基于禁忌搜索的混合粒子群优化算法

针对粒子群优化算法（PSO）易于陷入局部最优解并存在早熟收敛的问题,利用禁忌搜索算法较强的“爬山”能力,搜索时能够跳出局部最优解,转向解空间的其他区域的特点,提出了一种新的基于禁忌搜索（TS）的混合粒子群优化算法（TS—PSO）,并选用两个函数进行测试．结果表明,TS—PSO比其他改进粒子群算法更能提高收敛速度,获得全局最优解．     

基于免疫抗体生成算法的电力变压器故障诊断

提出了一种基于人工免疫的抗体生成算法,并将其应用于电力变压器的故障诊断。抗体生成算法仿生生物免疫系统中抗体对抗原的识别与记忆的机理,先对训练样本进行免疫学习和记忆,提取表征样本的有效特征,形成表征样本特征的记忆抗体集,再用最邻近分类法对测试样本进行分类识别。UCI的Iris数据集和电力变压器故障数据的仿真分析结果表明,抗体生成算法能够进行有效的分类,并具有很高的准确率。     

Research on technology of desktop virtualization based on SPICE protocol and its improvement solutions

Increasingly mature cloud computing technology promotes virtual desktop technology, which can solve many problems existing in traditional computing models. However, virtual desktop solutions introduce the thorny problem of how to deliver a real desktop experience to users, as if they are using it locally, especially when playing video. The SPICE （simple protocol for independent computing environments） virtual desktop solution provides several image compression algorithms to address this problem with the purpose of making virtual desktops as real as possible. Although different compression algorithms can contribute their own abilities to different images to a large extent, switching between them is a big problem that consumes a large amount of resources to detect the different type of image and also causes jitter of the virtual desktop. This paper proposes a new solution, called SPICEx, using the JPEG2000 compression algorithm with dynamic compression ratios to solve the problem and finally validates that the performance is better than that of SPICE. With better quality of user experience and also reducing bandwidth consumption, SPICEx solution is meaningful in virtual desktop fields and can be widely used.     

Probabilistic and interval reliability design of the composite pressure vessel

Considering the randomness or interval character of physical parameters and applied loads of composite pressure vessels (COPV), the COPV reliability-analyzing model is built. And then the computational expressions for the mean value, standard deviation and deviation of fiber stress are deduced by the random factor method. The probabilistic and interval reliability designs on COPV are implemented by utilizing the probabilistic method and interval method combined with the improved particle swarm optimization (PSO) algorithm, respectively. The influence of fluctuation of structure uncertain parameters on COPV design thickness is inspected. Finally, examples are given to demonstrate that both probabilistic and interval reliability methods can satisfy the safety requirement and both are of higher rationality than the traditional safety factor method. And probabilistic method has a lower relative error but a higher computational complexity contrasted with the interval method.     

An automatic parameter calibration method for the SWAT model in runoff simulation

Runoff simulation is highly significant for hydrological monitoring, flood peak simulation, water resource management, and basin protection. Runoff simulation by distributed hydrological models, such as the soil and water assessment tool (SWAT) model which is the most widely used, is becoming a hotspot for hydrological forecasting research. However, parameter calibration is inefficient and inaccurate for the SWAT model. An automatic parameter calibration (APC) method of the SWAT model was developed by hybrid of the genetic algorithm (GA) and particle swarm optimization (PSO). Multi‐station and multi‐period runoff simulation and accuracy analysis were conducted in the basin of the Zhangjiang River on the basis of this hybrid algorithm. For example, in the Yaoxiaba Station, the calibration results produced an R² of 0.87 and Nash Sutcliffe efficiency (NSE) index of 0.85, while verification results revealed an R² of 0.83 and NSE of 0.83. Results of this study show that the proposed method can effectively improve the efficiency and simulation accuracy of the model parameters. It can be concluded that the feasibility and applicability of GA‐PSO as an APC method for the SWAT model were confirmed via case studies. The proposed method can provide theoretical guidance for many hydrological research fields, such as hydrological simulation, flood prevention, and forecasting.     

电控柴油机分缸均衡控制算法及试验

柴油机各个气缸的进气量、喷油量、燃烧等存在差异,导致气缸间存在做功差异,直接影响柴油机转速的平稳性以及排放性能等。针对位置式电控柴油机,采用PI闭环控制,通过瞬时转速信号识别并计算各缸的工作差异,利用分缸均衡控制算法对油量进行补偿修正。试验结果显示低转速稳态工况转速波动由7 r/min降至3 r/min,各缸工作不均匀度由±5 r/min降至±2 r/min,在瞬态过程和高转速3 400 r/min时该算法仍然适用有效。     

基于改进Kendall-f算法的发电机定子温度分析

在线监测发电机定子温度可有效发现定子部分过热性故障,如何从检测的温度数据中提取反映定子故障预警的信息是目前国内外研究的热点。该文采用可变窗改进Kendall-f算法处理监测温度数据,提取其变化趋势特征信息;采用偏度分析提取监测温度异常值,从而正确诊断定子过热性故障,仿真分析及实验结果表明该方法有效提高了定子温度监测与预警的准确率。     

模具生产联合体加工作业统一调度问题的研究

模具生产联合体是由小规模企业组成的模具设计、制造联盟，由于设备、工装和人员分属于不同的加盟企业，其作业排序过程非常复杂。为此提出一种复合式模具生产联合体的生产排序方法：依据基本优先规则首先完成单一工序作业，然后对多工序作业用改进遗传算法进行工艺优化，求出最优解和次优解，再使用评价函数法选择最佳作业路线。该方法在生产试验中取得了良好的成效．能较好满足模具用户和加盟企业两方面的需要。     

Dual–primal proximal point algorithms for extended convex programming

This paper presents a class of dual–primal proximal point algorithms (PPAs) for extended convex programming with linear constraints. By choosing appropriate proximal regularization matrices, the application of the general PPA to the equivalent variational inequality of the extended convex programming with linear constraints can result in easy proximal subproblems. In theory, the sequence generated by the general PPA may fail to converge since the proximal regularization matrix is asymmetric sometimes. So we construct descent directions derived from the solution obtained by the general PPA. Different step lengths and descent directions are chosen with the negligible additional computational load. The global convergence of the new algorithms is proved easily based on the fact that the sequences generated are Fejér monotone. Furthermore, we provide a simple proof for the O(1/t) convergence rate of these algorithms.