QPSO算法在非线性观测器设计中的应用*

具有量子行为的粒子群优化(Quantum-behaved Particle Swarm Optimization,QPSO)算法是继粒子群优化算法(Particle Swarm Optimization,PSO)后,最新提出的一种新型、高效的进化算法.提出了运用QPSO算法设计的非线性观测器方法.该方法属于滚动时域估计方法,利用具有量子行为的粒子群算法优化获得系统状态的最优估计.仿真结果显示该方法对初始条件不敏感,具有很强的跟踪能力.     

使用新混合模糊聚类算法的模糊系统建模方法

为了进一步提高模糊系统建立模型的精度,提出一种新的模糊系统算法ANFIS-HC-QPSO：采用一种混合型模糊聚类算法来对模糊系统的输入空间进行划分,每一个聚类通过高斯函数的拟合产生一个隶属度函数,即完成ANFIS系统的前件参数--隶属度函数参数的初始识别,通过具有量子行为的粒子群算法QPSO与最小二乘法优化前件参数,直至达到停机条件,最终得到ANFIS的前件及后件参数,从而得到满意的模糊系统模型。实验表明,AN-FIS-HC-QPSO算法与传统算法相比,能在只需较少模糊规则的前提下就使模糊系统达到更高的精度。     

自适应收扩系数的双中心协作QPSO算法

针对量子粒子群优化(QPSO)算法迭代后期种群多样性下降、收敛速度慢、易陷入局部最优的缺点,提出一种自适应收缩-扩张系数的双中心协作最子粒子群优化算法。该算法从2个方面进行改进:(1)自适应调节收缩-扩张系数,其目的是帮助粒子跳出局部最优点,提高粒子的全局搜索能力;(2)双重更新全局最优位置,即在每次迭代中,先后分别采用2种不同的方式更新全局最优位置。第1种方式与QPSO算法一致,第2种方式则引入双中心粒子,使其和当前全局最优位置在相应维度上合作,从而达到更新全局最优位置的目的。从固定迭代次数和固定精度角度分析算法性能,仿真结果表明,相比于QPSO算法,该算法在保证复杂度较低的情况下,可提高收敛速度,增强全局和局部搜索能力。     

优化Elman神经网络用于网络流量预测

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.     

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.     

基于群智能算法的玻璃切割问题求解研究

通过对玻璃切割问题的研究,提出一种融合量子粒子群优化和蚁群优化的混合算法(QPSO-ACO算法)。该算法对QPSO及ACO的模型进行必要的修改,以实现对玻璃切割中的旅行商问题的较好求解。同时充分利用QPSO的快速性、全局收敛性和ACO的正反馈性及求精解效率高等特点,达到优势互补。实验结果表明,QPSO-ACO算法寻优能力较强,是解决玻璃切割问题的有效方法。     

一种二进制编码的量子粒子群优化算法

针对离散空间优化问题,给出二进制编码的量子粒子群优化（ＢＱＰＳＯ）算法的设计思路,重新定义粒子的位置矢量和粒子之间的距离,提出了ＢＱＰＳＯ 算法的进化方程．通过泛函分析的方法分析了ＢＱＰＳＯ 算法的收敛性,得出全局收敛的结论,并通过多个测试函数测试了ＢＱＰＳＯ 算法的性能．求解结果验证了算法的优越性．     

Training algorithm for radial basis function neural network based on quantum-behaved particle swarm optimization

Radial basis function (RBF) networks are widely applied in function approximation, system identification, chaotic time series forecasting, etc. To use a RBF network, a training algorithm is absolutely necessary for determining the network parameters. The existing training algorithms, such as orthogonal least squares (OLS) algorithm, clustering and gradient descent algorithm, have their own shortcomings respectively. In this paper, we propose a training algorithm based on a novel population-based evolutionary technique, quantum-behaved particle swarm optimization (QPSO), to train RBF neural network. The proposed QPSO-trained RBF network was tested on non-linear system identification problem and chaotic time series forecasting problem, and the results show that it can identify the system and forecast the chaotic time series more quickly and precisely than that trained by the particle swarm algorithm.     

求解非线性方程组的一种新方法及应用

针对非线性方程组求解问题提出一种变异量子粒子群算法,该算法首先把非线性方程组的求解转化为约束优化问题,然后根据可行性规则,引入约束违反度函数,结合变异算子,不断地寻找更优可行解,逐渐达到搜索全局最优解。数值实验表明,所设计变异量子粒子群算法是可行的、有效的,是求解非线性组的一种成功算法。     

量子粒子群算法在测井反演解释中的应用

量子粒子群算法是在粒子群算法的基础上,结合了量子运动原理提出的新算法,在数值试验中与其它的优化算法(如粒子群算法,蚁群算法,拟牛顿法,遗传算法,模拟退火算法)相比较有着收敛快,精度高的优点.粒子群算法,蚁群算法,拟牛顿法等都是测井反演问题中应用较为广泛的优化算法.本文用量子粒子群优化算法来确定侧向测井几何因子表达式,并...