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Description: 在网络异常检测中,为了提高对异常状态的检测率，降低对正常状态的误判率,本文提出一种基于量子粒子群优化算法训练小波神经网络进行网络异常检测的新方法。利用量子粒子群优化算法(QPSO)训练小波神经网络,将小波神经网络(WNN)中的参数组合作为优化算法中的一个粒子,在全局空间中搜索具有最优适应值的参数向量。- In order to improve the detection rate for anomaly state and reduce the false positive rate for normal state in the network anomaly detection, a novel method of network anomaly detection based on constructing wavelet neural network (WNN) using quantum-behaved particle swarm optimization (QPSO) algorithm was proposed. The WNN was trained by QPSO.
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Author: liang |
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Description: 基于二次插值的Quantum Behaved Particle Swarm Optimization，-A New Quantum Behaved Particle Swarm Optimization
This paper presents a variant of Quantum behaved Particle Swarm
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Size: 94208 |
Author: marsangle |
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Description: QoS路由组播问题的QPSO（Quantum-behaved Paricle Swarm Optimization）算法 量子粒子群优化算法 可应用于路由组播模型等-Quantum-behaved Paricle Swarm Optimization Algorithms based on Multicast Routing Issues
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Author: 索 |
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Description: 对量子粒子群优化(QPSO) 算法进行研究，提出了自适应量子粒子群优化(Adaptive QPSO) 算法，用于优化Elman 神经
网络的参数，改进了Elman 神经网络的泛化能力。利用网络流量时间序列数据进行预测，实验结果表明，采用AQPSO 算法优
化获得的Elman 神经网络模型不但具有较强的泛化能力，而且具有良好的稳定性，在网络流量时间序列数据的预测中具有
一定的实用价值-Quantum-behaved particle swarm optimization (QPSO) algorithm is researched and adaptive quantum-behaved particle
swarm optimization (AQPSO) algorithm is proposed in order to improve network’s performance. By applying AQPSO algorithm to
train the net parameters adopted in the Elman neural network, the generalization ability of the Elman neural network is improved. Experimental
results with network traffic time series data forecasting sets show that obtained network model has not only good generalization
properties, but also has better stability. It illustrates that Elman net with AQPSO optimization algorithm has the promising application
in network traffic time series data prediction.
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Size: 312320 |
Author: 张杰 |
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Description: 这是一个正交交叉的量子行为粒子群算法的matlab程序（MOQPSO）,用户可以根据需求自己更换目标函数-this is an multi-collapse Orthogonal cross
quantum-behaved particle swarm optimization program of matlab，the user can revise the Object function for your need.
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Size: 2048 |
Author: 袁永福 |
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Description: 介绍了一种利用量子行为粒子群算法(QPSO)求解多峰函数优化问题的方法。为此，在
QPSO中引进一种物种形成策略，该方法根据群体微粒的相似度并行地分成子群体。每个子群体是
围绕一个群体种子而建立的。对每个子群体通过QPSO算法进行最优搜索。从而保证每个峰值都有
同等机会被找到，因此该方法具有良好的局部寻优特性。将基于物种形成的QPSO算法与粒子群算
法(PSO)对多峰优化问题的结果进行比较。对几个重要的测试函数进行仿真实验结果证明，基于物
种形成的QPSO算法可以尽可能多地找到峰值点，峰值收敛性能优于PS-A quantum behaved particle swarm optimization (QPSO) method for solving multimodal function optimization problems. For this reason, the introduction of a species in QPSO form a strategy, the method is based on the similarity of the groups of particles divided into sub-groups in parallel. Each sub-group is established around the seeds of a group. The QPSO algorithm optimal search for each sub-group. In order to ensure that each peak has the same opportunity to find, this method has good local optimization features. Will compare the results of multimodal optimization problems based on QPSO algorithm and particle swarm optimization (PSO) species formed. Simulation results prove that several important test function, based species formed QPSO algorithm can be as much as possible to find the peak point, the peak convergence outperforms PS
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Author: zhuifenger |
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Description: ：智能算法如粒子群算法已被应用于PID控制器的参数优化，以弥补传统优化方法容易产生振荡和较大超调量
的不足，但是粒子群算法存在易于早熟的缺点，在分析量子粒子群算法的基础上，提出了使用量子粒子群算法优化PID控
制器的参数。为了兼顾控制系统的各项性能指标，根据控制器的实际要求对各项指标进行加权作为算法的目标函数，对
PID控制器进行多目标寻优。通过2个传递函数实例，分别使用z—N、粒子群算法和量子粒子群算法进行了PID控制器
参数优化设计，并对结果进行了分析。-Abstract：Heuristics such as particle swarnl optimization is employed to enhance the capability of traditional techniques，
which is easy to produce surge and big overshoot，but PS0 may be trapped in the local optima of the objective and lead to poor performance．
This paper propesed the quantum-behaved particle 8wsl in optimization for the parameter optimization of PID controller．
A fitness function containing performance indexes Was defined and the algorithm Was used in multi-object optimization of PID controllers．
Two examples were given to illustrate the design procedure and exhibit the effectiveness of the proposed method via tomo
parison study with the existing Z—N and PSO approaches．
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Author: dhskja |
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Description: 针对量子粒子群优化算法在处理高维复杂函数时存在的收敛速度慢、易陷入局部极小等问题，提出了混沌量子粒子群优化算法。-Abstract：Using quantum-behaved particle swarmoptimization (QPSO) to handle complex functions with high-dimension has the problems
of low convergence speed and sensitivity to local convergence.
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Size: 68608 |
Author: 耒阳 |
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Description: Application of quantum-behaved particle swarm algorithm in clustering of genes量子行为粒子群算法在基因聚类中的应用-Application of quantum-behaved particle swarm algorithm in clustering of genes
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Author: 张人龙 |
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Description: 量子理论和离子群优化算法结合，构建的量子粒子群优化算法，获得较的效果！-Quantum theory and particle swarm optimization algorithm combining quantum behaved particle swarm optimization algorithm to construct, acquire more effect!
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Size: 3072 |
Author: zhang |
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Description: 本人所写的基于量子行为优化的粒子群算法，很好的解决了粒子群算法易于陷入局部最优解的缺点-I wrote particle swarm optimization based on quantum-behaved, a good solution to the shortcomings of particle swarm algorithm is easy to fall into local optima
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Size: 2048 |
Author: mikhansay |
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Description: Quantum-behaved particle swarm optimization with Gaussian
distributed local attractor point
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Author: jafarisa |
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