Hybrid optimization algorithm based on chaos,cloud and particle swarm optimization algorithm

As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.

Power system stabilizer design using hybrid multi-objective particle swarm optimization with chaos

A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.

Chaos quantum particle swarm optimization for reactive power optimization considering voltage stability

The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.

Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

The performance of the support vector machine models depends on a proper setting of its parameters to a great extent.A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed.A multi-fault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines.The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine,and the precision and reliability of the fault classification results can meet the requirement of practical application.It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.

Control of Neural Network Feedback Linearization Based on Chaotic Particle Swarm Optimization

A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.

Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization

This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.

A New Class of Hybrid Particle Swarm Optimization Algorithm

A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.

Feature Selection Optimization for Mahalanobis-Taguchi System Using Chaos Quantum-Behavior Particle Swarm

The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper proposes an MTS variable optimization method based on chaos quantum-behavior particle swarm.In order to avoid the influence of complex collinearity on the distance measurement results,the Gram-Schmidt orthogonalization method is first used to calculate the Mahalanobis distance(MD)value.Then,the optimal threshold point of the system classification is determined through the receiver operating characteristic(ROC)curve;the misclassification rate and the selected variables are defined;the multi-objective mixed programming model is built.The chaos quantum-behavior particle swarm optimization(CQPSO)algorithm is proposed to solve the optimization combination,and the algorithm performs binary coding on the particle based on probability.Using the optimized combination of variables,a new Mahalanobis-Taguchi metric based prediction system is established to complete the task of precise discrimination.Finally,a fault diagnosis for the steel plate is taken as an example.The experimental results show that the proposed method can effectively enhance the iterative speed and optimization precision of the particles,and the prediction accuracy of the optimized MTS is significantly improved.

Improved PSO algorithm based on chaos theory and its application to design flood hydrograph

The deficiencies of basic particle swarm optimization （bPSO） are its ubiquitous prematurity and its inability to seek the global optimal solution when optimizing complex high-dimensional functions. To overcome such deficiencies, the chaos-PSO （COSPSO） algorithm was established by introducing the chaos optimization mechanism and a global particle stagnation-disturbance strategy into bPSO. In the improved algorithm, chaotic movement was adopted for the particles＇ initial movement trajectories to replace the former stochastic movement, and the chaos factor was used to guide the particles＇ path. When the global particles were stagnant, the disturbance strategy was used to keep the particles in motion. Five benchmark optimizations were introduced to test COSPSO, and they proved that COSPSO can remarkably improve efficiency in optimizing complex functions. Finally, a case study of COSPSO in calculating design flood hydrographs demonstrated the applicability of the improved algorithm.

基于CPSO-Elman神经网络矿井下可见光定位

针对传统矿井下定位方法精度偏低问题,提出一种混沌粒子群优化(CPSO)Elman神经网络矿井下可见光定位系统。由于Elman神经网络在初始化时存在参数设置的随机性导致预测精度不高,采用CPSO算法优化Elman神经网络,选取适合的各层的初始权值和阈值,用于提高神经网络拓扑的稳定性。仿真结果表明:在3.6 m×3.6 m×3.6 m的环境里,本文所提的算法的平均定位误差达到3.70 cm,最大定位误差为26.54 cm,在实验阶段的平均定位误差为5.91 cm,最大定位误差为36.95 cm,能够满足煤矿井下定位需求。

Optimal Parameter Estimation of Transmission Line Using Chaotic Initialized Time-Varying PSO Algorithm

Transmission line is a vital part of the power system that connects two major points,the generation,and the distribution.For an efficient design,stable control,and steady operation of the power system,adequate knowledge of the transmission line parameters resistance,inductance,capacitance,and conductance is of great importance.These parameters are essential for transmission network expansion planning in which a new parallel line is needed to be installed due to increased load demand or the overhead line is replaced with an underground cable.This paper presents a method to optimally estimate the parameters using the input-output quantities i.e.,voltages,currents,and power factor of the transmission line.The equivalentπ-network model is used and the terminal data i.e.,sending-end and receiving-end quantities are assumed as available measured data.The parameter estimation problem is converted to an optimization problem by formulating an error-minimizing objective function.An improved particle swarm optimization(PSO)in terms of time-varying control parameters and chaos-based initialization is used to optimally estimate the line parameters.Two cases are considered for parameter estimation,the first case is when the line conductance is neglected and in the second case,the conductance is considered into account.The results obtained by the improved algorithm are compared with the standard version of the algorithm,firefly algorithm and artificial bee colony algorithm for 30 number of trials.It is concluded that the improved algorithm is tremendously sufficient in estimating the line parameters in both cases validated by low error values and statistical analysis,comparatively.

PSS and SVC Controller Design using Chaos, PSO and SFL Algorithms to Enhancing the Power System Stability

In this paper, the Authors present the designing of power system stabilizer (PSS) and static var compensator (SVC) based on chaos, particle swarm optimization (PSO) and shuffled frog leaping (SFL) Algorithms has been presented to improve the power system stability. Single machine infinite bus (SMIB) system with SVC located at the terminal of generator has been considered to evaluate the proposed SVC and PSS controllers. The coefficients of PSS and SVC controller have been optimized by Chaos, PSO and SFL algorithms. Fi-nally the system with proposed controllers is simulated for the special disturbance in input power of genera-tor, and then the dynamic responses of generator have been presented. The simulation results show that the system composed with recommended controller has outstanding operation in fast damping of oscillations of power system and describes an application of Chaos, PSO and SFL algorithms to the problem of designing a Lead-Lag controller used in PSS and SVC in power system.

一种改进的CPSO-LSSVM软测量模型及其应用

针对最小二乘支持向量机(LS-SVM)在处理大规模数据集的回归和分类问题时缺少支持向量所具有的稀疏性和难以确定最佳模型参数值的问题,提出一种改进算法,利用样本间马氏距离分析样本相似程度,剔除部分相关样本,对样本集进行约简,以恢复LS-SVM的稀疏性,进而利用具有较强全局搜索能力的混沌粒子群优化算法(CPSO)对LS-SVM建模过程中的模型参数进行优化选择,以提高模型的拟合精度和泛化能力。将提出的改进算法用于湿法脱硫系统浆液pH值的软测量建模,给出了应用该方法的具体步骤,研究结果表明,该算法取得了较高的建模精度和泛化能力,为pH值的在线实时监测提供了一个有效手段。

基于CPSO与LSSVM融合的发酵过程软测量建模

发酵过程是一个复杂的时变、非线性、强耦合过程。发酵过程中的关键参量菌体浓度通常难以用传统物理传感器实时在线检测。为了测量该参数,将CPSO算法与LSSVM相结合构建发酵过程软测量模型。模型采用CPSO算法优化LSSVM软测量模型参数,克服了常规交叉验证法选取参数的耗时和盲目性。仿真结果表明,CPSO-LSSVM软测量模型较LSSVM软测量模型更能在较短的时间内获得较高的收敛精度,其平均误差为2.05%,说明该软测量模型可用于发酵过程不可在线测量的菌体浓度的实时在线软测量,并且预测精度高,预测速度快,预测能力强。该软测量建模方法也为发酵过程其他关键参量的实时在线测量提供了新的途径。

CPSO-LSSVM在自回归钟差预报中的应用

建立了基于自回归算法的钟差预报模型,利用具有较强非线性运算能力和容错能力的最小二乘-支持向量机算法来求解自回归参数,同时利用具有快速寻优特点的粒子群算法来优化最小二乘-支持向量机参数。为了克服粒子群算法容易陷入局部极值而形成早熟的缺点,提出了分别在粒子初始化位置和陷入局部极值的位置上进行混沌处理,提高了粒子搜索的遍历性和寻优能力,从整体上优化了算法。最后通过星载钟差数据对该算法进行了验证,结果表明:本文算法能够实现亚纳秒量级的预报精度并提升卫星授时导航性能。

基于CPSO-LSSVM的汽轮机热耗率软测量模型

为了准确建立汽轮机热耗率预测模型,提出了一种基于变空间Logistic混沌粒子群算法(CPSO)优化最小二乘支持向量机(LSSVM)的汽轮机热耗率软测量模型。采用变空间Logistic混沌搜索策略和粒子镜像越界处理策略来改善粒子群算法(PSO)的全局优化性能,提出了CPSO优化最小二乘支持向量机的超参数以改善模型预测精度,并以某600 MW汽轮机组为研究对象,利用该机组的运行数据建立CPSO-LSSVM的热耗率预测模型。结果表明:CPSO-LSSVM模型具有更高的预测精度和更强的泛化能力,能够准确有效地预测热电厂的汽轮机热耗率。

CPSO和LSSVM融合的网络入侵检测

网络攻击具有多样性和隐蔽性,为了提高网络安全性入侵检测的正确率,提出一种混沌粒子群算法(CPSO)和最小二乘支持向量机(LSSVM)相融合的网络入侵检测方法(CPSO-LSSVM)。利用混沌粒子群算法对LSSVM模型参数进行搜索,选择LSSVM最优参数,采用KDDCUP99数据集对CPSO-LSSVM性能进行测试,实验结果表明,CPSO-LSSVM提高了网络入侵检测正确率,降低了误报率,可以为网络安全提供有效保证。

基于CPSO的二维Otsu图像分割法

二维Otsu方法同时考虑了图像的灰度信息和像素间的空间邻域信息,图像分割效果好但算法计算量较大。针对上述情况,提出一种基于混沌粒子群优化算法(CPSO)的策略,将其用于二维Otsu方法中,并与标准粒子群优化算法(SPSO)进行仿真实验对比。实验结果表明,该方法可以提高分割速度,克服SPSO的缺点,图像分割结果较理想。

基于ICPSO优化的极限学习机在故障诊断中的应用

极限学习机(extreme learning machine,ELM)的分类性能受随机产生的输入权值和隐层阈值的影响,为此,提出一种改进的混沌粒子群算法(ICPSO),用以优化输入权值和阈值,得到基于ICPSO优化的ELM故障诊断模型。仿真和实验结果表明,ICPSO算法改善了ELM网络的学习效率和诊断精度,可有效应用于故障诊断。