应用PSO与GA混合算法重建超分辨图像

结合粒子群优化算法和遗传算法中的交叉与选择操作,提出了一种混合算法,对提出的混合算法用两个具有多个局部极值的函数进行了测试,测试结果表明混合算法寻优能力优于粒子群优化算法;利用该混合算法对低分辨率图像序列重建出一幅高分辨率图像。实验结果表明,该方法重建图像的视觉效果和信噪比均优于遗传算法与梯度下降算子相结合的混合算法重建图像的效果。     

Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis

Medical datasets are often classified by a large number of disease measurements and a relatively small number of patient records. All these measurements (features) are not important or irrelevant/noisy. These features may be especially harmful in the case of relatively small training sets, where this irrelevancy and redundancy is harder to evaluate. On the other hand, this extreme number of features carries the problem of memory usage in order to represent the dataset. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Thus, the learning model receives a concise structure without forfeiting the predictive accuracy built by using only the selected prominent features. Therefore, nowadays, FS is an essential part of knowledge discovery. In this study, new supervised feature selection methods based on hybridization of Particle Swarm Optimization (PSO), PSO based Relative Reduct (PSO-RR) and PSO based Quick Reduct (PSO-QR) are presented for the diseases diagnosis. The experimental result on several standard medical datasets proves the efficiency of the proposed technique as well as enhancements over the existing feature selection techniques.     

A note on the learning automata based algorithms for adaptive parameter selection in PSO

PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. In this paper, we study the ability of learning automata for adaptive PSO parameter selection. We introduced two classes of learning automata based algorithms for adaptive selection of value for inertia weight and acceleration coefficients. In the first class, particles of a swarm use the same parameter values adjusted by learning automata. In the second class, each particle has its own characteristics and sets its parameter values individually. In addition, for both classed of proposed algorithms, two approaches for changing value of the parameters has been applied. In first approach, named adventurous, value of a parameter is selected from a finite set while in the second approach, named conservative, value of a parameter either changes by a fixed amount or remains unchanged. Experimental results show that proposed learning automata based algorithms compared to other schemes such as SPSO, PSOIW, PSO-TVAC, PSOLP, DAPSO, GPSO, and DCPSO have the same or even higher ability to find better solutions. In addition, proposed algorithms converge to stopping criteria for some of the highly multi modal functions significantly faster.     

采用云量子PSO的属性约简方法

针对粒子群优化算法在处理信息系统中属性约简收敛速度慢、早熟的问题,提出了一种结合云模型的量子粒子群优化算法（CQPSO）的属性约简方法。改进量子粒子群优化算法,即利用量子粒子群算法的量子行为来加快收敛速度;引入云模型控制粒子种群在不同状态下进行寻优;根据属性依赖度等性质构造属性约简数学模型;采用CQPSO算法对其进行求解,得到约简结果。实验中采用标准测试函数对CQPSO算法进行仿真对比,验证了CQPSO算法性能优于量子PSO算法;采用UCI标准数据库的典型例子进行属性约简测试,结果表明提出的属性约简方法优于现有约简方法,其计算速度快、识别精度高。     

基于改进PSO算法的MAP图标定点选择新方法

为提高基于MAP图的控制系统驱动效果,并有效减小控制系统内的存储量,提出了一种基于改进粒子群算法的MAP图中标定点择优选取新方法。以液压机械无级变速传动比控制系统中采用的MAP图为例,将其横坐标的两个变量在其定义域内等分,并采用改进粒子群算法选取等分后每段内的坐标点数量和位置。选取过程采用多目标优化原理结合了随机产生100个点的实际值与MAP图线性插值的平均误差以及选定的标定点数量。为提高算法执行效率,对粒子群算法的迭代准则、惯性权重和学习因子进行改进。结果表明,改进后的粒子群算法收敛速度快,寻优精度高,仅需较少的标定数据即可制作控制效果较佳的MAP图。     

Recommendation using a clustering algorithm based on a hybrid features selection method

The cold start problem is a potentiel problem in Recommender Systems (RSs). It concerns the inability of the system to infer recommendaation for new users or new items about wich it has not enough iformation. Specifically, when an item is new, the system may fail to perform well due to the insufficiency of available information for this item. The most common solution addressed in the literature consists in combining the content and collaborative information under a single RS. However these hybrid solutions inherit the classical problems of natural language ambiguity and don’t exploit semantic knowledge in their items representations. In this paper, we propose a hybrid RS composed of three modules to surpass those weaknesses. The first one is rested on a powerful content clustering algorithm; which uses a Hybrid Features Selection Method (HFSM). It combines statistical and semantic relevant features to get the maximum profit from the content of items. The second module is the Collaborative Filtering (CF) one, which depends only on users’ ratings. The third one combines the previous modules to solve the problem of missing values in CF approach and to handle new-item issue. The proposed hybrid Recommender is evaluated against traditional item-based CF in different settings: no cold-start situation and a simulation of a new-item scenario (an item with few/ no ratings). The conducted experiments show the ability of the proposed hybrid recommender to deliver more accurate predictions for any item and its outperformance on the classical CF approach, which fails in cold-start situations.     

Rosenbrock 搜索与动态惯性权重粒子群混合优化算法

为了提高复杂优化问题的优化精度和鲁棒性能,提出两种将Rosenbrock搜索与动态惯性权重粒子群（DIPSO）相结合的混合算法,即＂协同＂与＂接力＂混合算法.两种算法充分利用了Rosenbrock搜索算法强大的局部搜索能力和DIPSO算法的全局寻优能力,很好地平衡了算法的全局＂探索＂与局部＂开发＂.通过4个典型基准函数的实验研究,表明了所提出的算法具有优化精度高、鲁棒性强等特点,适合于对高维多峰函数进行优化.     

基于改进PSO方法的保障性住房选址策略模型

保障性住房选址是目前城市规划中的一个重要问题。将保障性住房选址抽象为最优化问题并建立相应数学模型,基于粒子群优化算法提出有限最优值法的改进PSO方法,以克服可行解空间离散的问题。实验结果表明,改进的PSO方法适合于保障性住房选址问题的数学模型,能够正确求解该数学模型意义下的最优选址点。     

基于混合粒子群算法的保障资源优化研究

针对目前我军在武器保障过程中人力资源的过载问题,提出了应用混合粒子群算法求解资源约束项目调度问题的实现方法.分析了网络计划中工序逻辑关系特点,采用工期指标建立优化模型.在算法设计中,使用遗传算法的交叉和变异操作替代粒子速度和位置的更新,并采用修复算子,以保证个体生成的合法性.对某型武器装备保障进行了优化分析,结果表明方法具有很强的寻优能力,对于促进保障单位合理利用资源、科学安排工程调度具有重要的现实意义.     

基于自然选择的线性递减权重PSO与Taylor算法的TDOA协同定位算法研究

针对Taylor算法进行TDOA定位时,其初始估计位置的误差易导致Taylor算法不收敛和定位精度差的问题,提出一种基于自然选择的线性递减权重粒子群优化（W-SPSO）与Taylor算法协同定位的方法。该方法先通过W-SPSO算法得到一个初始估计位置（x,y）,再通过Taylor算法在（x,y）处进行迭代运算得到最终定位结果。不同噪声情况下的仿真结果显示：W-SPSO与Taylor算法协同定位方法对MS坐标估计值的均方差（RMSE）小于标准PSO（粒子群优化）、SelPSO（基于自然选择的粒子群优化算法）、W-SPSO、Taylor以及Chan五种算法的RMSE。因此,所提出的定位方法在保留了SelPSO算法求解精度和收敛性的基础上,同时提高了全局搜索能力,使其具有更高的定位精度和收敛性。     

基于词典和遗传算法的文本特征获取方法

Web文本特征获取是Web挖掘中重要而关键的前提工作,传统文本特征获取方法由于在确定文本词条的权重方面做得不够准确,从而直接影响了文本分类算法的精确度.为此,提出一种基于主题词典和遗传算法的文本特征获取方法(dic.tionary and GA-based feature selection algorithms,DGFSA),利用主题词典来调整词条权重,从而获取文本特征向量.实验结果表明,DGFSA比传统算法在文本分类的准确率和特征词的约简率方面分别提高了28.4%和16.3%.     

一种基于决策矩阵的属性约简及规则提取算法

研究了Rough集理论中属性约简和值约简问题,扩展了决策矩阵的定义,提出了一种基于决策矩阵的完备属性约简算法,该算法利用决策属性把论域划分成多个等价类,然后利用每个等价类对应的决策矩阵计算属性约简。与区分矩阵相比,采用决策矩阵可以有效地减少存储空间,提高约简算法效率。同时,借助决策矩阵进行值约简,提出了一种新的规则提取算法,使最终得到的决策规则更加简洁。实验结果表明,本文提出的属性约简和值约简算法是正确、有效、可行的。     

Applying a selection method to choose Quality Attribute Techniques

ContextSoftware products have requirements on software quality attributes such as safety and performance. Development teams use various specific techniques to achieve these quality requirements. We call these “Quality Attribute Techniques” (QATs). QATs are used to identify, analyse and control potential product quality problems. Although QATs are widely used in practice, there is no systematic approach to represent, select, and integrate them in existing approaches to software process modelling and tailoring.ObjectiveThis research aims to provide a systematic approach to better select and integrate QATs into tailored software process models for projects that develop products with specific product quality requirements.MethodA selection method is developed to support the choice of appropriate techniques for any quality attribute, across the lifecycle. The selection method is based on three perspectives: (1) risk management; (2) process integration; and (3) cost/benefit using Analytic Hierarchy Process (AHP). An industry case study is used to validate the feasibility and effectiveness of applying the selection method.ResultsThe case study demonstrates that the selection method provides a more methodological and effective approach to choose QATs for projects that target a specific quality attribute, compared to the ad hoc selection performed by development teams.ConclusionThe proposed selection method can be used to systematically choose QATs for projects to target specific product qualities throughout the software development lifecycle.     

基于混合克隆量子遗传策略的文本特征选择方法*

引入向量约简率和分类准确率的度量标准,采用量子比特对遗传算法进行编码,结合克隆算子,提出一种基于混合克隆量子遗传策略的文本特征选择方法。实验结果显示,该方法能有效地降低文本特征向量的维度,所提取的特征向量子集能有效提高文本分类的精度。     

基于PSO的LS-SVM特征选择与参数优化算法

针对最小二乘支持向量机特征选择及参数优化问题,提出了一种基于PSO的LS-SVM特征选择与参数同步优化算法。首先产生若干种群（特征子集）,然后用PSO算法对特征及参数进行优化。在UCI标准数据集上进行的仿真实验表明,该算法可有效地找出合适的特征子集及LS-SVM参数,且与基于遗传算法的最小二乘支持向量机算法（GALS-SVM）和传统的LS-SVM算法相比具有较好的分类效果。     

A new hybrid PSO algorithm based on a stochastic Markov chain model

Based on the recent research concerning the PageRank Algorithm used in the famous search engine Google [1], a new Inverse-PageRank-Particle Swarm Optimizer (I-PR-PSO) is presented in order to improve the performances of classic PSO. The resulted algorithm uses a stochastic Markov chain model to define an intelligent topological structure of the swarm’s population, in which the better particles have an important influence on the others. In the presented experiments, calculations on some benchmark functions classically used to test optimization methods are performed, and the results are compared to different versions of the standard PSO, that is using different topological structures of the population. The experimental results show that I-PR-PSO can converge quicker on the tested functions, and can find better results in the solution domain than its tested peers.     

基于一种改进粒子群算法的SVM参数选取

支持向量机作为一个新兴的数学建模工具已经被广泛地应用到很多工业控制领域中,其良好的泛化能力和预测精度在很大程度上受到其参数选取的影响.根据智能群体进化模式改进粒子群优化算法.利用模糊C均值聚类算法分类粒子群体,并用子群体最优点取代速度更新公式中的个体历史最优点,并利用该算法搜索支持向量机的最优参数组合.对比仿真实验表明:所提优化算法是支持向量机参数选取的有效算法,在非线性函数估计中体现出优良的性能.     

遗传算法与区分矩阵的属性约简算法

对于约简来说,其前提是保证知识库分类能力不变,由此引入弱约简的定义。利用区分矩阵能很容易计算出弱约简和遗传算法可以在全局寻优的优势,将染色体对区分函数的覆盖度作为适应度函数的参数,提出了一种基于遗传算法和区分矩阵的属性约简算法。算法中从粒计算的角度,重新度量粒度,对基于划分和覆盖的粗糙集决策表进行了研究。用k近邻算法通过准确率对弱约简效果进行评估。通过UCI数据集证明了该算法的有效性。该算法的时间复杂度是多项式的。     

A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms

One of the simple techniques for Data Clustering is based on Fuzzy C-means (FCM) clustering which describes the belongingness of each data to a cluster by a fuzzy membership function instead of a crisp value. However, the results of fuzzy clustering depend highly on the initial state selection and there is also a high risk for getting the best results when the datasets are large. In this paper, we present a hybrid algorithm based on FCM and modified stem cells algorithms, we called it SC-FCM algorithm, for optimum clustering of a dataset into K clusters. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained by K-means algorithm, FCM, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) Algorithm demonstrate the better performance of the new algorithm.     

基于PSO的多约束QoS网格资源选择模型

现有的网格资源选择算法中,只考虑到资源的可利用率,忽略了网络因素的影响,为此提出了一种基于粒子群优化算法的、带网络QoS约束的三层资源选择模型,并对该模型的算法进行了设计.该模型综合考虑了资源利用率和网络因素对网格资源选择的影响,过滤掉一些资源利用率很高但网络通信能力很低,甚至网络无法连通的结点,减轻了资源调度的负担.给出了一个仿真实例,以说明该模型和算法的有效性.