基于粒子群优化建模的贝叶斯网络结构学习方法

贝叶斯网络(BN)在不确定性的条件下表示信息和推理论证具有良好的性能,但由于其结构搜索空间的复杂性,通常将从一个数据集合中学习贝叶斯网络的结构认为是一种NP-hard的问题。基于此,提出一种新的基于粒子群优化算法建模的贝叶斯网络结构学习方法。为了学习一个贝叶斯网络的结构,该方法先使用粒子群优化算法在排序空间中进行搜索,然后运行K2算法计算每个排序的吻合度。每个排序都会有一个网络结构与之一致,该方法会返回这个网络的计分。仿真结果表明,在不同规模的数据集中,该算法相对于其他贝叶斯网络结构学习算法对不同类型的网络都具有更好的网络稳定性。     

分支测试中测试路径用例的简化生成方法

结构性测试是对过程式和面向对象程序都非常有效的测试方法,分支覆盖准则被实践证明是其中性价比最高的一种策略．通过深入研究DD图的性质并分析FTPS算法的不足,提出了一种简便、快捷和适合于大规模程序的非约束边集近似求解算法Find_SemiUE;还给出了基于正（逆）向广度（深度）生成树的分支测试路径用例集的简化生成算法Generate_PathSet,该算法在时间和空间开销上较FTPS算法均有较大提高．此外,所证明的关于DD图的结论也值得借鉴用于该图的更深一步研究．     

一种基于蚁群优化的图像分类算法

现有图像降维方法中特征信息被过多压缩,从而影响图像分类效果。提出IC-ACO算法,利用蚁群算法来解决图像分类问题。算法充分提取并保留图像的各种形态特征。利用蚁群优化算法在特征集中自动挖掘有效特征和特征值,构建各类分类规则,从而实现图像的分类识别。在真实的车标图像数据集上的实验结果表明,IC-ACO算法比其他类似算法具有更高的分类识别率。     

基于BPEL的MDO流程建模与调度系统设计和实现

在分析多学科设计优化特点的基础上,充分集成Web services技术和BPEL技术,设计一个基于BPEL的多学科设计优化的流程建模与调度系统。重点介绍该系统的两个模块即可视化流程建模工具模块和流程调度监控平台模块的设计和实现方法。最后给出一个优化实例说明该系统的可行性。     

Designing an adaptive approach for segmenting non-stationary signals

Signal analysis tools such as Fourier transform are often applicable on data with a limited length. Segmentation is an important pre-processing step in many signal processing applications. Statistical characteristics of the signal in a segment are often preferred to be similar. This characteristic, stationarity, improves performance of signal analysis technique. This article develops an adaptive segmentation method based on wavelet transform and fractal dimension from two aspects. One is to use discrete stationary wavelet transform in pre-processing step instead of using classical wavelet transform. The other is to choose the optimal parameters. Two parameters are needed to calculate the fractal dimension of a decomposed signal, window length and percentage overlapping of the successive windows, which affect the performance of the proposed approach. These parameters are optimally set using the particle swarm optimisation algorithm. Performance of the proposed method is compared with three other existing segmentation methods using both synthetic signal and real data. The results indicate the superiority of the proposed technique in terms of accuracy compared to existing methods.     

线搜索(LS)共轭梯度法非单调算法的全局收敛性

非单调线搜索技巧在非线性优化中得到成功的应用与扩展,非单调线搜索下的共轭梯度法则可以提高大规模非线性优化问题的收敛速度。对LS共轭梯度法做了某些变型,在非单调线搜索下,该方法保证每次迭带都会产生下降的方向,在较弱的条件下得到算法全局收敛性。     

基于级联CNNs的非约束车牌精确定位

为解决单一深层卷积神经网络用于非约束场景下车牌定位时,所输出的矩形检测框对非正面车牌定位效果不佳的问题,提出将目标检测与目标分类CNN网络级联,通过检测网络得到感兴趣区域,接着利用轻量级分类网络,将车牌顶点检测问题转化为回归问题。首先,利用YOLOv3网络进行粗定位,获取图像中所有车牌的候选区域;然后,使用基于MobileNetV3改进的轻量级神经网络定位候选区域中的车牌顶点,实现车牌区域精定位;最后,通过透视变换将车牌区域投影到矩形框内实现车牌校正。实验结果表明,所提出的级联CNNs能够有效解决单一CNN目标检测网络仅能输出矩形检测框,而不适用于非约束车牌定位的问题,具有较好的应用价值。     

Thermal buckling optimisation of composite plates using firefly algorithm

Abstract

Composite plates play a very important role in engineering applications, especially in aerospace industry. Thermal buckling of such components is of great importance and must be known to achieve an appropriate design. This paper deals with stacking sequence optimisation of laminated composite plates for maximising the critical buckling temperature using a powerful meta-heuristic algorithm called firefly algorithm (FA) which is based on the flashing behaviour of fireflies. The main objective of present work was to show the ability of FA in optimisation of composite structures. The performance of FA is compared with the results reported in the previous published works using other algorithms which shows the efficiency of FA in stacking sequence optimisation of laminated composite structures.     

Match-up approaches to a dynamic rescheduling problem

This article considers the problem of inserting arriving jobs into an existing schedule of a real world manufacturer. A number of match-up strategies, which collect the idle time on machines of a current schedule for the insertion of new jobs, are proposed. Their aim is to obtain new schedules with a good performance which are at the same time highly stable, meaning that they resemble as closely as possible the initial schedule. Basic rescheduling strategies such as ‘total rescheduling’, ‘right shift’ and ‘insertion in the end’ deliver either good performance or stability, but not both. Contrarily, our experimentation and statistical analysis reveal that the proposed match-up strategies deliver high performing schedules with a high stability. An analysis of the problem parameters that determine the behaviour of the proposed match-up algorithms is included in this article.     

A fast branch,bound and remember algorithm for disassembly line balancing problem

In recent years, the interests of disassembly line have increased owing to economic reasons and the increase of environmental awareness. Effective line can provide many advantages in terms of economic aspect and it facilitates competition the companies with others. This study contributes to the relevant literature by a branch, bound and remember algorithm for disassembly line balancing problem with AND/OR precedence. The proposed exact solution method employs the memory-based dominance rule to eliminate the reduplicated sub-problems by storing all the searched sub-problems and to utilise cyclic best-first search strategy to obtain high-quality complete solutions fast. In this paper, minimising the number of stations is taken as the performance measure. The proposed methodology is tested on a set of 260 instances and compared with the mathematical model using CPLEX solver and five well-known metaheuristics. Computational results show that the proposed method is capable of obtaining the optimal solutions for all the tested instances with less than 0.1?seconds on average. Additionally, comparative study demonstrates that the proposed method is the state-of-the-art algorithm and outperforms the CPLEX solver and metaheuristics in terms of both solution quality and search speed aspects.