基于QPSO和MATLAB优化资金投资组合

量子粒子群优化算法（QPSO）是一种基于粒子群优化算法（PSO）的进化算法,它收敛速度快、规则简单、易于编程实现;Matlab是国际控制界公认的标准计算软件。采用QPSO对资金组合投资的多目标问题进行优化,使用Matlab编程,解决了传统方法难以解决的问题,仿真实验表明采用本方法能对资金投资组合问题提出较好的优化决策。     

考虑电池寿命的插电式混合动力汽车能量管理优化

考虑电池寿命对插电式混合动力汽车全寿命周期成本的影响,以综合燃油消耗和电池寿命衰减最小为目标开展电池充放电功率的多目标优化研究. 引入权重系数将多目标优化问题转化为单目标优化问题,采用动态规划（DP）算法求解实现全局最优,并根据优化结果选择最优权重系数. 为了解决动态规划算法运算速度慢、须预知工况的缺陷,以最优权重系数的优化结果训练神经网络控制器并将其应用于控制策略中. 仿真结果表明,与以油耗为单一目标的优化相比,多目标优化可使电池寿命衰减减少13.5%,而燃油消耗仅增加0.5%,在保证燃油经济性的同时有效减少电池寿命的衰减程度;基于神经网络的控制策略有效克服了动态规划算法的缺点并能达到与其相近的运算效果,具有较好的应用前景.     

基于LabVIEW的物体目标跟踪系统设计

提出一种基于演化算法的模式匹配目标跟踪方法。传统的模式匹配算法大多采用遍历式搜索策略,因而计算量的降低有限。将演化算法引入匹配技术中,提出一种改进型图像匹配算法,并给出了基于LabVIEW软件的系统实现。实验结果表明该方法具有运算速度快、准确度高等特点。     

基于LSP时延和拥塞的分布式自适应流量工程算法

研究多协议标签交换(MPLS)网络中的流量工程,提出了基于标签交换路径(LSP)时延和拥塞 来优化网络流量的算法。给出全网流量优化的数学模型,并根据该模型内在的层次性,设计 了分布式自适应流量工程算法。为了使算法具有实时性强、收敛速度快、稳定性较好、运算 和网络开销小等特点,在设计中,采用了禁忌搜索和算法参数的非线性调整来提高算法的性 能。仿真分析表明,该算法能够实现对网络性能的全面优化,适合应用于骨干网的流量优化.     

一种改进多目标灰狼优化算法的多无人机任务分配

针对多无人机对空中移动目标协同执行多任务问题,本文提出了一种基于并行机制的多目标灰狼优化算法。结合无人机空中态势模型,以最小化执行代价和最小化时间代价为双目标函数,建立了多无人机协同多任务分配模型;将多个无人机视为并行的灰狼子群,对每个子群分别采用分层编码和多目标优化算法保留其最优个体;通过档案室共享策略获得整个群体的最优解;仿真对比验证了改进多目标灰狼优化算法与传统的智能算法。研究结果表明:与多目标灰狼优化算法和多目标粒子群算法相比,基于并行机制的多目标灰狼优化算法在代价函数均值方面分别降低了约3.8%和4.1%,在收敛值方面分别降低了约15.5%和6.2%,具有更好的稳定性和收敛性。     

基于神经动力学的目标跟踪算法

通过对神经网络模型的研究,将神经动力学的Shunting模型应用到目标跟踪系统中.该算法具有模型简单、参数不敏感、网络运算不易饱和、算法速度快,且不需要预设工作环境等优点,适用于未知动态环境下的优化目标跟踪.Matlab仿真试验表明,该算法可解决动态环境下的目标跟踪.     

基于椭圆包络线算法的探地雷达成像

针对超宽带探地雷达成像算法普遍存在成像模糊、计算量大、不适合实时处理等问题,提出一种基于椭圆包络线算法的探地雷达目标边界成像方法。该算法以不同介质内电磁波运行距离分段相加的方法计算椭圆长轴,使其适用于多层介质下探地雷达目标成像。FDTD数值仿真结果表明,椭圆包络线算法对于浅层目标体边界构造准确,运算速度快,对目标解释更精确。     

基于MOEA/D的船舶水动力性能优化

为了高效地求解船舶水动力性能优化问题,将基于分解技术的多目标进化算法应用到船舶设计领域,提出基于MOEA/D的船舶水动力性能多目标优化算法。建立了以快速性、耐波性和操纵性为3目标的优化模型;通过在MOEA/D算法中引入自适应约束处理技术和目标值归一化机制,获得SHPO问题在可行域内的Pareto最优解集;利用模糊集理论为决策者提供最优折中解。采用本文算法对DTMB5415船型参数进行优化设计,并将其与另外两种基于多目标优化算法的设计方案进行对比。结果表明,该算法具有更好的收敛速度和求解精度。     

基于电影视觉特性的动态多目标实时相机规划

针对已有相机规划方案应对目标较少或无法做到实时性的问题,从自动实现相机视觉效果优化的角度出发,提出了一种对于动态多目标的实时相机规划方法.首先,通过在多个目标之间构造椭球形的相机空间;之后,获取场景中各目标相对相机的视觉特性,以此来构造非线性规划优化函数.通过模型预测控制的方法对目标在相机屏幕上的表现进行实时优化.通过对算法的运算时间、运行效率以及在场景中的运行表现进行实验,验证了本文方法能够有效地优化场景中各目标的视觉效果,对多目标相机规划问题的研究有一定促进作用.     

基于OFDM的认知无线电系统中功率分配算法

针对基于正交频分复用(OFDM)的认知无线电系统中,子载波功率分配的最优化算法中出现的迭代运算繁琐、不易实现的问题,提出一种基于幂函数分布的次优化功率分配算法.该算法采用线性约束的凸优化数值运算方法,对认知用户频带内子载波的功率按照幂函数的数值特性依次分配,该算法具有运算速度快、易于实现的特点,并且有效地解决了授权用户受到认知用户频带内子载波带外泄漏功率的干扰问题.通过使用MATLAB建模仿真,结果表明,提出的算法方案在满足授权用户干扰门限的条件下,使得认知用户频带内信道容量能够达到最大化,并且在同样条件下提高了现有功率分配方案的最大传输速率.     

考虑输配交互的含风电场电力系统多目标动态随机模糊最优潮流

提出一种考虑输配交互的含风电场电力系统多目标动态随机模糊最优潮流方法。突破传统输配网最优潮流独立计算且配网一般以负荷计及的现状,考虑主动配电网与输电网的双向潮流交互特性和风电注入功率随机模糊性,兼顾系统经济、低碳、降损多目标优化,考虑输配网静态安全,基于随机模糊机会约束规划建立含风电场电力系统输配交互多目标动态随机模糊潮流模型;提出在随机模糊模拟、牛顿拉夫逊法与前推回代法求解输配系统随机模糊潮流基础上,通过输配网节点功率和电压考虑输配交互特性,基于Look-ahead和NSGA-I获取各时段Pareto解,采用最大满意度法决策,从而提出模型求解算法。改进的IEEE 30节点输电系统与IEEE 33节点配电系统的算例仿真结果表明了文中提出的模型和算法有效性和优越性。     

Improved multi-objective artificial bee colony algorithm for optimal power flow problem

The artificial bee colony（ABC） algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow（OPF） problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.     

多目标机炉协调控制中控制变量阈值范围的确定

机炉协调控制系统的性能直接关系到机组的安全性和经济性。对协调控制系统设定值的多目标优化可提高控制系统品质,协调控制中控制变量阈值范围的确定是实现设定值多目标优化的基础。以160 MW燃油锅炉汽轮发电机组动态模型为例,给出了确定各负荷下不同控制变量输出阈值的迭代算法,为研究多目标机炉协调控制打下基础。     

Multi-objective optimal configuration of current limiting strategies

With the level of short-circuit current of power systems growing increasingly higher, optimal allocation of current limiters has received considerable attention in recent years, especially in China. This paper analyzes two kinds of common used current limiters based on the increment of bus impedance matrix and proposes a multi-objective current limiters configuration model considering the investment of limiters, the level of short-circuit current, as well as the transient stability of power system. An innovative search space reduction technique based on sensitivity factor is introduced to choose better candidate locations for current limiters so as to avoid the curse of dimensionality. The elitist non-dominated sorting genetic algorithm II is used to search the Pareto-optimal solutions of the proposed model. In order to further improve optimization efficiency, master-slave parallel modification of NSGA-II program structure is implemented. The satisfactory case study results demonstrate the feasibility of the proposed multi-objective method in power system current limiters allocation associated with cost, security and stability.     

高占比新能源电源接入区域电网的无功优化策略研究

从高占比风电、光伏接入电网运行的安全性和稳定性等方面综合考虑,建立了以电压偏移量和网损最小为目标的无功优化模型。利用MATLAB软件对风电、光伏电站接入IEEE-33节点系统进行无功优化仿真分析,采用多目标粒子群（MOPSO）算法进行无功优化调度方案求解。基于Pareto前沿解的MOPSO算法可为决策者根据不同需求提供方案,并且同时达到提高测试系统的电压稳定性、降低网损的目标,验证了所提优化策略的有效性。     

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network. Foundation item: Project (70671039) supported by the National Natural Science Foundation of China     

同时考虑发电费用与网络损耗的多目标有功最优潮流

在对传统的单纯以发电费用最少和网络损耗最小为目标的单目标最优潮流分析的基础上,利用权重系数将两者有机结合,建立了多目标的有功最优潮流模型,分别利用二次规划算法和遗传算法进行求解,并对两种计算方法的计算结果和求解效率进行了分析。     

Security region based real and reactive power pricing of power system

This paper develops a novel model and an algorithm of security region based real and reactive power pricing of power systems. In the proposed model, the reactive power production cost is represented as the opportunity cost. The static voltage stability region in the cut set power space (CVSR) and the practical dynamic security region (PDSR) in the injection power space are used to represent the constraints of voltage stability and transient stability, so that the consideration of this kind of constraints in the optimization becomes very easy. In the proposed algorithm, a decoupled optimization and iteration method of active power production cost and reactive power production cost is suggested. According to the K-T optimality conditions, the prices of active power and reactive power, and the different components corresponding to the concerned security constraints are derived. The components of spot prices can reflect the influence of different node power injections on each kind of security constraints, so that through the node price all of the participants in power market can be stimulated to take an active part in maintaining the system security. An illustrative example on the New England 10-genetator 39-bus System is used to demonstrate the proposed method. Supported by the key research project of the National Natural Science Foundation of China (Grant No. 50595413)     

考虑电动汽车不确定性的配电网软联络开关优化配置

电动汽车的接入对配电网的安全稳定运行带来了根本性的影响,利用直流软联络(SNOP)代替联络/分段开关,不仅能够提高有源配电网的潮流控制能力和实现双端馈线的电气解耦,而且能够较准确调控两端所连馈线功率和提高有源配电网运行的灵活性。该文首先分析电动汽车及其负荷的不确定性,并基于二者的时序性概率出力模型,综合考虑系统网损和电动汽车的充放电功率特性,构建多目标的SNOP最优配置模型,然后提出相应的求解算法。最后,利用IEEE 33节点系统验证数学模型和求解算法的可行性和有效性。该文的理论分析结果可为计及电动汽车接入的配电网损耗计算分析提供借鉴作用。     

基于模糊数学的发电权交易多目标函数优化算法

为了达到发电调度节能减排要求,并综合考虑电网安全,在分析传统发电权交易模式的基础上,将发电权交易的效益、煤耗及网损3个参数结合起来,提出了在役机组发电权交易的多目标函数模型.采用模糊数学方法对多目标函数进行了求解.并通过实例分析检验了该求解方法的有效性.