针对选址问题的一种遗传算法改进探究

选址问题是现代地理信息资源配置的重要研究领域之一,通用性强、鲁棒性高的遗传算法可以较好地解决这类问题。常用方法是使用二进制编码的遗传算法对栅格数据地图进行选址。为克服二进制编码的标准遗传算法在解决选址问题过程中易陷入早熟的缺点,在研究了使用不同算子、引入观测概念这两大类解决标准遗传算法陷入早熟问题的方法后,针对选址问题的特点,选择了引入多样性测度与应用小生境技术对遗传算法进行改进,并深入探究了引入多样性测度与应用小生境技术后,遗传算法解决选址问题的过程中准确性、在线性能函数、离线性能函数的改善;接着提出了进一步改进小生境技术的方法,使得遗传群体中的每一个个体都参与遗传操作,并且避免了两个相同的个体参与交叉操作的情况。最后通过地图选址实验,将改进的小生境遗传算法与多样性测度结合,成功提高了遗传算法的性能。     

An improved weighted recursive PCA algorithm for adaptive fault detection

A novel weighted adaptive recursive fault detection technique based on Principal Component Analysis (PCA) is proposed to address the issue of the increment in false alarm rate in process monitoring schemes due to the natural, slow and normal process changes (aging), which often occurs in real processes. It has been named as weighted adaptive recursive PCA (WARP).The aforementioned problem is addressed recursively by updating the eigenstructure (eigenvalues and eigenvectors) of the statistical detection model when the false alarm rate increases given the awareness of non-faulty condition. The update is carried out by incorporating the new available information within a specific online process dataset, instead of keeping a fixed statistical model such as conventional PCA does. To achieve this recursive updating, equations for means, standard deviations, covariance matrix, eigenvalues and eigenvectors are developed. The statistical thresholds and the number of principal components are updated as well.A comparison between the proposed algorithm and other recursive PCA-based algorithms is carried out in terms of false alarm rate, misdetection rate, detection delay and its computational complexity. WARP features a significant reduction of the computational complexity while maintaining a similar performance on false alarm rate, misdetection rate and detection delay compared to that of the other existing PCA-based recursive algorithms. The computational complexity is assessed in terms of the Floating Operation Points (FLOPs) needed to carry out the update.     

改进选择策略的烟花算法

烟花算法(FWA)中的选择策略直接影响其收敛效率、收敛精度、对初值敏感性以及能否跳出局部最优,对此,提出一种改进选择策略的烟花算法(ISSFWA). ISSFWA建立峰值火花和探索火花的概念,并提出基于N-1朵峰值火花和一朵探索火花充当下一代N朵烟花的选择策略.峰值火花兼顾了火花的适应度值及相对位置,保证选择全局最优火花及峰值火花邻域内的局部最优火花,同时避免重复选择搜索能力相似的火花,而基于最远距离的探索火花可以增强全局探索能力.在10次标准及增加位置偏移的测试函数实验中, ISSFWA在最优适应度值方面优于PSO、GA、FWA;在平均适应度值方面优于PSO和FWA,略劣于GA.这一结果表明, ISSFWA能够增强寻找最优解的能力,降低对初值的敏感性,并提升搜索效率.     

An improved boosting based on feature selection for corporate bankruptcy prediction

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability in corporate bankruptcy prediction. In this paper, a new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Experimental results reveal that FS-Boosting could be used as an alternative method for the corporate bankruptcy prediction.     

基于免疫文化算法的故障特征选择方法

提出了一种新的文化算法,基于免疫克隆选择原理改进了文化算法的种群空间,同时设计了一种新的历史知识及其影响函数。为了去除工业中故障诊断过程中的冗余变量,实现数据降维,提高故障诊断性能,将该免疫文化算法应用到故障特征选择当中,提出了一种封装式的特征选择方法。该方法利用抗体种群进行全局搜索,通过文化算法的信念空间保留历代最优个体,并对UCI数据集的高维数据进行特征子集选择。将该方法应用到TE过程故障诊断中,结果表明,相比于直接使用高维数据进行故障诊断,该算法有效降低了特征空间的维数,提高了分类精度。     

克隆代码有害性预测中的特征选择模型

为解决克隆代码有害性预测过程中特征无关与特征冗余的问题,提出一种基于相关程度和影响程度的克隆代码有害性特征选择组合模型。首先,利用信息增益率对特征数据进行相关性的初步排序;然后,保留相关性排名较高的特征并去除其他无关特征,减小特征的搜索空间;接着,采用基于朴素贝叶斯等六种分类器分别与封装型序列浮动前向选择算法结合来确定最优特征子集。最后对不同的特征选择方法进行对比分析,将各种方法在不同选择准则上的优势加以利用,对特征数据进行分析、筛选和优化。实验结果表明,与未进行特征选择之前对比发现有害性预测准确率提高15.2~34个百分点以上;与其他特征选择方法比较,该方法在F1测度上提高1.1~10.1个百分点,在AUC指标上提升达到0.7~22.1个百分点,能极大地提高有害性预测模型的准确度。     

基于EFDR编码压缩的非确定位填充算法

针对EFDR编码算法中非确定位填充算法的不足,提出了一种基于EFDR编码压缩算法的非确定位填充算法（ESA）。该算法在填充测试数据中的非确定位时,依据EFDR编码算法的特点,考虑非确定位两边确定位的特征以及非确定位游程自身的特点,对非确定位采用全0填充、全1填充和分块填充三种方法,从而提高了EFDR编码压缩算法的压缩效率并减少了测试时间,同时由于算法仅对测试数据的非确定位进行操作,不会增加测试的物理开销。实验结果表明,在不增加测试功耗和测试硬件开销的情况下,实现了EFDR编码压缩算法压缩效率的提高和测试时间的减少。     

一种改进的锂离子电池剩余寿命预测算法

锂离子电池故障往往会使系统性能下降甚至瘫痪,故障部件剩余寿命的精确估计对整个系统的寿命预测和健康管理至关重要。粒子滤波是一种有效的序列信号处理方法,然而应用于锂离子电池剩余寿命预测准确性并不高。根据锂离子电池电学特性,提出一种改进的粒子滤波算法,基于锂离子电池容量退化指数模型,结合训练数据对锂离子电池剩余寿命进行预测。仿真及实验结果表明,改进的粒子滤波算法对锂离子电池剩余寿命预测误差小于5%。     

An improved,randomized algorithm for parallel selection with an experimental study

A common statistical problem is that of finding the median element in a set of data. This paper presents an efficient randomized high-level parallel algorithm for finding the median given a set of elements distributed across a parallel machine. In fact, our algorithm solves the general selection problem that requires the determination of the element of rank k, for an arbitrarily given integer k.Our general framework is an SPMD distributed-memory programming model that is enhanced by a set of communication primitives. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The algorithms have been coded in the message-passing standard MPI, and our experimental results from the IBM SP-2 illustrate the scalability and efficiency of our algorithm and improve upon all the related experimental results known to the author.The main contributions of this paper are(1) New techniques for speeding the performance of certain randomized algorithms, such as selection, which are efficient with likely probability.(2) A new, practical randomized selection algorithm (UltraFast) with significantly improved convergence.     

An improved binary sparrow search algorithm for feature selection in data classification

Neural Computing and Applications - Feature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially...     

An improved scheme for minimum cross entropy threshold selection based on genetic algorithm

Image segmentation is one of the most critical tasks in image analysis. Thresholding is definitely one of the most popular segmentation approaches. Among thresholding methods, minimum cross entropy thresholding (MCET) has been widely adopted for its simplicity and the measurement accuracy of the threshold. Although MCET is efficient in the case of bilevel thresholding, it encounters expensive computation when involving multilevel thresholding for exhaustive search on multiple thresholds. In this paper, an improved scheme based on genetic algorithm is presented for fastening threshold selection in multilevel MCET. This scheme uses a recursive programming technique to reduce computational complexity of objective function in multilevel MCET. Then, a genetic algorithm is proposed to search several near-optimal multilevel thresholds. Empirically, the multiple thresholds obtained by our scheme are very close to the optimal ones via exhaustive search. The proposed method was evaluated on various types of images, and the experimental results show the efficiency and the feasibility of the proposed method on the real images.     

快速的CCSDS压缩方法的编码选项选择算法

为提升“空间数据系统咨询委员会”( CCSDS)提出的通用无损压缩编码方法的时间效率和压缩性能,提出一种新的编码选项选择算法.该算法利先用块内J个数据的和,粗略搜索最优编码选项参数的范围,再从该范围内精确查找.证明:最佳的搜索范围为3J/2,算法的时问复杂度为O(K+J).相对于CCSDS图像压缩标准中的快速编码选择方法,新算法可以使压缩性能最优;相比启发式的编码选择算法,该算法执行时间缩短了15％ ～25％.     

On the dynamic evidential reasoning algorithm for fault prediction

In this paper, a new fault prediction model is presented to deal with the fault prediction problems in the presence of both quantitative and qualitative data based on the dynamic evidential reasoning (DER) approach. In engineering practice, system performance is constantly changed with time. As such, there is a need to develop a supporting mechanism that can be used to conduct dynamic fusion with time, and establish a prediction model to trace and predict system performance. In this paper, a DER approach is first developed to realize dynamic fusion. The new approach takes account of time effect by introducing belief decaying factor, which reflects the nature that evidence credibility is decreasing over time. Theoretically, it is show that the new DER aggregation schemes also satisfy the synthesis theorems. Then a fault prediction model based on the DER approach is established and several optimization models are developed for locally training the DER prediction model. The main feature of these optimization models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune the DER prediction model whose initial parameters are decided by expert’s knowledge or common sense. Finally, two numerical examples are provided to illustrate the detailed implementation procedures of the proposed approach and demonstrate its potential applications in fault prediction.     

一种基于抗原软子空间聚类的否定选择算法*

否定选择算法（NSA）是免疫检测器生成的重要算法,传统否定选择算法在亲和力计算过程中未考虑不同种类抗原关键特征与冗余特征之间的差异性,存在算法检测性能较低的问题。对此,提出了一种基于抗原软子空间聚类的否定选择算法（ASSC-NSA）。该算法首先利用抗原软子空间聚类计算出不同种类抗原的各个关键特征及其权值,随后通过这些关键特征引导检测器生成以有效地减少冗余特征的影响,从而提高算法检测性能。实验结果表明,在BCW与KDDCup数据集上,相对于经典的否定选择算法,ASSC-NSA能在误报率无明显变化的情况下显著地提高检测率。     

一种改进选择算子的遗传算法

遗传算法（Genetic Algorithm,GA）是一种模拟生物进化的智能算法,被广泛应用于求解各类问题。简单遗传算法（Simple GA）仅靠变异产生新的数值,常常存在搜索精确度不高的问题。针对这个问题,对SGA的选择算子进行改进,即把相似个体分在同一组中,以组为单位进行选择,并通过该组个体的特点进行高斯搜索生成新的群体。这样使得GA在搜索过程中不仅可以很好地保持个体的多样性,并且可以提高解的精确度。通过对11个函数（单峰和多峰）的仿真实验,证明了采用新的选择算子后,GA在求解问题的精确度上有了很大地改善。     

Motor fault diagnosis using negative selection algorithm

In this paper, we propose a novel multi-level negative selection algorithm (NSA)-based motor fault diagnosis scheme. The hierarchical fault diagnosis approach takes advantage of the feature signals of the healthy motors so as to generate the NSA detectors and further uses the analysis of the activated detectors for fault diagnosis. It can not only efficiently detect incipient motor faults, but also correctly identify the corresponding fault types. The applicability of our motor fault diagnosis method is examined using two real-world problems in computer simulations.     

一种配电网故障区间定位改进算法的研究

当前配电网故障定位的研究主要关注遗传算法等智能算法。本文着眼于常规遗传算法在局部收敛、寻优速度等方面存在的缺陷问题,提出了一种改进方法。该方法以遗传算法全局寻优能力为基础,通过引入自适应调整系数对参数进行优化编码,对种群多样性水平进行综合考量,在保持高收敛性和寻优能力的基础上,增强了全局寻优能力,有效规避了局部最优解,同时在变异操作过程中进行个体优选,提高了算法的寻优速度,减少了迭代次数,显著提升了寻优效率。通过最后通过对一个20节点的配电网络进行了故障定位实验仿真,证明了该方法该算法在信息完整与信息畸变的情形下都能完成准确的故障定位,并且寻优效果显著优于传统算法,具备很好的有效性与优越性。     

一种图像置乱的改进算法

提出了一种改进的基于混沌的图像置乱算法.该算法在对图像运用抽样技术预处理的基础上,利用Logistic映射产生的伪随机序列,对图像进行灰度值的变换和全局的块置乱.实验仿真表明,该加密算法具有良好的加密效果,能有效地抵御统计攻击和差分攻击.     

一种应用于故障诊断中的高效推理算法

针对经典联合树推理算法的信息传播共享和推理时间等问题, 提出一种高效联合树推理算法. 该算法基于获得的证据信息和查询节点对原始的网络结构化简, 然后在化简后的网络结构上进行联合树推理. 在信息传递过程中, 该算法可以实现不同证据下的信息共享. 经仿真验证, 高效联合树算法能够在保证准确率的同时, 以更短的时间作出诊断推理. 基于现场收集的数据, 建立水泥回转窑故障诊断系统模型并应用改进的算法实现了精准且快的故障诊断.