New judging model of fuzzy cluster optimal dividing based on rough sets theory

To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory： information system discrete algorithm （algorithm 1） and samples representatives judging algorithm （algorithm 2）. On the principle of the farthest distance, algorithm 1 transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.

基于Rough Sets理论的保障房项目后评价指标体系优化

保障性住房项目是社会保障体系的重要组成部分,关乎民生与社会和谐。首先对保障房项目后评价的主要内容和特点进行分析,采用改进型德尔菲法与HHM法,从项目目标、执行过程、项目影响、项目效益、项目可持续性等五个维度对保障房项目的后评价指标进行识别与筛选。而后通过基于知识粒度与属性重要度的Rough Sets(粗糙集)理论对所有指标的重要度、指标权重、偏离程度进行计算,剔除与约简了偏离程度较大的冗余指标,建立起了更加科学合理的符合保障房项目固有特点的后评价指标体系,以期为以后的保障房项目后评价管理以及全寿命周期管理提供研究基础。

Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm

In this paper we present a new optimization algorithm, and the proposed algorithm operates in two phases. In the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept e-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.

QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is needed.Opti-mizing the plethora of cloud services has thus become a top priority.Cloud ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some users.To resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification mechanisms.Untrusted data are assessed by an information correction method.The weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service quality.The manufacturing cloud service optimization algorithm thus provides a quantitative reference for service selection.In experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

Engineering Design Optimization Based on Intelligent Response Surface Methodology

An intelligent response surface methodology (IRSM) was proposed to achieve the most competitive metal forming products, in which artificial intelligence technologies are introduced into the optimization process. It is used as simple and inexpensive replacement for computationally expensive simulation model. In IRSM, the optimal design space can be reduced greatly without any prior information about function distribution. Also, by identifying the approximation error region, new design points can be supplemented correspondingly to improve the response surface model effectively. The procedure is iterated until the accuracy reaches the desired threshold value. Thus, the global optimization can be performed based on this substitute model. Finally, we present an optimization design example about roll forming of a "U" channel product.

Web-Based Synthetic Optimization Design System of Micro-Components

In order to meet the requirement of network synthesis optimization design for a micro component, a three-level information frame and functional module based on web was proposed. Firstly, the finite element method (FEM) was used to analyze the dynamic property of coupled-energy-domain of virtual prototype instances and to obtain some optimal information data. Secondly, the rough set theory (RST) and the genetic algorithm (GA) were used to work out the reduction of attributes and the acquisition of principle of optimality and to confirm key variable and restriction condition in the synthesis optimization design. Finally, the regression analysis (RA) and GA were used to establish the synthesis optimization design model and carry on the optimization design. A corresponding prototype system was also developed and the synthesis optimization design of a thermal actuated micro-pump was carried out as a demonstration in this paper.

基于粗糙集理论与PCA-APSO-SVM的沥青路面使用性能预测

针对传统沥青路面使用性能预测精度较低的问题,建立了基于粗糙集理论(rough set,RS)与主成分分析法(principal compoent analysis,PCA)-自适应粒子群算法(adaptive particle swarm optimization,APSO)-支持向量机(support vector machine,SVM)的沥青路面使用性能预测模型。基于沥青路面的时序指标与影响因素指标,建立了11个初始预测指标(包括前3年的路面使用性能、当量轴次、路龄、养护性质、坑槽率、修补率、年降水量、平均气温、日照时数);通过RS属性约减筛选出9个核心指标;利用PCA提取4个主成分,得到了基于4个主成分的数据集;将APSO引入到SVM中,对数据集进行训练,并优化了SVM模型参数;建立了路面使用性能的PCA-APSO-SVM预测模型,并以G6京藏高速甘肃境内某段道路为例,对路面使用性能进行预测。研究结果表明:PCA-APSO-SVM模型预测精度较PCA-PSO-SVM、APSO-SVM、PSO-SVM有较大提高,预测结果与实际情况更加符合,能为路面养护决策提供相关参考。

Cooperative extended rough attribute reduction algorithm based on improved PSO

Particle swarm optimization （PSO） is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduc- tion of decision tables is a non-derterministic polynomial （NP）-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness func- tions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine （UCI） data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.

Simulated annealing algorithm for the optimal translation sequence of the jth agent in rough communication

In rough communication, because each agent has a different language and cannot provide precise communication to each other, the concept translated among multi-agents will loss some information and this results in a less or rougher concept. With different translation sequences, the problem of information loss is varied. To get the translation sequence, in which the jth agent taking part in rough communication gets maximum information, a simulated annealing algorithm is used. Analysis and simulation of this algorithm demonstrate its effectiveness.

云制造模式下采用Rough-ANP的机械设计知识优选推送策略

合理应用机械设计知识可以辅助提升创新设计的效率和质量。本文立足于云模式下的机械设计过程,提出基于行为-结构-知识的机械设计知识解构模型;提出一种云制造模式下基于RoughANP的机械设计知识优选推送策略,该方法充分结合了粗糙集理论(Rough set theory)在处理模糊性和不确定性方面的优势以及网络层次分析法(ANP)在处理多目标评估问题的优势。最后以电动铲运机设计为案例,验证了该机械设计知识优选推送策略的有效性。

基于Rough集的战时车辆野战维修装备优化配备方法研究

介绍了Rough集理论的属性约简方法和规则提取方法,分析了现代战争条件下战时车辆野战维修装备的优化配备;结合实例给出了不同条件下的优化配备方案,并对战时车辆野战维修装备的配备决策进行了优化。

基于信息最大覆盖率蚁群算法的Rough集属性优化约简

属性约简是一个NP难问题,这种算法不仅复杂而且非常耗时。因此本文提出了一种基于等价划分的信息一致性的属性约简算法,降低了属性约简计算复杂度,并将信息最大覆盖率和属性重要性作为蚁群优化算法的信息素进行添加式属性约简,既减小了属性约简的空间复杂度,也提高了计算效率。最后通过一个具体的例子,证明了此算法的有效性和可行性。

广义多尺度决策表最优尺度选择的快速算法

广义多尺度决策表的条件属性和决策属性都具有多个尺度.最优尺度能够将较粗的条件属性与较细的决策属性相结合,达到效率与精度的平衡.然而现有的最优尺度选择算法计算效率较低.为此,提出一种最优尺度选择的快速算法.首先探讨最优尺度的一些性质和边界域的变化情况,给出判断边界域是否相等的条件以及最优尺度的等价定义;然后一种快速算法.最后通过数值实验表明其相较于现有算法速度更快,能有效解决最优尺度选择问题,该算法在计算效率方面取得了显著的提升,实现了在较短的时间内得出最佳结果的目标.

利用Rough集理论优化日光温室室内温度数据

以日光温室为试验研究对象,基于Rough集理论对日光温室室内温度试验数据的完备化推理分析,补齐遗漏属性值,为日光温室结构优化设计提供准确完整的温室室内温度属性数据库,提出了一种基于试验数据本身信息的日光温室结构优化设计的新方法。

基于粗糙集最优解山区公路边坡防护效果评价

为系统有效评估不同边坡防护技术的护坡效果,通过调查大量护坡技术对边坡沿线生态环境、边坡特性等的影响,以粗糙集理论和条件信息熵为基础,分析护坡效果与稳定性、抗侵蚀性等因素的相互关系,结合最优解优化评价指标重要度,提出了一种适用于山区公路边坡防护效果的指标权重确定方法。构建了边坡防护效果评价体系。运用模糊理论和模糊综合评价理论,建立了山区公路边坡防护效果模糊综合评价方法。依托某山区实体边坡对常见的11种护坡技术分析得到:生态混凝土护坡技术和土工网植草防护技术的护坡效果最优。对生态混凝土和土工网植草组合防护技术进行仿真计算,得到边坡水平最大位移和最大剪应变分别降低66.21%和90.17%,边坡稳定性系数从1.252提高到1.351,符合规范要求,为山区公路边坡防护技术的优选提供了新的思路。

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted;consistent with the advancements in computing and growing the use of social media.High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features.Furthermore,most reviews from social media carry a lot of noise and irrelevant information.Therefore,this study proposes a new text-feature selection method that uses a combination of rough set theory(RST)and teaching-learning based optimization(TLBO),which is known as RSTLBO.The framework to develop the proposed RSTLBO includes numerous stages:(1)acquiring the standard datasets(user reviews of six major U.S.airlines)which are used to validate search result feature selection methods,(2)preprocessing of the dataset using text processing methods.This involves applying text processing methods from natural language processing techniques,combined with linguistic processing techniques to produce high classification results,(3)employing the RSTLBO method,and(4)using the selected features from the previous process for sentiment classification using the Support Vector Machine(SVM)technique.Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing.More importantly,the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.

融合粗糙数据推理的离散麻雀搜索算法求解HFSP问题

针对麻雀搜索算法(SSA)易陷入局部最优、无法求解离散优化问题等不足,提出了一种改进离散麻雀搜索算法(IDSSA)。抽象原始麻雀搜索算法的位置更新公式,针对个体的不同身份设计新的离散化启发式位置更新策略,并针对混合流水车间调度问题(HFSP)设计了编码与解码方式;引入粗糙数据推理理论,通过数学证明解释了引入理论的可行性与合理性,为算法提供理论支撑,提高可解释性;利用上近似的性质扩大搜索空间,提高种群多样性,避免算法早熟,结合划分及粗糙数据推理提出3种策略,促进种群间信息共享,调节种群的开发能力与探索能力,降低算法陷入局部最优的概率;使用改进离散麻雀搜索算法求解混合流水车间调度问题,对3个小规模实例与10个Liao经典测试集进行仿真实验,验证了改进离散麻雀搜索算法求解混合流水车间调度问题的可行性,通过与遗传算法、差分进化算法等经典算法的对比实验,证明了所提算法的优越性与改进策略的有效性。

Genetic algorithm for λ-optimal translation sequence of rough communication

In rough communication, because each agent has a different language and can not provide precise communication to each other, the concept translated among multi-agents will loss some information, and this results in a less or rougher concept. With different translation sequences the amount of the missed knowledge is varied. The λ-optimal translation sequence of rough communication, which concerns both every agent and the last agent taking part in rough communication to get information as much as he （or she） can, is given. In order to get the λ-optimal translation sequence, a genetic algorithm is used. Analysis and simulation of the algorithm demonstrate the effectiveness of the approach.

基于三重约束社会蜘蛛优化的最小属性约简算法

针对社会蜘蛛优化算法求解最小属性约简时收敛速度慢和约简结果差的问题,提出一种基于三重约束的社会蜘蛛优化最小属性约简算法(TRSSOAR),分别对初始化阶段、迭代过程中以及迭代结束后种群中的个体进行约束。首先,提出一种适应度投票策略优化种群初始状态,使种群中的多数个体处于良好的位置;然后,在迭代过程中引入对立学习,设计局部对立学习策略提升种群个体质量,扩大搜索空间;接下来为了获得较少的约简结果,采用冗余检测策略去除约简结果中的冗余属性;最后,在9个UCI数据集上进行实验,并与4种代表性算法进行比较。结果表明,该算法在约简能力、运行时间和收敛速度上均表现良好,在求解最小属性约简问题上具有一定的优越性。

优化的Rough Set综合评价算法在房地产开发和销售中的应用

将优化的Rough Set综合评价算法属性的同分辨能力数引入到粗糙集评价,增加了评价的客观性,优化了评价算法,对于房地产开发的决策和综合评价有辅助作用。