微电网储能容量优化配置算法仿真

为了确保微电网系统的电能质量和稳定运行,提出基于Benders分解的微电网储能容量优化配置算法。以微电网系统作为研究对象,分析不同储能单元的充放电特性,将储能系统全寿命周期最小成本作为目标函数,组建微电网储能容量优化配置模型。根据模型特点,将模型分解为上下2层子模型,通过Benders分解算法对模型求解,进而获取最优微电网储能容量优化配置方案。经实验测试结果表明,所提算法可以更好完成容量优化配置,经过优化配置后,电网功率波动范围得到了明显降低。     

面向级联失效的加权网络负载容量非线性模型鲁棒性优化

在考虑网络负载容量关系的情况下,研究加权网络中的级联失效现象。提出了负载容量非线性模型,并针对模型参数的多目标优化问题,通过理论分析确定其影响因素。以级联失效前后网络连通性测度的比值作为鲁棒性测度,以网络容量与负载的比值平均数作为网络成本测度,采用基于拥挤距离排序的多目标粒子群优化算法(MOPSO-CD)对参数进行优化。最后通过在模拟网络和实际网络中进行的仿真比较,验证了优化后的负载容量非线性模型可以在有限的网络成本下更好地提高网络对级联失效的抵御能力。     

基于遗传算法公交线路网优化模型仿真研究

研究了城市公交线路网优化问题.由于公交车的容量、耗时、站点配置等问题,传统的遗传算法在公交线路网优化中的运行效率不高等.为提高公共交通车辆调度效率,提出了改进的遗传算法公交线路网优化模型,保证公交公司不亏损和乘客乘车成本两者前提下,构造了遗传算法适应度函数以及约束条件,并建立有效的初始化算法种群,从而建立新的公交线路网优化模型,在求解模型过程中,采用了改进的遗传算法求解优化模型,即在传统遗传算法中加入了优先级编码方式以及多染色体结构.仿真结果表明改进算法的可行性,并与传统的遗传算法相比,算法种群的整体性能要优于传统的遗传算法,对提高遗传算法的运行效率有较高的作用.     

基于网络编码的对等网流媒体传输模型和算法

在对等网(peer-to-peer,简称P2P)流媒体系统中,节点(用户)的输出带宽(上行带宽)容量利用率的提高能够降低服务器的带宽开销.网络编码可以实现组播的最大吞吐率,因而具有提高系统中节点输出带宽容量利用率的潜力.将随机线性网络编码应用到P2P流媒体系统中,建立了基于随机线性网络编码的P2P流媒体传输过程模型,并据此建立传输算法的优化模型,比较研究了贪婪式算法、最少者优先算法和随机算法等.优化结果表明,随机算法可以平等均匀地获取数据包,能够最充分地利用节点的输出带宽容量,降低服务提供商的运营成本.通过对优化模型解的分析对实际系统中的传输算法给出了设计指导原则.     

含风电场的旋转备用容量优化调度研究

为保证含风电电力系统的安全稳定与经济运行,构建了以最小化系统运行成本为目标的旋转备用容量优化调度模型。为提升模型的求解运算速度,提出一种改进的模拟植物生长算法对模型进行优化求解。针对算法在对模型求解时优化效率低、易陷入局部最优等不足做出改进,将反向学习思想引入植物生长算法,对生长点进行反向变异以扩展算法的搜索空间;通过智能变步长搜索和精英集的变异机制,保证快速寻优的同时提高求解精度。通过标准测试函数验证了改进后的算法计算速度更快,寻优能力更强。最后,在IEEE 30节点系统上进行实例验证,实验结果表明,所提出的模型能够有效地解决含风电场的旋转备用容量优化调度问题。     

基于稀疏模型的曲线光顺算法

为了对曲线进行有效光顺,将曲线光顺问题建模成基于稀疏模型的优化问题,并提出了新的曲线光顺算法.通过分析非光顺曲线的例子,对现有的判别准则进行了总结;根据总结出的判别准则,曲线的光顺性与曲率图二阶差分的稀疏性与大小密切相关,将曲线光顺问题建模成一种基于稀疏模型的优化问题;并给出了一种高效的迭代调整算法求解该模型.数值实验结果表明,文中算法是实用和有效的;与其他光顺算法进行对比时,该算法也有十分突出的表现.     

智能优化算法在机械故障诊断领域的应用综述

开展机械设备的故障诊断对保障设备的稳定运行和企业的安全生产具有重要意义.故障诊断的准确率在很大程度上受所提取特征质量、诊断模型参数的影响,因此采用优化算法进行特征优选、寻找模型最优参数是提高诊断准确率的关键.介绍了3种经典智能优化算法和3种新型智能优化算法的原理、基本运算过程及应用现状;总结了算法的优缺点,提出了改进算法;重点分析了各算法在机械故障诊断中的具体作用,并对未来智能优化算法在故障诊断领域的研究方向进行了展望.     

计算机系统与计算机网络中的动态优化:模型、求解与应用

动态优化是计算机系统与计算机网络中进行资源分配与任务调度等方面研究所采用的主要理论工具之一.目前,国内外已开展大量研究,致力于深化动态优化的理论研究与工程应用.文中从模型、求解与应用3个角度,对马尔可夫决策过程动态优化理论模型进行了综述,并重点介绍了将动态优化理论与随机Petri网理论相结合的马尔可夫决策Petri网和随机博弈网模型,详细讨论了这些模型的建模方法、求解算法与一些应用实例.最后,对全文进行了总结,并对未来可能的研究方向进行了展望.     

车辆装配线线边物料准时化配送算法

为了有效地解决带线边缓存容量约束的车辆装配线准时化物料配送问题,提出了改进免疫克隆选择调度算法.首先进行了问题域的描述,以最小化搬运成本和线边库存成本总和为优化目标,建立了数学规划模型.针对这一多重约束的混合优化问题,给出了问题的性质分析,并将原问题转化为带缓存容量约束的离散优化问题.在算法设计过程中,开发了直接反映配送路径及配送量的变长双层整数编码机制.为了提升算法的收敛性能,在邻域生成机制中融入了疫苗接种操作和基于Metropolis接受准则的局部搜索算子.最后,对算法进行了仿真实验.结果表明,该配送算法可行、有效.     

分布式电源的双目标区域优化

为合理规划分布式电源的容量与位置,减少配电网有功损耗,提高静态电压稳定性,提出了分布式电源的双目标区域优化方法。首先建立双目标优化模型,为符合配电网分布式电源的实际配置要求,定义了区域优化矩阵与容量限制矩阵,进而应用量子粒子群算法统一优化得出优化区域内分布式电源的容量与位置;最后对IEEE 33节点配电网进行仿真优化,结果表明分布式电源区域优化方法能较为准确地优化配电网中分布式电源的容量与位置,且简单可行。     

无线传感器网络节点的加权定位研究

根据实际工程提出一种基于加权定位算法的粮库压力传感器网络模型。运用数学建模、数学分析、最优化理论知识,根据浅圆粮仓粮堆底部力场特性,提出一种加权定位算法,根据此算法构建压力传感器网络模型。对比文献中提出的几种传感器网络模型,该方法优化了传感器节点的布置,提高了监测精度。实验显示该方法具有很高可靠性和适用性。     

基于不确定理论的复杂体系优化方法研究

为研究复杂体系的优化方法,首先针对复杂体系的三个发展阶段,提出了三类优化问题;其次通过研究体系结构、体系能力和体系效能之间的内在关系,给出了体系优化的思路,建立了以体系效能为优化目标的复杂体系优化模型;接着利用模糊集理论与证据理论讨论了体系能力评估中定性信息的定量化问题,探索了新的体系能力评估方法,并基于体系效能仿真,采用粗糙集理论建立了求解体系能力权值因子的算法;最后给出了求解优化模型的方法.所提出的优化方法为复杂体系优化问题提出了一种新思路.     

Decentralized capacity allocation of a single-facility with fuzzy demand

Capacity allocation under uncertainty environment is an important decision problem in manufacturing. The decentralized capacity allocation of a single-facility among different organizations with fuzzy demand is investigated in this paper. The objective and demand of each organization are assumed to be private information that other organizations and the facility cannot access to. In addition, we assume organizations have limited view of the capacity and loading of the facility. First, fuzzy optimization models associated with each organization and the facility are set up. Then, based on fuzzy theory, the fuzzy optimization models are converted into parametric programming models and subsequently an interactive algorithm is proposed to solve those parametric programming models. The extra benefit of this algorithm is that the whole solving process is amenable to decentralized implementation. Finally, experimental results illustrate the effectiveness of this work under two levels of information sharing: capacity information of the facility unknown to organizations and capacity information of the facility partially known to organizations.     

面向Kullback-Leibler散度不确定集的正则化线性判别分析

线性判别分析是一种统计学习方法. 针对线性判别分析的小样本奇异性问题和对污染样本敏感性问题, 目前许多线性判别分析的改进算法已被提出. 本文提出了基于Kullback-Leibler (KL)散度不确定集的判别分析方法. 提出的方法不仅利用了L_s范数定义类间距离和L_r范数定义类内距离, 而且对类内样本和各类中心的信息进行基于KL散度不确定集的概率建模. 首先通过优先考虑不利区分的样本提出了一种正则化对抗判别分析模型并利用广义Dinkelbach算法求解此模型. 这种算法的一个优点是在适当的条件下优化子问题不需要取得精确解. 投影(次)梯度法被用来求解优化子问题. 此外, 也提出了正则化乐观判别分析并采用交替优化技术求解广义Dinkelbach算法的优化子问题. 许多数据集上的实验表明了本文的模型优于现有的一些模型, 特别是在污染的数据集上, 正则化乐观判别分析由于优先考虑了类中心附近的样本点, 从而表现出良好的性能.     

基于列存储的MapReduce分布式Hash连接算法

大数据具有规模大、深度大、宽度大、处理时间短、硬件系统普通化、软件系统开源化的特点。传统关系型数据库在对大数据进行操作时存在系统性能严重下降、计算效率提升有限以及可扩展性差等问题,因此引入MapReduce并行计算模型,提出一种大数据上基于列存储的MapReduce分布式Hash连接算法。首先,设计面向大数据的分布式计算模型,在设计的分片聚集并行连接的基础上,利用Hash连接以及动态探测方法优化了数据并行连接处理效率;然后,针对该算法开发了基于Hadoop的原型系统。通过实验证明,在大数据分析处理中,所提算法在执行时间和负载能力上都有很好的性能表现,也能提供良好的可扩展性。     

A Comparative Analysis of Various Optimization Models for Electricity Capacity Expansion

Several optimization models have been proposed in the literature to solve the project selection, timing and sequencing problem. Models based on dynamic programming (DP) such as embebed state DP, objective space DP, and others have been used to solve the capacity expansion problem. Also models based on mixed integer linear programming using Benders decomposition theory, and heuristic rules have been proposed to solve that problem. In this paper these models are analyzed and compared from the theoretical point of view. All the reviewed models are then compared using them to define the minimum cost expansion of the Colombian electric sector. The Colombian interconnected electric generation system has about 10,000 MW of installed capacity with a generation composition of 80% hydro-generation plants and 20% thermo-generation plants. the expansion planning of the system has been carried out at a national level using simulation and optimization techniques. All these techniques are reviewed and discussed in this paper. Some conclusions and recommendations are finally presented.     

Time series prediction with improved neuro-endocrine model

The paper is focused on improving the performance of neuro-endocrine models with considering the interaction of glands. Comparing to conventional neuro-endocrine models, the concentration of hormone of one gland is modulated by those of others, and the weights of cells are modulated by the improved endocrine system. The interacted equation among all glands is designed and the parameters of them are chosen with theory analysis. Because all the parameters of the model are constants when the system reaches the equilibrium state, particle swarm optimization algorithm is utilized to search the optimal parameters of the model. The theory analysis indicates that the performance of neuro-endocrine model is better than or at least equal to that of corresponding artificial neural network. To indicate the effectiveness of the proposed model, some time series from different research fields, which are used in some literatures, are tested with the proposed model, the results indicate that the proposed model has some good performance.     

Performance optimization and mechanical modeling of uniaxial piezoresistive microaccelerometers

The acceleration measurements in automotive, navigation, biomedical and consumer applications demand high-performance microaccelerometers. This paper presents an optimization model to maximize the bandwidth of uniaxial piezoresistive microaccelerometers based on cantilever-type beams. The proposed model provides a high sensitivity as well as normal stress levels lower than the material rupture stress of these microaccelerometers. This model uses the Rayleigh method to determine the objective function of the bandwidth and the maximum-normal-stress failure theory to obtain a stress constraint that guarantees safe operation for the microaccelerometer structure. The Box-Complex optimization method is used to solve the optimization model due to its easy programming algorithm. Finite element models (FE) are developed to determine the mechanical behavior of the optimized piezoresistive microaccelerometers. The results of the FE models agree well with those of the optimization model. The optimization model can be easily used by designers to find the optimum geometrical dimensions of piezoresistive microaccelerometers to maximize their performance.     

考虑库区吊机能力的板坯倒垛问题的建模与优化方法研究

研究了钢铁企业板坯库的板坯倒垛(Slab stack shuffling, SSS)问题. 与以往研究倒垛问题不同, 考虑了板坯存储各库区吊机能力的实际限制. 对该问题通过考虑实际约束, 以总倒垛次数最小为目标函数建立了非线性整数规划模型. 基于对问题特征的分析, 分别针对轧制项目间是否存在共同可选板坯的两种情况, 将模型变换为线性整数规划模型. 另外, 利用提出的问题性质, 降低了模型的求解复杂性. 实验结果表明, 对于实际的问题规模, 基于该线性整数规划模型的优化方法能有效地求得问题的最优解.     

A constrained non-linear optimization model for fuzzy pairwise comparison matrices using teaching learning based optimization

Non-linear optimization models have been recently proposed to derive crisp weights from fuzzy pairwise comparison matrices. In this paper, a TLBO (Teaching Learning Based Optimization) based solution is presented for solving an optimization model as a system of non-linear equations to derive crisp weights from fuzzy pairwise comparison matrices in AHP (Analytic Hierarchy Process). This fuzzy-AHP method is named as TLBO-1. It has been found that TLBO-1 can lead to inconsistent or less consistent weights. To solve the problem of inconsistent weights, a new constrained non-linear optimization model is proposed in this paper. This model is based on the min-max approach for fuzzy pairwise comparison ratios of weights. TLBO is again used to solve this optimization model, and crisp weights are derived. This fuzzy AHP method is named as TLBO-2. The effectiveness of the proposed model is illustrated by three examples. For each example, the consistency of the derived crisp weights is compared with other optimization models. The results show that the TLBO-2 method can derive more consistent weights for the fuzzy AHP based Multi-Criteria Decision Making (MCDM) systems as compared to the other optimization models.