossover was also proposed to suit the NN-based realization. This model was tested with a benchmark function set, and the experimental results validated the potential of the neurocomputing model. The significance of this model means that HGA and PGA can be integrated and the inherent parallelism of GA can be explicitly and farthest realized, as a result, the optimization speed of GA will be accelerated by one or two magnitudes compered to the serial implementation with same speed hardware, and GA will be turned from an algorithm into a machine. 展开更多
关键词 神经计算模型 二进制编码 遗传算法 神经网络 交叉算子 并行计算
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Parallelization and sustainability of distributed genetic algorithms on many-core processors
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作者 Yuji Sato Mikiko Sato 《International Journal of Intelligent Computing and Cybernetics》 EI 2014年第1期2-23,共22页
Purpose–The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core pr... Purpose–The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core processors such as graphics processing units(GPUs)and multi-core processors(MCPs).Design/methodology/approach–For distributed genetic algorithm(GA)models,the paper proposes a method where an island’s ID number is added to the header of data transferred by this island for use in fault detection.Findings–The paper has shown that the processing time of the proposed idea is practically negligible in applications and also shown that an optimal solution can be obtained even with a single stuck-at fault or a transient fault,and that increasing the number of parallel threads makes the system less susceptible to faults.Originality/value–The study described in this paper is a new approach to increase the sustainability of application program using distributed GA on GPUs and MCPs. 展开更多
关键词 Evolutionary computation genetic algorithms Fault identification Many-core processors parallelIZATION
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MOEAGAC:an energy aware model with genetic algorithm for efficient scheduling in cloud computing
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作者 Nageswara Prasadhu Marri N.R.Rajalakshmi 《International Journal of Intelligent Computing and Cybernetics》 EI 2022年第2期318-329,共12页
Purpose-Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism.This research aims to propose the optimization of makespan,energy ... Purpose-Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism.This research aims to propose the optimization of makespan,energy consumption and data transfer time(DTT)by considering the priority tasks.The research work is concentrated on the multi-objective approach based on the genetic algorithm(GA)and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approach-Cloud computing is the recent advancement of the distributed and cluster computing.Cloud computing offers different services to the clients based on their requirements,and it works on the environment of virtualization.Cloud environment contains the number of data centers which are distributed geographically.Major challenges faced by the cloud environment are energy consumption of the data centers.Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan.This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm.This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines.The energy model is developed for picking the task based on the fitness function.The simulation results show the performance of the multi-objective model with respect to makespan,DTT and energy consumption.Findings-The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine.The directed acyclic graph is used to represent the task dependencies.The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms.The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/value-This paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm.The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing.The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection.The energy model is used as fitness function to theGAfor selecting the tasks to perform the scheduling. 展开更多
关键词 Cloud computing PRIORITY Roulette wheel selection genetic algorithm POPULATION
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A novel genetic algorithm based on all spanning trees of undirected graph for distribution network reconfiguration 被引量:9
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作者 Jian ZHANG Xiaodong YUAN Yubo YUAN 《Journal of Modern Power Systems and Clean Energy》 SCIE EI 2014年第2期143-149,共7页
Network reconfiguration is of theoretical and practical significance to guarantee safe and economical operation of distribution system.In this paper,based on all spanning trees of undirected graph,a novel genetic algo... Network reconfiguration is of theoretical and practical significance to guarantee safe and economical operation of distribution system.In this paper,based on all spanning trees of undirected graph,a novel genetic algorithm for electric distribution network reconfiguration is proposed.Above all,all spanning trees of simplified graph of distribution network are found.Tie branches are obtained with spanning tree subtracted from simplified graph.There is one and only one switch open on each tie branch.Decimal identity number of open switch on each tie branch is taken as the optimization variable.Therefore,the length of chromosome is very short.Each spanning tree corresponds to one subpopulation.Gene operations of each subpopulation are implemented with parallel computing method.Individuals of offspring after gene operation automatically meet with radial and connected constraints for distribution network operation.Disadvantages of conventional genetic algorithm for network reconfiguration that a large amount of unfeasible solutions are created after crossover and mutation,which result in very low searching efficiency,are completely overcome.High calculation speed and superior capability of the proposed method are validated by two test cases. 展开更多
关键词 Network reconfiguration genetic algorithm paralleling computing All spanning trees of undirected graph Decimal coding Distribution network
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Global annealing genetic algorithm and its convergence analysis
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作者 张讲社 徐宗本 梁怡 《Science China(Technological Sciences)》 SCIE EI CAS 1997年第4期414-424,共11页
A new selection mechanism termed global annealing selection (GAnS) is proposed for the genetic algorithm. It is proved that the GAnS genetic algorithm converges to the global optimums if and only if the parents are al... A new selection mechanism termed global annealing selection (GAnS) is proposed for the genetic algorithm. It is proved that the GAnS genetic algorithm converges to the global optimums if and only if the parents are allowed to compete for reproduction, and that the variance of population’s fitness can be used as a natural stopping criterion. Numerical simulations show that the new algorithm has stronger ability to escape from local maximum and converges more rapidly than canonical genetic algorithm. 展开更多
关键词 genetic algorithm simulated EVOLUTIONARY computation computational INTELLIGENCE ANNEALING selection MARKOV chain.
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Partner selection model and soft computing approach for dynamic alliance of enterprises 被引量:5
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作者 汪定伟 容启亮 叶伟雄 《Science in China(Series F)》 2002年第1期68-80,共13页
Partner selection is an active research topic in agile manufacturing and supply chain management. In this paper, the problem is described by a 0-1 integer programming with non-analytical objective function. Then, the ... Partner selection is an active research topic in agile manufacturing and supply chain management. In this paper, the problem is described by a 0-1 integer programming with non-analytical objective function. Then, the solution space is reduced by defining the inefficient candidate. By using the fuzzy rule quantification method, a fuzzy logic based decision making approach for the project scheduling is proposed. We then develop a fuzzy decision embedded genetic algorithm. We compare the algorithm with tranditional methods. The results show that the suggested approach can quickly achieve optimal solution for large size problems with high probability. The approach was applied to the partner selection problem of a coal fire power station construction project. The satisfactory results have been achieved. 展开更多
关键词 aglie manufacturing dynamic alliance partner selection soft computing fuzzy logic genetic algorithm .gorithm.
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基于种群混合迁移策略的并行量子遗传算法 被引量:1
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作者 陆涛 管荑 +2 位作者 贾鹏 曲志坚 王子灵 《计算机工程与设计》 北大核心 2024年第8期2386-2392,共7页
针对量子遗传算法求解大规模优化问题存在收敛速度慢、易于陷入局部最优等问题,改进量子遗传算法。设计一种种群混合迁移机制促进算法的种群多样性,采用仿TriBA种群结构、双精英种群、重生种群、自适应迁移算子、个体竞争排挤算子以及... 针对量子遗传算法求解大规模优化问题存在收敛速度慢、易于陷入局部最优等问题,改进量子遗传算法。设计一种种群混合迁移机制促进算法的种群多样性,采用仿TriBA种群结构、双精英种群、重生种群、自适应迁移算子、个体竞争排挤算子以及随机失活机制,提高算法的局部勘测能力和全局寻优能力。利用Spark框架实现算法在分布式集群环境下的运算。改进2-opt&R优化算法,通过引入高斯变异提高算法的局部搜索能力,缩小算法的搜索空间。实验结果表明,改进后的算法在全局优化能力、收敛速度、运行速度和求解稳定性等方面均有大幅度提升。 展开更多
关键词 量子遗传算法 种群迁移 Spark框架 并行计算 收敛速度 全局优化 搜索空间
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Trajectory Tracking of a Planer Parallel Manipulator by Using Computed Force Control Method 被引量:6
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作者 Atilla BAYRAM 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2017年第2期449-458,共10页
Despite small workspace, parallel manipulators have some advantages over their serial counterparts in terms of higher speed, acceleration, rigidity, accuracy, manufacturing cost and payload. Accordingly, this type of ... Despite small workspace, parallel manipulators have some advantages over their serial counterparts in terms of higher speed, acceleration, rigidity, accuracy, manufacturing cost and payload. Accordingly, this type of manipulators can be used in many applications such as in high-speed machine tools, tuning machine for feeding, sensitive cutting, assembly and packaging. This paper presents a special type of planar parallel manipulator with three degrees of freedom. It is constructed as a variable geometry truss generally known planar Stewart platform. The reachable and orientation workspaces are obtained for this manipulator. The inverse kinematic analysis is solved for the trajectory tracking according to the redundancy and joint limit avoidance. Then, the dynamics model of the manipulator is established by using Virtual Work method. The simulations are performed to follow the given planar trajectories by using the dynamic equations of the variable geometry truss manipulator and computed force control method. In computed force control method, the feedback gain matrices for PD control are tuned with fixed matrices by trail end error and variable ones by means of optimization with genetic algorithm. 展开更多
关键词 parallel manipulator Variable geometry truss manipulator Planar Stewart platform. Dynamic analysis Computed force control genetic algorithm
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智慧交通场景下云边端协同的多目标优化卸载决策 被引量:1
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作者 朱思峰 宋兆威 +2 位作者 陈昊 朱海 乔蕊 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2024年第3期63-75,共13页
随着智慧交通、云计算网络以及边缘计算网络的快速发展,车载终端与路基单元、中心云服务器之间的信息交互变得越发频繁。针对智慧交通云边端协同计算场景下如何高效地实现车路云一体化融合感知、群体决策以及各级服务器间对资源的合理... 随着智慧交通、云计算网络以及边缘计算网络的快速发展,车载终端与路基单元、中心云服务器之间的信息交互变得越发频繁。针对智慧交通云边端协同计算场景下如何高效地实现车路云一体化融合感知、群体决策以及各级服务器间对资源的合理分配问题,设计了基于云边端与智慧交通全面融合的网络架构。在该架构下,通过对任务类型的合理划分,再由各服务器对其进行选择性的缓存、卸载;在智慧交通云边端协同计算场景下,依次设计了一种对任务自适应的缓存模型、任务卸载时延模型、系统能量损耗模型、车载用户对服务质量不满意度评价模型、多目标优化问题模型,并给出了一种基于改进型非支配遗传算法的任务卸载决策方案。实验结果表明,文中方案能够有效降低任务卸载过程中所带来的时延和能耗,提高了系统资源利用率,给车辆用户带来更好的服务体验。 展开更多
关键词 智慧交通 云边端协同计算 卸载决策 多目标优化算法 非支配遗传算法
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基于并行自适应遗传算法的水文模型率定研究
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作者 左翔 马剑波 丛小飞 《水利水电技术(中英文)》 北大核心 2024年第3期102-112,共11页
【目的】参数率定是影响水文模型预报精度的重要因素,采用人工智能算法可以有效提高水文模型参数的率定效果。【方法】采用基于种群离散程度的自适应算子,对GA算法的交叉、变异和迁移过程进行自适应优化,并利用粗粒度并行计算模型提高... 【目的】参数率定是影响水文模型预报精度的重要因素,采用人工智能算法可以有效提高水文模型参数的率定效果。【方法】采用基于种群离散程度的自适应算子,对GA算法的交叉、变异和迁移过程进行自适应优化,并利用粗粒度并行计算模型提高种群进化效率,综合以上手段研究了一种基于自适应策略的并行遗传算法。将传统遗传算法(GA),串行自适应遗传算法(AGA)和并行自适应遗传算法(PAGA),应用于屯溪流域新安江模型的参数率定,从率定效率、率定收敛性、率定稳定性和率定效果四个方面,验证PAGA算法的综合性能。【结果】结果表明:PAGA算法的计算加速效果显著,在10核环境下相对于AGA算法计算时间减少了87.9%;在进化后期,PAGA算法能够更加稳定的收敛于最优解,收敛后的目标函数值具有更好的稳定性;在验证期的场次洪水模拟中,采用PAGA算法率定的模型模拟效果最优,总体洪水合格率大于90%,确定性系数均值为0.85。【结论】PAGA算法能够明显降低模型参数寻优耗时,改善模型率定效果和收敛性能,为水文模型参数的率定提供了新思路。 展开更多
关键词 水文预报 遗传算法 自适应策略 新安江模型 并行计算 人工智能算法 径流 数值模拟
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串行式混合类型航道船舶交通组织优化 被引量:1
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作者 王志强 张新宇 +1 位作者 李倍莹 王婧贇 《计算机应用与软件》 北大核心 2023年第2期26-32,39,共8页
随着港口航道类型逐渐向多样化、复杂化的混合类型趋势发展,港口交通问题愈发严峻。调研国内外港口混合类型航道,抽象出一种串行式简单混合类型航道作为研究对象。分析混合航道船舶交通状况,构建以单向/混合通航模式转化、混合航道异类... 随着港口航道类型逐渐向多样化、复杂化的混合类型趋势发展,港口交通问题愈发严峻。调研国内外港口混合类型航道,抽象出一种串行式简单混合类型航道作为研究对象。分析混合航道船舶交通状况,构建以单向/混合通航模式转化、混合航道异类子航道间通航模式切换、港池连接水域船舶交通冲突消解等为约束的串行式简单混合类型航道船舶交通组织优化模型。基于Spark并行计算框架,结合NSGA-II算法遗传操作天然并行性特点,提出一种Spark分布式多目标遗传算法,将全部种群分散在多节点上并行执行算法的遗传操作。实验表明,提出的算法具有较快的收敛速度和较好的稳定性,模型求解出的优化方案合理、有效。 展开更多
关键词 混合类型航道 船舶交通组织优化 分布式多目标遗传算法 Spark并行计算框架
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基于并行遗传算法的高轨卫星导航选星方法 被引量:1
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作者 石涛 庄学彬 +1 位作者 林子健 曾小慧 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2023年第12期3528-3536,共9页
高轨航天器自主导航能力在北斗三号卫星导航系统建成后得到了增强,但是也带来了部分时刻可见星数量冗余的问题。为降低运算量以保证服务的实时性,提出一种利用多种群并行遗传算法(PGA)进行快速选择当前最优可见星组合的方法。该方法将... 高轨航天器自主导航能力在北斗三号卫星导航系统建成后得到了增强,但是也带来了部分时刻可见星数量冗余的问题。为降低运算量以保证服务的实时性,提出一种利用多种群并行遗传算法(PGA)进行快速选择当前最优可见星组合的方法。该方法将加权精度因子(WDOP)作为适应度评判标准,利用粗粒度式并行划分成的多个子种群进行搜索加速,并通过变异因子差异化设置与子种群间的信息交流来提高搜索能力。对多个典型高轨环境下7颗及以上选星任务的仿真测试表明,基于PGA的选星方法解相比遍历法所求最优解绝对误差平均值小于0.1,相对误差最大不超过1%。仿真结果表明,在典型高轨环境F1接收机利用四系统组合导航时,所提方法可以有效地快速、准确完成指定卫星数的选星任务。 展开更多
关键词 北斗三号卫星导航系统 高轨 选星 并行遗传算法 加权精度因子
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基于并行计算和遗传算法的钢-UHPC华夫板组合梁优化设计 被引量:1
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作者 史腾 朱劲松 +1 位作者 王子挺 秦亚婷 《计算力学学报》 CAS CSCD 北大核心 2023年第3期357-365,共9页
为实现钢-超高性能混凝土(UHPC)华夫板组合梁结构快速经济合理的设计,提出了基于并行计算与遗传算法的结构优化设计方法.通过Python建立了并行计算平台,使Abaqus和Python能够执行同步数值模拟和数据处理,以成本最小化为目标,采用遗传算... 为实现钢-超高性能混凝土(UHPC)华夫板组合梁结构快速经济合理的设计,提出了基于并行计算与遗传算法的结构优化设计方法.通过Python建立了并行计算平台,使Abaqus和Python能够执行同步数值模拟和数据处理,以成本最小化为目标,采用遗传算法对钢-UHPC华夫板组合梁进行了优化,验证了所提方法的可行性.结果表明,遗传算法中密集的分析任务可以并行化并分配给不同的计算资源以提高计算效率;使用并行计算可以提高8.6倍的优化效率;并行计算和串行计算的CPU平均使用率分别为82%和18%.本文方法的成功应用可为其他类型结构的优化设计提供参考. 展开更多
关键词 超高性能混凝土 华夫板 组合梁 结构优化设计 并行计算 遗传算法
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基于多轴振动环境再现的地铁车辆排障器寿命评估研究
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作者 郑雨豪 吴兴文 +2 位作者 刘阳 池茂儒 梁树林 《机车电传动》 北大核心 2023年第6期88-98,共11页
车辆服役过程中,由于轮轨间的高频激励,极易引发结构共振疲劳问题。文章针对某地铁车辆排障器的疲劳开裂问题,开展了排障器的振动疲劳试验,发现了线路上通过频率为93 Hz的钢轨波磨是引发结构共振疲劳的主要原因。针对试验研究方法存在... 车辆服役过程中,由于轮轨间的高频激励,极易引发结构共振疲劳问题。文章针对某地铁车辆排障器的疲劳开裂问题,开展了排障器的振动疲劳试验,发现了线路上通过频率为93 Hz的钢轨波磨是引发结构共振疲劳的主要原因。针对试验研究方法存在的传感器布置数量有限、成本较大等问题,文章从仿真分析角度,提出了一种基于多轴振动环境再现的地铁车辆排障器寿命评估方法。首先,建立了基于虚拟激励法的排障器随机振动模型,该模型可以很好地再现排障器的实际振动环境;然后,通过遗传算法对模态阻尼比进行优化,使得仿真和实测响应结果具有较好的一致性,验证了建模方法的正确性;最后,利用多核并行计算的方法实现了排障器任意位置的全程损伤和疲劳寿命计算,显著提高了仿真计算的效率。结果表明:排障器焊缝拐角处为其结构的薄弱位置,其全程工况损伤值为6.25E-03,疲劳寿命为0.60万km,远低于设计使用寿命的360万km,通过上述方法评估出来的排障器最大损伤位置与结构实际破坏位置相同,进一步证明了此方法的正确性。 展开更多
关键词 地铁车辆 疲劳试验 排障器多轴振动模型 遗传算法 并行计算 疲劳寿命
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