时序微阵列数据中的同步和异步共调控基因聚类

基因的共调控可分为同步和异步两种.文中提出了一种新的聚类模型Reg-Cluster,将具有相同编码的同步和异步共调控基因聚集到同一个共调控基因类中.在此基础上,提出了一种有效的聚类算法FBLD,采用先宽度优先、后深度优先的搜索策略,并结合高效的削减规则,挖掘得到所有符合条件的最大Reg-Cluster.聚类结果中包含了详细而完备的共调控信息,有助于基因调控网的研究.算法可扩展用于三维基因-样本-时间微阵列数据集的分析.FBLD算法已经应用到真实和人造微阵列数据集中,其结果被提交到Gene Ontology,实验结果证明了算法的高效性和有效性.     

基于改进GSA的数据聚类机制

数据聚类是大数据分析的基本手段,传统聚类方法易于陷入局部最优.针对这一问题,提出一种基于改进引力搜索机制GSA的数据聚类算法.定义一种适合于引力搜索进化的聚类解编码方式.为了衡量不同聚类解的差异,设计一种基于汉明距离的引力搜索粒子距离度量方法,有效衡量数据对象在各维度属性上的不同.同时,在粒子速度更新方面,引入加速因子...     

一种高维数据聚类遗传算法

聚类分析是数据挖掘中的一个重要研究课题。在许多实际应用中,聚类分析的数据往往具有很高的维度,例如文档数据、基因微阵列等数据可以达到上千维,而在高维数据空间中,数据的分布较为稀疏。受这些因素的影响,许多对低维数据有效的经典聚类算法对高维数据聚类常常失效。针对这类问题,本文提出了一种基于遗传算法的高维数据聚类新方法。该方法利用遗传算法的全局搜索能力对特征空间进行搜索,以找出有效的聚类特征子空间。同时,为了考察特征维在子空间聚类中的特征,本文设计出一种基于特征维对子空间聚类贡献率的适应度函数。人工数据、真实数据的实验结果以及与k-means算法的对比实验证明了该方法的可行性和有效性。     

差异化运动参数的有效挖掘技术仿真

研究运动参数准确挖掘方法.在运动参数挖掘的过程中,由于图像采集时间间隔比较短,短时间内运动幅度的变化非常小,所以很难对运动参数差异性进行准确的描述,无法实现运动参数准确挖掘.提出利用蚁群聚类算法的运动参数挖掘方法.根据约束模糊聚类相关原理,获取运动参数特征聚类目标函数,对特征差异目标进行最大化处理,完成运动参数特征提取.计算残留信息素对于聚类中心的隶属度,设置合理的蚁群目标搜索调整因子,计算聚类中心和对应的偏差,并对偏差进行有效补偿,完成参数挖掘.实验结果表明,利用改进算法进行运动参数挖掘,能够有效提高挖掘的准确性,提高数据管理的效率.     

基于AQPSO的数据聚类

提出了一种新的聚类算法——适应性的基于量子行为的微粒群优化算法的数据聚类(AQPSO)。AQPSO在全局搜索能力和局部搜索能力上优于PSO和QPSO算法,它的适应性方法比较接近于高水平智能群体的社会有机体的学习过程,并且能保证种群不断地进化。聚类过程都是根据数据向量之间的Euclidean(欧几里得的)距离。PSO和QPSO的不同在于聚类中心的进化上。QPSO和AQPSO的不同在于参数的选择上。实验中用到4个数据集比较聚类的效果,结果证明了AQPSO聚类方法优于PSO和QPSO聚类方法。     

基于多样性的多视图低秩稀疏子空间聚类算法

本文主要研究如何通过挖掘多视图特征的多样性信息来促进多视图聚类,提出了基于多样性的多视图低秩稀疏子空间聚类算法。该方法直接将视图多样性概念应用于多视图低秩稀疏子空间聚类算法框架中,确保不同视图的子空间表示矩阵的多样性;为了实现多个视图聚类一致性同时达到提高聚类性能的目标,在该框架中引入谱聚类算法共同优化求解。通过对3个图像数据集的实验验证了该算法的有效性,同时其聚类的性能优于已有的单视图及多视图算法。     

DMGA-FCM:衍生多种群遗传进化的FCM自适应聚类算法

模糊C均值聚类(Fuzzy C-means Clustering, FCM)算法是分析医学数据的重要方法之一，FCM的聚类效果容易受初始聚类中心的影响；诸多研究人员往往采用多种群遗传算法(Multiple Population Genetic Algorithm, MPGA)解决上述问题，但MPGA的全局搜索能力不足并缺少自适应性、易过早收敛、初始聚类中心不佳.为此，本文提出一种DMGA-FCM:衍生多种群遗传进化(DMGA)的FCM自适应聚类算法.在DMGA-FCM中，本文首次提出的衍生算子，对初始化种群进行衍生操作，提升算法寻优能力，处理种群间寻优能力不足；利用模糊控制动态调节遗传概率，以提升算法自适应性，进而增强DMGA算法全局寻优能力，避免过早收敛；用DMGA优化FCM算法的初始聚类中心，以提升算法聚类效果.在仿真实验中，本文将该算法与其他相关FCM算法进行对比，可得到更优的医疗数据聚类效果和图像聚类分割效果.     

数据流聚类算法研究

许多应用程序会产生大量的流数据,如网络流、web点击流、视频流、事件流和语义概念流。数据流挖掘已成为热点问题,其目标是从连续不断的流数据中提取隐藏的知识/模式。聚类作为数据流挖掘领域的一个重要问题,在近期被广泛研究。不同于传统的静态数据聚类问题,数据流聚类面临有限内存、一遍扫描、实时响应和概念漂移等许多约束。本文对数据流挖掘中的各种聚类算法进行了总结。首先介绍了数据流挖掘的约束;随后给出了数据流聚类的一般模型,并描述了其与传统数据聚类之间的关联;最后提出数据流聚类领域中进一步的研究热点和研究方向。     

多值属性的数据处理与关联规则挖掘方法研究 *

讨论了在多值属性关系中进行关联规则挖掘的应用特点,提出利用数据整理和数值编码的方式对关联 规则挖掘算法进行优化。将目标数据属性按其在算法中的作用划分,并分别进行转换和编码;然后对数据先进 行聚类,再在聚类结果中发掘频繁项目集;最后利用聚类后关联规则快速更新算法获取关联规则。算法分析和 实验结果表明,该算法比传统的关联规则挖掘算法更有效率。     

CIS：一种基于迭代扩张的微阵列数据聚类算法

DNA微阵列技术使同时监测成千上万的基因表达水平成为可能.直接把传统聚类算法用于高维基因表达数据分析会受到＂维难＂的困扰.特征转换和特征选择是两种常用的降维方式,但前者产生的新特征难以用原来的领域知识解释,后者通常会丢失信息.另外,传统的聚类算法通常由用户指定聚类参数,参数设置不同对聚类结果有很大的影响.针对上述问题,本文提出了一种新的基于迭代扩张的微阵列数据聚类算法-CIS.它不采用特征转换和特征选择的方式,并自动确定聚类参数.CIS反复用最新得到的样本聚簇得到新的聚类基因,然后以新的基因聚簇为特征重新聚类样本,逐步求精,最终的结果容易解释且避免了信息的丢失.该方法降低了由于用户缺少领域知识引起的实验误差.CIS算法被应用于两个真实的微阵列数据集,实验结果证实了算法的有效性.     

Measuring the convergence and diversity of CDAS Multi-Objective Particle Swarm Optimization Algorithms: A study of many-objective problems

The interest for many-objective optimization has grown due to the limitations of Pareto dominance based Multi-Objective Evolutionary Algorithms when dealing with problems of a high number of objectives. Recently, some many-objective techniques have been proposed to avoid the deterioration of these algorithms' search ability. At the same time, the interest in the use of Particle Swarm Optimization (PSO) algorithms in multi-objective problems also grew. The PSO has been found to be very efficient to solve multi-objective problems (MOPs) and several Multi-Objective Particle Swarm Optimization (MOPSO) algorithms have been proposed. This work presents a study of the behavior of MOPSO algorithms in many-objective problems. The many-objective technique named control of dominance area of solutions (CDAS) is used on two Multi-Objective Particle Swarm Optimization algorithms. An empirical analysis is performed to identify the influence of the CDAS technique on the convergence and diversity of MOPSO algorithms using three different many-objective problems. The experimental results are compared applying quality indicators and statistical tests.     

Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization

Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html.     

Multi-objective K-connected Deployment and Power Assignment in WSNs using a problem-specific constrained evolutionary algorithm based on decomposition

The K-connected Deployment and Power Assignment Problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing the network coverage and lifetime objectives under K-connectivity constraints, in a single run. Recently, it is shown that the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem-specific knowledge for increasing its efficiency. In a constrained Multi-objective Optimization Problem (such as K-connected DPAP), the search space is divided into feasible and infeasible regions. Therefore, problem-specific operators are designed for MOEA/D to direct the search into optimal, feasible regions of the space. Namely, a DPAP-specific population initialization that seeds the initial solutions into promising regions, problem-specific genetic operators (i.e. M-tournament selection, adaptive crossover and mutation) for generating good, feasible solutions and a DPAP-specific Repair Heuristic (RH) that transforms an infeasible solution into a feasible one and maintains the MOEA/D’s efficiency simultaneously. Simulation results have shown the importance of each proposed operator and their interrelation, as well as the superiority of the DPAP-specific MOEA/D against the popular constrained NSGA-II in several WSN instances.     

A Matlab toolbox for designing Multi-Objective Optimal Operations of water reservoir systems

Water reservoir operations have great potential for contributing positively to the development of different socio-economic sectors as well as for reducing the vulnerabilities of water systems caused by changing hydroclimatic and anthropogenic forcing. This motivates the search for advanced, flexible, and open tools supporting the design of operating policies capable of meeting multiple competing objectives. This work contributes the Multi-Objective Optimal Operations (M3O) Matlab toolbox, which allows users to design Pareto optimal (or approximate) operating policies for managing water reservoir systems through several alternative state-of-the-art methods. Version 1.0 of M3O includes Deterministic and Stochastic Dynamic Programming, Implicit Stochastic Optimization, Sampling Stochastic Dynamic Programming, fitted Q-iteration, Evolutionary Multi-Objective Direct Policy Search, and Model Predictive Control. The toolbox is designed to be accessible to practitioners, researchers, and students, and to provide a fully commented and customizable code for more experienced users.     

Case-base maintenance with multi-objective evolutionary algorithms

Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ability of the Case-Base Reasoning system. However, when the case-base has many cases, then performance problems arise due to the time needed to find those similar cases to the input problem. At this point, Case-Base Maintenance algorithms can be used to reduce the number of cases and maintain the accuracy of the Case-Base Reasoning system at the same time. Whereas Case-Base Maintenance algorithms typically use a particular heuristic to remove (or select) cases from the case-base, the resulting maintained case-base relies on the proportion of redundant and noisy cases that are present in the case-base, among other factors. That is, a particular Case-Base Maintenance algorithm is suitable for certain types of case-bases that share some indicators, such as redundancy and noise levels. In the present work, we consider Case-Base Maintenance as a multi-objective optimization problem, which is solved with a Multi-Objective Evolutionary Algorithm. To this end, a fitness function is introduced to measure three different objectives based on the Complexity Profile model. Our hypothesis is that the Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance may be used in a wider set of case-bases, achieving a good balance between the reduction of cases and the problem-solving ability of the Case-Based Reasoning system. Finally, from a set of the experiments, our proposed Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance shows regularly good results with different sets of case-bases with different proportion of redundant and noisy cases.     

EMOEA/D-DE算法在卫星有效载荷配置中的应用

针对卫星有效载荷配置问题,提出了一种基于差分进化分解的改进多目标优化算法(EMOEA/D-DE)的有效载荷配置模型。该模型将配置问题转化为以卫星数、卫星冗余度为目标的多目标优化问题(MOP),并采用EMOEA/D-DE进行求解。此外,针对随机均匀初始化会导致种群在目标空间分布过于集中的问题,采用与优化目标相结合的随机初始化方法进行改进。实验结果表明,该模型所求解集的平均差异性在0.05以内,分布度值在0.9以上,具有较好的稳定性及分布性,且改进后的算法收敛速度提升近1倍,所求解的近似Pareto前沿相对更优。     

Conception of a dominance-based multi-objective local search in the context of classification rule mining in large and imbalanced data sets

Classification on medical data raises several problems such as class imbalance, double meaning of missing data, volumetry or need of highly interpretable results. In this paper a new algorithm is proposed: MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data), a multi-objective local search algorithm that is conceived to deal with these issues all together. It is based on a new modelization as a Pittsburgh multi-objective partial classification rule mining problem, which is described in the first part of this paper. An existing dominance-based multi-objective local search (DMLS) is modified to deal with this modelization. After experimentally tuning the parameters of MOCA-I and determining which version of DMLS algorithm is the most effective, the obtained MOCA-I version is compared to several state-of-the-art classification algorithms. This comparison is realized on 10 small and middle-sized data sets of literature and 2 real data sets; MOCA-I obtains the best results on the 10 data sets and is statistically better than other approaches on the real data sets.     

基于多策略融合的混合多目标蝗虫优化算法

为提高蝗虫优化算法（GOA）求解多目标问题的性能,提出一种基于多策略融合的混合多目标蝗虫优化算法（HMOGOA）。首先,利用Halton序列建立初始种群,保证种群在初始阶段具有均匀分布和较高多样性;然后,通过差分变异算子引导种群变异,促进种群向优势个体移动同时进行更大范围寻优;最后,利用自适应权重因子根据种群优化情况动态调整算法全局搜索和局部寻优能力,提高优化效率及解集质量。选取7个典型函数进行实验测试,并将HMOGOA与多目标蝗虫优化、多目标粒子群（MOPSO）、基于分解的多目标进化（MOEA/D）及非支配排序遗传算法（NSGA Ⅱ）对比分析。实验结果表明,该算法避免了其他四种算法的局部最优问题,明显提高了解集分布均匀性和分布广度,具有更好的收敛精度和稳定性。     

基于多策略融合的混合多目标蝗虫优化算法

为提高蝗虫优化算法（GOA）求解多目标问题的性能，提出一种基于多策略融合的混合多目标蝗虫优化算法（HMOGOA）。首先，利用Halton序列建立初始种群，保证种群在初始阶段具有均匀分布和较高多样性；然后，通过差分变异算子引导种群变异，促进种群向优势个体移动同时进行更大范围寻优；最后，利用自适应权重因子根据种群优化情况动态调整算法全局搜索和局部寻优能力，提高优化效率及解集质量。选取7个典型函数进行实验测试，并将HMOGOA与多目标蝗虫优化、多目标粒子群（MOPSO）、基于分解的多目标进化（MOEA/D）及非支配排序遗传算法（NSGA Ⅱ）对比分析。实验结果表明，该算法避免了其他四种算法的局部最优问题，明显提高了解集分布均匀性和分布广度，具有更好的收敛精度和稳定性。     

基于参考向量的自适应约束多目标进化算法

针对目前用多目标进化算法(MOEA)处理约束多目标优化问题(CMOP)的研究通常以解决单一类型约束为主,而在面对不同种类的复杂约束时算法难以收敛或者种群分布性差的问题,以基于分解的多目标进化算法(MOEA/D)框架为基础,提出一种基于参考向量的自适应约束多目标进化算法(ARVCMOEA).首先将参考向量分成主参考向量及...