A multi-objective particle swarm optimisation for filter-based feature selection in classification problems

Feature selection has the two main objectives of minimising the classification error rate and the number of features. Based on binary particle swarm optimisation (BPSO), we develop two novel multi-objective feature selection frameworks for classification, which are multi-objective binary PSO using the idea of non-dominated sorting (NSBPSO) and multi-objective binary PSO using the ideas of crowding, mutation and dominance (CMDBPSO). Four multi-objective feature selection methods are then developed by applying mutual information and entropy as two different filter evaluation criteria in each of the proposed frameworks. The proposed algorithms are examined and compared with a single objective method on eight benchmark data sets. Experimental results show that the proposed multi-objective algorithms can evolve a set of solutions that use a smaller number of features and achieve better classification performance than using all features. In most cases, NSBPSO achieves better results than the single objective algorithm and CMDBPSO outperforms all other methods mentioned above. This work represents the first study on multi-objective BPSO for filter-based feature selection.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Investigation on particle swarm optimisation for feature selection on high-dimensional data: local search and selection bias

Feature selection is an essential step in classification tasks with a large number of features, such as in gene expression data. Recent research has shown that particle swarm optimisation (PSO) is a promising approach to feature selection. However, it also has potential limitation to get stuck into local optima, especially for gene selection problems with a huge search space. Therefore, we developed a PSO algorithm (PSO-LSRG) with a fast “local search” combined with a gbest resetting mechanism as a way to improve the performance of PSO for feature selection. Furthermore, since many existing PSO-based feature selection approaches on the gene expression data have feature selection bias, i.e. no unseen test data is used, 2 sets of experiments on 10 gene expression datasets were designed: with and without feature selection bias. As compared to standard PSO, PSO with gbest resetting only, and PSO with local search only, PSO-LSRG obtained a substantial dimensionality reduction and a significant improvement on the classification performance in both sets of experiments. PSO-LSRG outperforms the other three algorithms when feature selection bias exists. When there is no feature selection bias, PSO-LSRG selects the smallest number of features in all cases, but the classification performance is slightly worse in a few cases, which may be caused by the overfitting problem. This shows that feature selection bias should be avoided when designing a feature selection algorithm to ensure its generalisation ability on unseen data.     

基于小波神经网络和粒子群算法的铝合金板冲压回弹工艺参数优化

针对铝合金复杂件冲压后出现的较大回弹缺陷,同时为减少冲压成形工艺参数的优化时间,使用有限元仿真软件DYNAFORM对冲压成形及回弹过程进行数值模拟,在确保数值模拟与试验结果基本一致的基础上,利用代理模型对回弹进行了优化研究。以NUMISHEET'96 S梁为研究对象,凸模圆角半径、凹模圆角半径、压边力、板料厚度作为影响因素,成形后最大回弹值作为成形目标,运用拉丁超立方抽样,通过数值仿真获得样本数据,建立影响因素与成形目标之间的小波神经网络代理模型,利用粒子群算法对该模型迭代寻优获得最优工艺参数。结果表明:小波神经网络能较好地描述板料工艺参数与回弹之间的映射关系,优化后成形件的回弹量大大减小。     

基于BP神经网络的管材材料参数逆向识别

管材材料塑性本构参数是研究管材弯曲成形的关键因素之一。对于大直径管材的力学性能,可通过取样拉伸试验测得材料的应力应变曲线与弹性模量;对于小直径管材的材料参数,则较难直接通过实验测得。该文利用ABAQUS有限元软件,对小直径厚壁管材绕弯成形及回弹过程进行数值模拟,提出基于BP神经网络算法与数值模拟仿真实验相结合的管材材料参数逆向识别的方法,实验数据的对比表明,该方法能够有效的预测管材材料参数。     

基于BP人工神经网络的钢轨交流闪光焊焊接接头质量预测

对刘国东等提出的BP(误差反向传播)神经网络归一化模型进行了改进,得到了适合钢轨交流闪光焊落锤质量预测的BP神经网络归一化模型。基于LabView开发软件编制了高速采集软件。采集了U71Mn钢轨焊接工艺正交试验的焊接电流、焊接电压和动立柱的位移,并从中提取加速烧化前一阶段的闪光率、能量输入、焊接时间和烧化量等质量特征量作为BP神经网络预测模型的输入量。建立了输入层单元数为5、隐含层单元数为14的BP神经网络焊接接头落锤质量的预测模型;以正交设计工艺试验的27个焊接接头中的17个作为训练样本,对预测模型进行训练。以余下的lO个作为检验样本,采用将训练后的预测模型进行预测,预测准确率达到90％。     

3J33B马氏体时效钢时效工艺-时效硬度的人工神经元预报

对不同时效处理的3J33B马氏体时效钢进行硬度测试,获得了时效工艺（温度、时间）、硬度参数数据。利用BP人工神经网络建立起其关系网络模型。结果表明,所建立的网络可以很好地反映出材料的时效工艺-时效硬度之间的关系,网络模型可以用来预测不同时效条件下3J33B马氏体时效钢的时效硬度,并且利用粒子群优化,对3J33B马氏体时效钢的时效工艺进行优化,对实际生产具有有效的指导作用。     

基于改进的PSO在并联机构神经网络控制系统中的应用

针对传统 PID 神经网络不能实时有效地控制非线性多变量系统的问题,设计了一种新型多变量自适应 PID 神经网络控制器。该控制器的隐含层带有输出反馈和激活反馈,实现了比例、微分和积分功能。利用一种基于解空间划分的改进粒子群算法对控制器参数进行优化,消除了初始值对控制器准确性的影响,并将控制器应用于并联机构控制中。由仿真结果可知：控制器控制精度高,鲁棒性和自适应性较强。这一研究为并联机构的精准控制和优化设计提供了理论基础。     

温锻压力机肘杆机构的自适应粒子群算法优化

为了改善温锻压力机肘杆机构的下压性能、提高产品质量,提出了肘杆机构的自适应粒子群算法优化方法.使用封闭矢量法建立了传动机构的运动学模型.以6连杆尺寸为优化参数,以滑块最大运动速度、最大加速度和曲柄最大输出扭矩为优化目标,建立了优化模型,根据曲柄存在条件、上连杆摆角约束、滑块行程约束等设置了约束条件.在粒子群算法的基础上...     

遗传算法与神经网络结合优化焊接接头力学性能预测模型

基于建立的反向传播（back propagation,BP）神经网络焊接接头力学性能预测模型,并综合运用遗传算法（genetic algorithm,GA）来优化BP神经网络连接权的方法,对模型预测性能进行了有效的改进,提高了神经网络模型的预测精度和泛化能力。对模型性能的分析表明,焊接接头力学性能预测模型的预测规律符合已有研究结论,预测误差小于5％。随着样本数据的不断充实,样本覆盖空间的不断扩大,力学性能预测模型的应用范围将不断扩大,其实际应用价值也必将越来越高。     

基于动态递归模糊神经网络的轧机辊缝动态辨识

模糊系统和神经网络,由于具有逼近任意连续非线性映射的特性,而广泛应用于系统的辨识与控制。但是传统的模糊神经网络是一种静态映射,不适用于动态系统的辨识,而轧制过程中影响轧机辊缝的因素复杂,外界干扰严重,过程参数难以确定,为提高轧机辊缝动态的辨识精度,提出了一种基于动态递归模糊神经网络的辨识模型。轧制仿真结果表明,该模型具有很高的辨识精度。     

Using modified fruit fly optimisation algorithm to perform the function test and case studies

Evolutionary computation is a computing mode established by practically simulating natural evolutionary processes based on the concept of Darwinian Theory, and it is a common research method. The main contribution of this paper was to reinforce the function of searching for the optimised solution using the fruit fly optimization algorithm (FOA), in order to avoid the acquisition of local extremum solutions. The evolutionary computation has grown to include the concepts of animal foraging behaviour and group behaviour. This study discussed three common evolutionary computation methods and compared them with the modified fruit fly optimization algorithm (MFOA). It further investigated the ability of the three mathematical functions in computing extreme values, as well as the algorithm execution speed and the forecast ability of the forecasting model built using the optimised general regression neural network (GRNN) parameters. The findings indicated that there was no obvious difference between particle swarm optimization and the MFOA in regards to the ability to compute extreme values; however, they were both better than the artificial fish swarm algorithm and FOA. In addition, the MFOA performed better than the particle swarm optimization in regards to the algorithm execution speed, and the forecast ability of the forecasting model built using the MFOA's GRNN parameters was better than that of the other three forecasting models.     

基于混沌振荡PSO-BP算法的电阻率层析成像非线性反演

粒子群优化算法是一种启发式的全局优化算法,将其与 BP 神经网络结合,能够有效地改善 BP 神经网络在进行电阻率层析反演中的收敛速度和求解质量。提出一种基于混沌振荡的粒子群算法,使用混沌振荡曲线来自适应调整惯性权重w以提高PSO算法的全局寻优能力,并使用其训练和优化BP神经网络的权值和阈值。比较不同隐含层节点数目和惯性权重w值对反演结果的影响,并给出混沌振荡PSO-BP算法非线性反演的具体实现方案。对均匀半空间中异常体理论模型进行反演,实验结果表明：混沌振荡PSO-BP不依赖初始模型,在稳定性和准确性上优于BP反演和标准PSO-BP反演,成像质量优于最小二乘法反演的。     

大气SO_2含量时间序列的BP人工神经网络预测模型

基于某市1996～2008年大气中SO2含量数据,利用BP人工神经网络(ANN)方法,建立SO2含量变化的时间序列人工神经网络模型。对该市2009～2015年大气中的SO2含量变化趋势和规律进行研究。并与趋势外推法(TEND)的预测结果进行比较。结果表明基于人工神经网络技术的SO2含量预测是可行的,模型能较好地反映SO2含量的动态变化规律。     

基于BP人工神经网络的高强度管线钢焊接接头性能参数CTOD预测系统

针对实际中高强度管线钢焊接工艺参数的选择主要依据试验和经验的局限性,使用VC 6.0建立了预测高强度管线钢焊接接头性能参数裂纹尖端张开位移(CTOD)的BP神经网络模型.该模型输入层节点数为4,1个隐层,节点数为14,激活函数为Sigmoid型.根据试验数据提取平均热输入、壁厚、预热温度和接头区域作为预测模型的输入量,预测结果的平均绝对误差为0.154,预测值误差在±20%以内的样本数占总样本数的93.3%.结果表明,人工神经网络方法是预测管线钢焊接接头性能参数CTOD的一种有效途径,可为管线钢焊接过程中主要工艺参数的选择和优化提供有效的手段.     

基于BP神经网络的钢轨闪光对焊接头灰斑面积预测

针对钢轨闪光对焊的特点,根据GAAS80/580焊机记录的压力、电流和动端位移随时间而变化的曲线,从中提取了10个主要影响接头灰斑面积的特征参数作为BP神经网络预测模型的输入量,建立了钢轨闪光对焊接头的灰斑面积预测模型.采用粒子群算法优化了BP神经网络的权值和阈值,并利用优化后的BP网络模型对接头灰斑面积进行了预测.结果表明,提取的特征参数能较好地反映焊接接头灰斑情况,粒子群算法优化的BP神经网络预测模型能较准确地预测出焊接接头灰斑面积.     

塑件质量的BP神经网络智能控制研究

设计了一个多输入多输出的BP神经网络程序,依据正交试验设计训练样本,实现了注射成型产品多质量目标的高精度预测;应用质量控制环和工艺控制环构建的注射成型自适应控制系统可实现对产品质量的自适应调整,提高了实际生产效率,降低了废品率。     

Modeling the compressive strength of molasses–cement sand system using design of experiments and back propagation neural network

Molasses, an eco-friendly and relatively cheap binder may be used as a substitute for chemical binders. For commercial exploitation of the molasses–cement sand system it is essential to generate models for predicting the properties of the sand mix from the composition. Central composite design is used to develop regression equations for predicting compressive strength of the sand mix when molasses is varied between 5.5% and 7.5% and cement between 2% and 4%. Though central composite design is an effective tool for studying the complex effects of number of independent variables on response factor it has quite a few limitations. Back propagation neural network is not only capable of modeling highly non-linear relationship using dispersed data in the solution domain but has a few advantages over the central composite design. But one of the major drawbacks of this network is that no theoretical basis exists to determine the number of hidden layers and number of neurons therein. Different configurations of BPNN have great effects on the predicted results. Back propagation neural networks of different configurations are trained. Results obtained form these networks are analyzed and compared with those obtained form regression equations and experiments. Guidelines for selecting the effective configuration of back propagation networks are proposed.     

广义动态模糊神经网络焊接接头力学性能预测

建立广义动态模糊神经网络模型,用来预测焊接接头力学性能. 模型结构不再是建模时预设,而是在对逐个样本的学习过程中动态自适应调整. 引入椭圆基函数扩大函数的接收域,利用系统误差和模糊规则ε完备性作为模糊规则增加的依据,并将模糊规则ε完备性作为径向基单元的宽度确定准则. 以误差减少率评价模糊规则的重要性,并以此为依据对模型的模糊规则进行修剪. 采用三种不同厚度、不同工艺TC4钛合金TIG焊接试验,获得17组训练样本和5组仿真样本数据,建模并仿真. 结果表明,该模型能够对焊接接头力学性能进行较为准确的预测.