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.     

A prior-knowledge input LSSVR metamodeling method with tuning based on cellular particle swarm optimization for engineering design

Engineering design is usually a daunting optimization task which often involving time-consuming, even computation-prohibitive process. To reduce the computational expense, metamodels are commonly used to replace the actual expensive simulations or experiments. In this paper, a new and efficient metamodeling method named prior-knowledge input least square support vector regression (PKI-LSSVR) is developed, in which samples from different levels of fidelity are incorporated to gain an accurate approximation with limited times of the high-fidelity (HF) expensive simulations. The low-fidelity (LF) output serves as a prior-knowledge of the real response function, and then is used as the input variables of least square support vector regression (LSSVR). When the corresponding HF response is gained, a function that maps the LF outputs to HF outputs is constructed via LSSVR. The predictive accuracy of LSSVR models is highly dependent on their learning parameters. Therefore, a novel optimization method, cellular particle swarm optimization (CPSO), is exploited to seek the optimal hyper-parameters for PKI-LSSVR in order to improve its generalization capability. To get a better optimization performance, a new neighborhood function is developed for CPSO where the global and local search is efficiently balanced by adaptively varied neighbor radius. Several numerical experiments and one engineering case verify the efficiency of the proposed PKI-LSSVR method. Sample quality merits including sample sizes and noise, and metamodel performance evaluation measures incorporating accuracy, robustness, and efficiency are considered.     

Optimal training subset in a support vector regression electric load forecasting model

This paper presents an optimal training subset for support vector regression (SVR) under deregulated power, which has a distinct advantage over SVR based on the full training set, since it solves the problem of large sample memory complexity O(N²) and prevents over-fitting during unbalanced data regression. To compute the proposed optimal training subset, an approximation convexity optimization framework is constructed through coupling a penalty term for the size of the optimal training subset to the mean absolute percentage error (MAPE) for the full training set prediction. Furthermore, a special method for finding the approximate solution of the optimization goal function is introduced, which enables us to extract maximum information from the full training set and increases the overall prediction accuracy. The applicability and superiority of the presented algorithm are shown by the half-hourly electric load data (48 data points per day) experiments in New South Wales under three different sample sizes. Especially, the benefit of the developed methods for large data sets is demonstrated by the significantly less CPU running time.     

一种基于LS拟合判别函数的SVR特征选择算法

本文提出一种基于最小二乘（LS）拟合判别函数的SVR特征选择算法（简称15特征选择法）。该算法采用了一个适合支持向量回归（SVR）的新目标函数,并在特征子集选择中根据实验数据集冗余特征较少的特点,采用顺序后向选择算法。仿真实验表明,本方法与常用的降维方法PCA和KPCA相比有更好的效果。     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

用于特征子集选择的异步并行微粒群优化方法

针对大量无关或冗余的特征通常会降低模式分类中分类器性能的问题,提出一种基于异步并行微粒群优化的特征子集选择方法（AP-PSO）.该方法采用二进制微粒群优化搜索特征子集,利用异步并行方式提高算法的运算效率;为有效协调种群的全局探索和局部开发能力,充分利用混沌运动的遍历性和随机性,提出一种一致混沌变异算子.与已知4种特征子集选择方法进行比较,所得结果验证了该算法的有效性.     

Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration

This paper presents a swarm intelligence based parameter optimization of the support vector machine (SVM) for blind image restoration. In this work, SVM is used to solve a regression problem. Support vector regression (SVR) has been utilized to obtain a true mapping of images from the observed noisy blurred images. The parameters of SVR are optimized through particle swarm optimization (PSO) technique. The restoration error function has been utilized as the fitness function for PSO. The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness. The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.     

一种基于PSO的RBF-SVM模型优化新方法

针对使用径向基核函数的支持向量机,采用粒子群优化方法实现模型优化.基于训练集中样本之间的最近平均距离和最远平均距离,给出参数σ的取值空间,从而减小了超参数搜索的范围,并采用对数刻度进一步提高粒子群优化方法的参数搜索效率.与遗传算法和网格法的对比实验表明,所提出的方法收敛速度更快,得出的超参数更优.     

基于BPSO-SVM的网络入侵特征选择和检测

采用改进的二进制粒子群优化进行入侵特征子集选择,粒予群中每个粒子代表。个选择的特征子集,结合支持向世机使用该特征子集所对应的数据集进行分类,正确分类结果作为该粒子的适应度,通过粒子群优化实现最优入侵特征选择。改进的BPSO方沾中通过引入粒于群依概率整体变异来避免陷入局部最优,同时采用粒子禁忌搜索列表来扩大粒子搜索范围和避免重复计算;SVM中采用基于粒度的网格搜索来获得最优核参数。最后用KDD99标准数据集进行实验研究,结果表明该方法能获得满意的检测效果。     

Gene selection using hybrid particle swarm optimization and genetic algorithm

Selecting high discriminative genes from gene expression data has become an important research. Not only can this improve the performance of cancer classification, but it can also cut down the cost of medical diagnoses when a large number of noisy, redundant genes are filtered. In this paper, a hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) method is used for gene selection, and Support Vector Machine (SVM) is adopted as the classifier. The proposed approach is tested on three benchmark gene expression datasets: Leukemia, Colon and breast cancer data. Experimental results show that the proposed method can reduce the dimensionality of the dataset, and confirm the most informative gene subset and improve classification accuracy.     

一种求解混载校车路径的启发式算法

为了避免二进制粒子群算法(BPSO)容易陷入局部极值的缺陷,提出了一种改进的二进制粒子群算法(IBPSO)。该算法在运行过程中引入遗传算法的交叉和变异策略,以便增加种群的多样性,避免粒子的早熟收敛;同时采用免疫算法的疫苗机制,通过合理的疫苗提取、疫苗接种、疫苗选择有效地抑制种群退化的可能。首先采用Wilcoxon秩和检验指标来获得对分类起较大作用的预选特征子集,然后利用IBPSO算法对基因的特征子集和支持向量机(SVM)的参数进行寻优,最后采用IBPSO算法对结肠癌检测问题进行了研究。实验结果表明,该方法可以在较少的特征基因下取得较高精度,且所选的特征基因与结肠癌密切相关,进一步验证了方法的可行性和有效性。     

基于粒子群和支持向量机为青霉素发酵建模

针对支持向量机发酵建模中,选择重要建模参数值的问题,提出利用全局搜索能力较强的粒子群优化算法,优化调整支持向量机建模过程中的重要参数,每一个粒子的位置向量对应一组支持向量机建模的参数。参数不断优化后,得到拟合预测效果较优的模型,预测青霉素发酵过程。仿真结果表明,该方法能使模型的预测效果较好。     

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。     

基于支持向量回归机的股票价格预测

研究股票价格预测问题,股票价格变化具有非线性、时变性,传统线性预测模型难以准确刻画股价变化规律,且非线性神经网络存在过拟合、局部最小等缺陷,预测精度比较低。为提高股票价格预测精度,提出一种基于粒子群优化支持向量机的股票价格预测模型。利用粒子群算法良好的寻优能力,对支持向量机参数进行优化,加快支持向量机学习速度,再采用非线性预测能力优异的支持向量机对股票价格进行预测。以南天信息股票价格对模型性能进行仿真,实验结果证明,支持向量机预测模型能全面反映股票价格变化的非线性的时变规律,获得更高预测精度,预测结果对股民实际操作具有较大的指导价值。     

一种基于多粒子群协同进化的高光谱图像波段选择与分类方法

针对高光谱图像分类过程中数据波段多以及信息冗余量大引起的处理速度慢及Hughes现象等问题,提出了一种基于多粒子协同进化算法进行高光谱图像自动波段选择与分类的方法:使用多粒子群协同进化算法搜索特征子集,对粒子群优化算法进行改进,定义新的位置和速度的更新策略,并以支持向量机为分类器,同时对特征子集和SVM核函数参数进行优化。在协同搜索过程中,引入遗传算法改善粒子群优化的"早熟"收敛问题,构建了一种新的MPSO-SVM(Multiple particle swarm optimization-SVM)分类模型。对高光谱遥感图像的实验结果表明:MPSO-SVM方法不仅能有效地压缩光谱的特征维数,得到最佳的波段组合,还能得到最优的SVM参数,达到较好的分类效果,提高分类精度。     

基于时间序列的模糊支持向量回归

在分析了模糊支持向量回归的基础上,给出了一种基于时间序列分析的模糊支持向量回归方法TS-FSVR,并通过粒子群优化方法对模型中的参数进行了优化。并将该方法成功地应用到城市旅游环境承载力的评估系统中,取得了较好的实验结果。     

融合概率分布和单调性的支持向量回归算法

传统支持向量回归是单纯基于样本数据的输入输出值建模,仅使用样本数据信息,未充分利用其他已知信息,模型泛化能力不强.为了进一步提高其性能,提出一种融合概率分布和单调性先验知识的支持向量回归算法.首先将对偶二次规划问题简化为线性规划问题,在求解时,加入与拉格朗日乘子相关的单调性约束条件;通过粒子群算法优化惩罚参数和核参数,优化目标包括四阶矩估计表示的输出样本概率分布特性.实验结果表明,融合这两部分信息的模型,能使预测值较好地满足训练样本隐含的概率分布特性及已知的单调性,既提高了预测精度,又增加了模型的可解释性.     

基于粒子群优化支持向量机的石油需求预测

在能源问题的研究中,石油需求的准确预测对于我国经济管理部门制定石油生产与进口计划、安排相关行业生产计划以及调整产业结构具有非常重要意义。为了实现石油需求准确预测,采用实时准确算法,提出基于粒子群优化支持向量机(PSO-SVM)的石油需求预测方法,PSO-SVM中采用粒子群优化算法优化SVM参数,以获得较优的SVM预测模型。并以我国1990~2007年石油需求数据进行测试与分析,计算实验结果表明,在石油需求预测中,PSO-SVM比BP有着更高的预测精度,为实际需求提供依据。     

基于遗传算法与粒子群算法的支持向量机参数选择

支持向量机(SVM)参数的选择是评价SVM性能的一个很重要的因素。SVM在解决小样本、非线性等问题中起到的效果是很好的。但是,该方法的缺点是在解决大样本数据集时消耗时间长,且易陷入局部最优解。为了降低SVM在这方面的不足,本文提出了遗传算法和粒子群算法相结合(PSOGA)对参数进行优化求解,并将该算法建立的模型应用到实验中。仿真结果说明该方法避免了陷入局部解,提高了收敛速度并缩短了优化时间,是一个很有效的方法。