基于粒子群与支持向量机的隧道变形预测模型

针对粒子群算法易早熟且在算法后期易在全局最优解附近产生振荡现象,提出一种自适应调整惯性权重的优化粒子群算法。该算法引入双曲线正切函数的非线性变化思想,使惯性权重随着迭代次数的增加产生自适应调整,有利于增强粒子搜索能力及收敛速度,不易陷入局部极值点。将该算法应用于基于支持向量机的隧道变形预测模型中,对预测模型的超参数进行优化,并利用稳态与非稳态两组实测工况数据对组合算法进行工程测试,结果表明采用SaωPSO＋SVM算法可有效提高预测模型的计算精度,增强其鲁棒性,有助于隧道变形的工程建模。     

一种并行协同粒子群优化的支持向量机预测模型

转炉提钒过程是一个非常复杂的多元非线性反应过程,从统计学和反应机理等角度出发,难以建立终点控制静态模型．针对这样的问题,提出了并行协同粒子群优化的支持向量机预测模型,不仅克服了支持向量机偏差ε和折中参数C选择的随机性,而且较好地解决了大数据集的快速并行计算,缩短了计算时间,从而有利于连续生产操作．试验表明,用该模型预测转炉提钒的冷却剂加入量和吹氧时间,结果的误差减小,满足了终点命中率在90%以上的指标,具有工程实用性．     

基于粒子群最小二乘支持向量机的水文预测

支持向量机理论为研究中长期水文预测提供了新的方法。针对最小二乘支持向量机模型参数选择费时且效果差这一问题,给出基于粒子群算法的最小二乘支持向量机水文预测模型(PSO-LSSVM)。该模型运用最小二乘支持向量机回归原理建立,参数选取采用具有全局搜索能力的粒子群算法进行寻优。用此模型对南桠河冶勒水电站月径流进行预测,仿真计算结果表明,该算法可提高预测效率与预测精度。     

基于粒子群优化和支持向量机的花粉浓度预测模型

为了提高花粉浓度预报的准确率，解决现有花粉浓度预报准确率不高的问题，提出了一种基于粒子群优化（PSO）算法和支持向量机（SVM）的花粉浓度预报模型。首先，综合考虑气温、气温日较差、相对湿度、降水量、风力、日照时数等多种气象要素，选择与花粉浓度相关性较强的气象要素构成特征向量；其次，利用特征向量与花粉浓度数据建立SVM预测模型，并使用PSO算法找出最优参数；然后利用最优参数优化花粉浓度预测模型；最后，使用优化后的模型对花粉未来24 h浓度进行预测，并与未优化的SVM、多元线性回归法（MLR）、反向神经网络（BPNN）作对比。此外使用优化后的模型对某市南郊观象台和密云两个站点进行逐日花粉浓度预测。实验结果表明，相比其他预报方法，所提方法能有效提高花粉浓度未来24 h预测精度，并具有较高的泛化能力。     

基于自适应变异混沌粒子群优化和SVM的导弹命中预测模型

针对国内外关于导弹命中预测方面存在的研究深度不足、算法寻优能力不强、模型预测精度不高等缺陷,提出一种基于自适应变异混沌粒子群算法（AMCPSO）和支持向量机（SVM）的导弹命中预测模型。首先,对空战数据进行特征提取,构建模型训练所需样本库;然后,采用改进的AMCPSO算法对SVM中的惩罚因子C和核函数参数g进行寻优,并用优化后的模型对样本进行预测;最后,与经典PSO算法、BP神经网络法、网格法构建的预测模型进行了对比实验。实验结果表明,所提算法的全局寻优能力与局部寻优能力均得到提高,模型预测精度较高,可为导弹命中预测研究提供一定的参考依据。     

基于支持向量机和粒子群算法的软测量建模

针对PX氧化过程中的4-CBA浓度的估计问题,提出了基于支持向量机和粒子群算法来估计机理模型参数的方法．用支持向量机回归来提取特征样本,这些少量的特征样本估计机理模型参数可以减少计算时间,同时避免了人工随机试凑法选择训练样本的盲目性．采用粒子群算法来估计非线性机理模型的参数,可以避免传统方法对初始点和样本的依赖．工业实例表明,本文提出的方法是有效的．     

基于离散粒子群和支持向量机的特征基因选择算法

基因芯片表达谱信息,为识别疾病相关基因及对癌症等疾病分型、诊断及病理学研究提供一新途径。在基因表达谱数据中选择特征基因可以提高疾病诊断、分类的准确率,并降低分类器的复杂度。本文研究了基于离散粒子群(binary particle swarm optimization,BPSO)和支持向量机(support vector machine,SVM)封装模式的BPSO-SVM特征基因选择方法,首先随机产生若干种群(特征子集),然后用BPSO算法优化随机产生的特征基因,并用SVM分类结果指导搜索,最后选出最佳适应度的特征基因子集以训练SVM。结果表明,基于BPSO-SVM的特征基因选择方法,的确是一种行之有效的特征基因选择方法。     

基于粒子群优化模式搜索的支持向量机参数优化及应用

针对核函数参数选择的重要性,提出了粒子群(PSO)模式搜索算法来搜索最优参数,该算法结合了PSO算法的全局搜索能力强和模式搜索的局部收敛性好的优点,使PSO模式搜索算法表现出了较高的性能,并将其应用到农业科技项目分类中。实验结果表明,该算法不仅效率高,收敛速度快,而且搜索到的最优参数达到了较高的准确率。     

基于交叉变异的混合粒子群优化算法

粒子群优化算法是一种基于群体智能理论的全局优化算法,通过群体中粒子间的合作与竞争实现对问题空间的高效搜索。针对算法后期收敛速度较慢、易陷入局部最优的缺点,提出了一种混合粒子群算法。该算法通过改变种群初始化方法,引入交叉和变异机制等措施改善基本粒子群算法的性能。数值试验结果表明,改进型粒子群算法在提高全局寻优能力和加快收敛速度等方面均有良好的表现。     

Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization

Reliability-based design optimization (RBDO) is concerned with designing an engineering system to minimize a cost function subject to the reliability requirement that failure probability should not exceed a threshold. Conventional RBDO methods are less than satisfactory in dealing with discrete design parameters and complex limit state functions (nonlinear and non-differentiable). Methods that are flexible enough to address the concerns above, however, come at a high computational cost. To enhance computational efficiency without sacrificing model flexibility, we propose a new RBDO framework: PS2, which combines Particle Swarm Optimization (PSO), Support Vector Machine (SVM), and Subset Simulation (SS). SS can efficiently estimate small failure probabilities, based on which SVM is adopted to evaluate the reliability of candidate solutions using binary classification. PSO is employed to solve the discrete optimization problem. Primary emphasis is placed upon the cooperation between SVM and PSO. The cooperation is mutually beneficial since the SVM classifier helps PSO evaluate the feasibility of solutions with high efficiency while the optimal solutions obtained by PSO assist in retraining the SVM classifier to attain better accuracy. The PS2 framework is implemented to find the optimal design of a ten-bar truss, whose component sizes are selected from a commercial standard. The reliability constraints are non-differentiable with two failure modes: yield stress and buckling stress. The interactive process between PSO and SVM contributes greatly to the success of the PS2 framework. It is shown that in various trials the PS2 framework consistently outperforms both the double-loop and single-loop approaches in terms of computational efficiency, solution quality, and model flexibility.     

Multi-fault classification based on support vector machine trained by chaos particle swarm optimization

A novel method of training support vector machine (SVM) by using chaos particle swarm optimization (CPSO) is proposed. A multi-fault classification model based on the SVM trained by CPSO is established and applied to the fault diagnosis of rotating machines. The results show that the method of training SVM using CPSO is feasible, the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, the precision and reliability of the fault classification results can meet the requirement of practical application.     

粒子群优化支持向量机的入侵检测算法

为了提高网络入侵的检测正确率,针对网络入侵检测中特征选择问题,将二值粒子群优化算法(BPSO)用于网络入侵特征选择,结合支持向量机(SVM)提出了一种基于BPSO-SVM的网络入侵检测算法。该算法将网络入侵检测转化为多分类问题,采用wrapper特征选择模型,以SVM为分类器,通过样本训练分类器,根据分类结果,利用BPSO算法在特征空间中进行全局搜索,选择最优特征集进行分类。实验结果表明,BPSO-SVM有效降低了特征维数,显著提高了网络入侵的检测正确率,还大大缩短了检测时间。     

基于平均速度的混合自适应粒子群算法

针对传统粒子群寻优速度慢和局部收敛等缺点,提出一种基于平均速度的混合粒子群优化算法.给出了粒子群平均速度的定义,用来表征粒子群的活跃程度,并作为粒子群惯性系数和学习因子调节的依据,加快了粒子群的寻优速度.设计了基于平均速度的切换模拟退火算法和退火温度的更新公式,使得粒子群在保持较快的寻优速度条件下,仍能很容易地跳出局部极小点.对3个典型测试函数的寻优问题进行实验,所得结果表明了该算法的有效性.     

ECG beat classification using particle swarm optimization and support vector machine

In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine (SVM) classifier. The method extracts electrocardiogram’s spectral and three timing interval features. Non-parametric power spectral density (PSD) estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using particle swarm optimization (PSO). These parameters are: Gaussian radial basis function (GRBF) kernel parameter σ and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid particle swarm optimization-support vector machine (SVMPSO) classification method offers significantly improved performance over the SVM which has constant and manually extracted parameter.     

鲶鱼粒子群算法优化支持向量机的短期负荷预测

为了准确、有效地预测短期负荷,提出了一种鲶鱼粒子群算法优化支持向量机的短期负荷预测模型（BFPSO-SVM）。基于混沌理论对短期负荷时间序列进行相空间重构;将支持向量机参数的组合看作一个粒子位置串,通过粒子间互作找到最优支持向量机参数,并引入“鲶鱼效应”,克服基本粒子群算法的缺点;根据最优参数建立短期负荷预测模型,并对模型性能进行仿真测试。仿真结果表明,相对于其他预测模型,BFPSO-SVM不仅加快了支持向量机参数寻优速度,而且提高了短期负荷预测精度,更适用于短期负荷预测的需要。     

基于自适应变异的混沌粒子群优化算法

粒子群优化算法参数少,寻优速度快,但其寻优效率低且在寻优后期易早熟收敛。为改善其寻优性能,在标准粒子群优化算法中,通过引入混沌映射和自适应变异策略,提出具有自适应变异的混沌粒子群优化（ACPSO）算法,以增强种群的全局寻优性能和局部寻优效率。六个基准测试函数的仿真结果表明,ACPSO算法比已有的五个算法具有更好的寻优能力。     

带自适应变异的量子粒子群优化算法

提出了一种带有自适应变异的量子粒子群优化（AMQPSO）算法,利用粒子群的适应度方差和空间位置聚集度来发现粒子群陷入局部寻优时,对当前每个粒子经历过的最好位置进行自适应变异以实现全局寻优。通过对典型函数的测试以及与量子粒子群优化（QPSO）算法和自适应粒子群优化（AMPSO）算法的比较,说明AMQPSO算法增强了全局搜索的性能,优于其他算法。     

Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression

BackgroundShort-term load forecasting is an important issue that has been widely explored and examined with respect to the operation of power systems and commercial transactions in electricity markets. Of the existing forecasting models, support vector regression (SVR) has attracted much attention. While model selection, including feature selection and parameter optimization, plays an important role in short-term load forecasting using SVR, most previous studies have considered feature selection and parameter optimization as two separate tasks, which is detrimental to prediction performance.ObjectiveBy evolving feature selection and parameter optimization simultaneously, the main aims of this study are to make practitioners aware of the benefits of applying unified model selection in STLF using SVR and to provide one solution for model selection in the framework of memetic algorithm (MA).MethodsThis study proposes a comprehensive learning particle swarm optimization (CLPSO)-based memetic algorithm (CLPSO-MA) that evolves feature selection and parameter optimization simultaneously. In the proposed CLPSO-MA algorithm, CLPSO is applied to explore the solution space, while a problem-specific local search is proposed for conducting individual learning, thereby enhancing the exploitation of CLPSO.ResultsCompared with other well-established counterparts, benefits of the proposed unified model selection problem and the proposed CLPSO-MA for model selection are verified using two real-world electricity load datasets, which indicates the SVR equipped with CLPSO-MA can be a promising alternative for short-term load forecasting.     

粗糙集和支持向量机的表具识别算法研究

针对量子粒子群算法具有陷入局部值缺点,提出了一种基于改进量子粒子群算法优化的粗糙集和支持向量机相结合的表具识别算法,引入人工蜂群算法和免疫算法,来提高算法搜索空间、收敛速度。首先通过改进量子粒子群算法优化的粗糙集对得到的特征向量进行属性约简,然后经过改进量子粒子群算法优化支持向量机参数。最后通过实验仿真表明,改进的算法能有效地减少决策属性的个数,提高了粗糙集属性约简能力,优化了支持向量机的参数,算法收敛速度快,识别准确率高。     

Car assembly line fault diagnosis based on triangular fuzzy support vector classifier machine and particle swarm optimization

This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of finite samples and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating the fuzzy theory and v-support vector classifier machine, the triangular fuzzy v-support vector regression machine (TF v-SVCM) is proposed. To seek the optimal parameters of TF v-SVCM, particle swarm optimization (PSO) is also applied to optimize parameters of TF v-SVCM. A diagnosing method based on TF v-SVCM and PSO are put forward. The results of the application in fault system diagnosis confirm the feasibility and the validity of the diagnosing method. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on TF v-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than standard v-SVCM.