A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms have been developed;however few of them are tested in solving building design problems.This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building(n ZEB) where more than 1.610 solutions would be possible.The compared algorithms include a controlled non-dominated sorting genetic algorithm witha passive archive(p NSGA-II),a multi-objective particle swarm optimization(MOPSO),a two-phase optimization using the genetic algorithm(PR_GA),an elitist non-dominated sorting evolution strategy(ENSES),a multi-objective evolutionary algorithm based on the concept of epsilon dominance(ev MOGA),a multi-objective differential evolution algorithm(sp MODE-II),and a multi-objective dragonfly algorithm(MODA).Several criteria was used to compare performance of these algorithms.In most cases,the quality of the obtained solutions was improved when the number of generations was increased.The optimization results of running each algorithm20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity,followed by the p NSGA-II,ev MOGA and sp MODE-II.Uncompetitive results were achieved by the ENSES,MOPSO and MODA in most running cases.The study also found that 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model.

PERFORMANCE COMPARISON OF ECG COMPRESSION ALGORITHMS BASED ON CLINICAL DIAGNOSIS

This paper reviewed the recent progress in the field of electrocardiogram (ECG) compression and compared the efficiency of some compression algorithms. By experimenting on the 500 cases of ECG signals from the ECG database of China, it obtained the numeral indexes for each algorithm. Then by using the automatic diagnostic program developed by Shanghai Zhongshan Hospital, it also got the parameters of the reconstructed signals from linear approximation distance threshold (LADT), wavelet transform (WT), differential pulse code modulation (DPCM) and discrete cosine transform (DCT) algorithm. The results show that when the index of percent of root mean square difference(PRD) is less than 2.5%, the diagnostic agreement ratio is more than 90%; the index of PRD cannot completely show the damage of significant clinical information; the performance of wavelet algorithm exceeds other methods in the same compression ratio (CR). For the statistical result of the parameters of various methods and the clinical diagnostic results, it is of certain value and originality in the field of ECG compression research.

Aperiodic Checkpoint Placement Algorithms—Survey and Comparison

In this article we summarize some aperiodic checkpoint placement algorithms for a software system over infinite and finite operation time horizons, and compare them in terms of computational accuracy. The underlying problem is formulated as the maximization of steady-state system availability and is to determine the optimal aperiodic checkpoint sequence. We present two exact computation algorithms in both forward and backward manners and two approximate ones;constant hazard approximation and fluid approximation, toward this end. In numerical examples with Weibull system failure time distribution, it is shown that the combined algorithm with the fluid approximation can calculate effectively the exact solutions on the optimal aperiodic checkpoint sequence.

基于深度学习的无锚框目标检测算法综述

近年来,基于深度学习的无锚框目标检测算法备受关注。为了深入理解无锚框检测算法,对比分析了基于深度学习的无锚框检测算法的原理机制、网络结构、核心特性以及优缺点,归纳总结了无锚框检测算法的核心技术,并在同一数据集上通过性能实验研究上述算法的性能,总结提出基于深度学习的目标检测算法未来的研究方向。

数据驱动模式下基于非平行超平面SVM算法的贸易经济预测

数据驱动的多元化发展导致数据异构性增强、维度提升和特征量规模扩大,给贸易经济分析带来更大挑战。为了提高贸易经济分析的科学性,采用非平行超平面支持向量机算法(support vector machine,SVM)对贸易经济进行预测分析。首先,根据贸易经济影响因素进行主成分分析,获取影响贸易经济的关键特征,并对特征进行量化和去噪处理。然后,采用广义特征值最接近支持向量机(proximal support vector machine via generalized eigenvalues,GEPSVM)进行贸易经济预测分类。根据预测指标要求,选择核函数GEPSVM算法(KGEPSVM算法)对分类的非平行超平面求解,通过类别划分函数获得经济预测结果。实证分析表明,对比常用的非平行超平面支持向量机算法,所提算法的贸易经济预测性能更优,而且在常用贸易经济指标的预测中,表现出较高预测精度和稳定性。

基于语义聚类的遗传规划算法比较

针对遗传规划算法容易陷入局部最优解与局部搜索过慢的问题,提出一种基于语义聚类的遗传规划算法(genetic programming algorithm based on semantic clustering,SCGP),比较不同聚类算法对SCGP表现的影响。同时提出一种基于子种群规模的自适应适应度函数,提高局部搜索能力。在多个基准问题上对比标准遗传规划、几何语义遗传规划、K均值聚类遗传规划与SCGP,实验结果表明,SCGP算法在拟合能力和泛化能力上都有较大改善。在诸多聚类方法中,层次聚类嵌入的SCGP算法在基准问题上的泛化能力最优,与标准遗传规划、几何语义遗传规划、K均值聚类遗传规划相比,分别提高了32.36%、61.29%、20.53%。

基于联邦加权学习算法的三维激光隧道裂缝探测研究

为解决采用三维激光扫描仪探测隧道裂缝的识别精度低、抗干扰能力差的问题,提出基于联邦加权学习算法的裂缝探测研究新思路。基于隧道激光点云数据,首先使用优化联邦加权学习算法,并采用异步和残差测试自适应调整算法,以此整体达到精准探测隧道裂缝的目的。在临滕高速隧道进行试验,围绕裂缝探测的可靠性、准确度和测量精度等指标,将本文算法与传统算法进行对比分析,结果表明本文提出的新方法能有效提高隧道裂缝探测的可靠性及准确率,对探测裂缝宽度的精度上也有较好的性能。当探测结果中出现灰尘、钢筋裸露等干扰因素时,新算法较传统算法在可靠性上仍有明显优势,仍能达到95%以上的识别准确度和低于10%的识别误识率,这些确保了算法应用效果的鲁棒性。通过工程现场的实践,本算法识别出的裂缝宽度与人工测量值之间最小偏差仅为0.06 mm,验证了其良好的裂缝识别精度。

基于ε-level蝙蝠算法的无人机三维航迹规划

针对复杂地形环境、存在各种威胁和约束的无人机航迹规划问题,提出了一种基于ε-level改进蝙蝠算法的航迹规划算法。根据无人机目标函数和约束条件,建立无人机三维航迹规划模型;其次,针对蝙蝠算法处理高维带约束问题存在的早熟现象,设计了自适应权重系数和迭代阈值以平衡算法的探索能力和开发能力,并结合ε-level比较策略提高算法对非凸非线性约束优化问题的处理能力;设计了转弯半径可变的Dubins曲线用来对航迹进行平滑处理并解决2个航迹点连线贯穿地形的问题。通过仿真实验,并与蝙蝠算法、粒子群优化算法、ε-level粒子群优化算法和ε-level差分进化算法相比较,所提算法在开发能力、稳定性和成功率等方面都表现出较优的性能。

基于蛇优化算法的电力系统无功优化

无功优化在电力系统中具有重要的意义,可以提高系统的功率因数,减少有功损耗并改善电压稳定性。蛇优化算法具有全局搜索能力和快速收敛的特点,故将该算法作为一种新的优化方法运用到无功优化中,建立以机端电压、变压器变比和无功补偿量为系统控制变量,以网络损耗最小为目标的无功优化模型。通过采用57节点系统,将蛇优化算法与粒子群算法、改进粒子群算法分别进行比较验证。实验结果表明,蛇优化算法能够有效地优化电力系统的无功功率,提高系统的性能和稳定性。

架空导线载流量计算方法的比较

以JL/LB20A-630/45型三层铝绞线为例,分析比较了Morgen公式及CIGRE标准导线载流量计算方法,并在不同风速风向、日照辐射及环境温度下对导线载流量进行计算。结果表明,CIGRE标准计算更为保守,但与Morgen公式计算结果差别不大,不同风速风向下最大差值仅为Morgen计算值的1.88%,不同日照辐射强度下最大差值仅为Morgen计算值的1.24%,不同环境温度下最大差值仅为Morgen计算值的1.56%,较为极端环境条件下最大差值仅为Morgen计算值的3.26%。因此,进行JL/LB20A-630/45型三层铝绞线载流量计算时,Morgen公式与CIGRE标准计算差异较小,在工程中仍可用更为简单的Morgen公式计算导线载流量。

基于图像处理的CAD图纸比对算法

为保证施工图纸的准确性和完整性,避免工程质量问题和安全隐患,提出了一种基于图像处理的CAD图纸比对算法,可以快速地发现两幅CAD图纸之间的差异,算法的主要步骤是:将CAD图纸转换为PDF文件,进行边缘切割和像素级对齐,利用高斯分布抑制噪声,计算像素值的差异,通过腐蚀和膨胀操作消除误检或漏检,最后在原始CAD图纸上标注出不同处,并对多个不同标注进行合并处理。实验结果表明该算法的有效性和可行性,对设计工作有着重要的辅助作用。

基于聚合二次模态分解及Informer的短期负荷预测

针对区域级负荷的非平稳性及长序列预测精度低的问题,该文提出了一种基于聚合二次模态分解及Informer的短期负荷预测方法。首先,运用改进完全集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)对负荷序列进行初步分解,削弱原始序列的随机性与波动性;其次,根据子序列的样本熵计算结果进行聚合,并通过比较不同的聚合方式选出最优重构方案;然后,利用变分模态分解对高复杂度的合作模态函数进行二次分解;充分考虑到电价、气象等因素对负荷的影响,采用随机森林(random forest,RF)算法进行相关性分析,从而为每个子序列构建不同的高耦合度特征矩阵并输入Informer进行建模,并通过其多层次编码及稀疏多头自注意力机制等方式提高对负荷序列的预测效率;最后采用巴塞罗那区域级负荷数据集进行实例验证,结果显示所提框架有效解决了模态分解过程中的模态混叠以及高频分量问题,并且其长序列预测均方根误差相比其他经典深度学习模型最高降低了65.28%。

基于机器学习算法的某地源热泵系统能耗研究

暖通空调系统是建筑耗能的主要设备,研究空调系统的设备能耗模型对制定高效的优化策略起到重要作用。通过获取实际地源热泵系统的运行参数对系统主要能耗设备进行预测对比,使用MATLAB进行建模,建立反向传播神经网络(BPNN)、长短时记忆神经网络(LSTM)、支持向量机(SVM)、随机森林(RF)、极限学习机(ELM)、卷积神经网络(CNN)、基于遗传算法的反向神经网络(GA-BP)模型和基于思维进化算法的反向神经网络(MEA-BP)模型,并将预测模型的误差进行对比。在机组能耗预测中,预测效果最好的是MEA-BP模型,均方根误差(RMSE)、平均绝对误差(MAE)和平均绝对百分比误差(MAPE)分别为1.487、0.939和2.429%。在用户泵能耗预测中,预测效果最好的是GA-BP模型,RMSE、MAE和MAPE分别为0.098、0.085和2.314%。一般情况下,优化复合模型的准确性通常高于单一模型,但在一些实际工程上,个别单一模型在特定工程中的表现接近或超过组合模型。因此,在评价新型算法的性能时,必须全方位考量,并将其与传统单一模型进行详细对比。

跨区域药品与耗材服务平台

文章设计构建了一个跨区域药品与耗材服务平台,实现了药品与耗材的议价、日常采购、集中配送、结算等全过程流程化管理。基于微服务的架构设计,能够支撑不同区域、不同医院同时在一个平台上按需使用平台服务。针对药品耗材目录类型的不同,平台内置多种比价算法,提高了议价比价环节的工作效率。多维度的数据分析,为招标质量与配送效率提供了评价依据。平台建成投入使用后,服务于多个市级单位,涵盖20余家医疗机构采购配送业务,有效降低了区域内药品耗材的采购管理成本,采购规模效益也得到了最大化。

AI智能识别在运维工作中的应用探索

针对基站维护工作中的日常巡检、资源资产盘点耗时费力的问题,提出了借助AI智能算法及大数据分析实现远程智能巡检、资源资产盘点的设想,并通过技术探讨、现场实践、效果验证等多环节论证,实现了既定的目标,从而为智能运维探索出了新的思路与创新手段。

考虑CDC系统时滞的半主动悬架控制器设计研究

针对某高级轿车研制的CDC减振器原理样机,通过实验测试及其数据分析,建立了考虑CDC系统时滞影响的非线性系统模型。分别对单、双电磁阀两种模式CDC减振器进行了研究。充分考虑复杂多变的车辆实际运行工况及系统非线性因素(包括路面激励较大时可能导致的悬架缓冲块撞击,以及轮胎跳离地面等情况),基于建立的非线性车辆系统模型及不同激励水平输入下的非线性系统仿真,研究了减振器系统时滞对车辆性能的影响。结合NSGA-Ⅱ算法优化结果,设计了一个改进的天棚控制算法,分别对单、双阀减振器进行了车辆性能分析与对比。研究结果表明,采用最优天棚控制的双阀CDC减振器相比于单阀CDC减振器有更好的控制效果,且对CDC系统时滞适应能力更强。研究结果可为自主研发的减振器设计和改进提供指导,并为CDC半主动悬架控制算法设计提供重要依据。

A Genetic Algorithm on Multiple Sequences Alignment Problems in Biology

The study and comparison of sequences of characters from a finite alphabet is relevant to various areas of science, notably molecular biology. The measurement of sequence similarity involves the consideration of the possible sequence alignments in order to find an optimal one for which the “distance” between sequences is minimum. In biology informatics area, it is a more important and difficult problem due to the long length (100 at least) of sequence, this cause the compute complexity and large memory require. By associating a path in a lattice to each alignment, a geometric insight can be brought into the problem of finding an optimal alignment, this give an obvious encoding of each path. This problem can be solved by applying genetic algorithm, which is more efficient than dynamic programming and hidden Markov model using commomly now.

Analysis and Evaluation of IKONOS Image Fusion Algorithm Based on Land Cover Classification

Different fusion algorithm has its own advantages and limitations,so it is very difficult to simply evaluate the good points and bad points of the fusion algorithm. Whether an algorithm was selected to fuse object images was also depended upon the sensor types and special research purposes. Firstly,five fusion methods,i. e. IHS,Brovey,PCA,SFIM and Gram-Schmidt,were briefly described in the paper. And then visual judgment and quantitative statistical parameters were used to assess the five algorithms. Finally,in order to determine which one is the best suitable fusion method for land cover classification of IKONOS image,the maximum likelihood classification( MLC) was applied using the above five fusion images. The results showed that the fusion effect of SFIM transform and Gram-Schmidt transform were better than the other three image fusion methods in spatial details improvement and spectral information fidelity,and Gram-Schmidt technique was superior to SFIM transform in the aspect of expressing image details. The classification accuracy of the fused image using Gram-Schmidt and SFIM algorithms was higher than that of the other three image fusion methods,and the overall accuracy was greater than 98%. The IHS-fused image classification accuracy was the lowest,the overall accuracy and kappa coefficient were 83. 14% and 0. 76,respectively. Thus the IKONOS fusion images obtained by the Gram-Schmidt and SFIM were better for improving the land cover classification accuracy.

A NEW DETERMINISTIC FORMULATION FOR DYNAMIC STOCHASTIC PROGRAMMING PROBLEMS AND ITS NUMERICAL COMPARISON WITH OTHERS

A new deterministic formulation,called the conditional expectation formulation,is proposed for dynamic stochastic programming problems in order to overcome some disadvantages of existing deterministic formulations.We then check the impact of the new deterministic formulation and other two deterministic formulations on the corresponding problem size,nonzero elements and solution time by solving some typical dynamic stochastic programming problems with different interior point algorithms.Numerical results show the advantage and application of the new deterministic formulation.

基于相序的多点地质统计学相建模方法及其应用

目前的多点地质统计建模方法均以一定维度的数据模板为载体,将概率或几何距离作为建模工区未知区域模拟取值的判定标准,该判定标准在地质建模时与地质含义的联系较为薄弱。针对上述不足,文章回归地质建模的本质,考虑相建模过程中数据事件与训练图型对比的地质含义,提出基于相序的多点地质统计相建模方法。该方法以具有沉积学意义的相序为基本的相似度对比单元,新建适合于相序相似度对比的动态数据模板,采用句法模式识别技术,通过动态规划方法计算数据事件中已知相序与训练图型中对应相序的相似度,对建模工区未知区域进行相模拟,实现了从沉积学意义出发对强非均质性储层进行多点地质统计学建模。理想模型和塔河X区三角洲前缘储层沉积相模拟表明,本文方法能够有效重现训练图像所显示的各地质体的几何形态,较好地反映储层不同相的沉积展布规律,大幅提高了相建模的精度。该方法将沉积学与多点地质统计学有效结合,提出了一种全新的多点地质统计学相似度对比方法和多点地质统计学建模框架,为复杂储层的相建模提供了一种新的方法。