一种武器-目标分配模型及求解算法

武器-目标分配是拟制作战计划的一项重要内容,是合理运用现有武器系统,充分发挥其作战效能的关键.依据火力分配的基本要素,针对多种武器对多个目标的分配问题,运用一种基于模糊优选技术的多目标混合优化理论,建立了武器-目标最佳分配模型,然后用遗传算法来求解,并在计算机条件下对求解的效果进行了检验.     

基于预报误差分布拟合的卫星历史机动检测方法

轨道机动会引起在轨卫星的轨道异常,是空间态势感知的重点关注对象之一。为此,提出一种从卫星的两行轨道要素编目数据中检测历史机动的方法。通过分析预报误差检测轨道参数的异常编目值,进而获得相应的历史机动信息。对卫星的两行轨道要素编目数据两两预报求差得到预报误差的样本数据后,利用期望最大化算法可以拟合得到以高斯混合模型表达的预报误差的概率分布模型;以此为基础,可以确定利用预报误差检测轨道参数异常编目值的门限;利用轨道机动与异常编目值之间的对应关系,最终实现机动检测。对典型活动卫星的机动检测结果表明,所提方法能够在准确检出历史机动事件的同时保持较低的误检率。     

基于混沌量子粒子群的舰炮身管多目标优化研究

舰炮的炮口扰动是影响舰炮射击精度的重要因素。为了减少舰炮的炮口扰动,优化身管结构,建立了柔性身管的有限元模型并将该模型的模态计算结果与模态试验测试值进行对比,证明建立的柔性身管有限元模型是有效的。提出了混沌量子粒子群算法与动力学联合优化的方法,进行身管和炮口的多目标优化,优化结果表明：优化后的炮口中心的线速度、角速度和角位移与优化前相比显著减小,身管质量有所降低,结构更加合理,该优化方法有效可行,为下一步全炮总体优化设计提供了一定的借鉴。     

基于内外子区域划分的高阶精度CFD程序异构并行算法

对计算流体力学(CFD)程序CNS提出一种Offload模式下基于内外子区域划分的异构并行算法,结合结构化网格下有限差分计算和四阶龙格库塔方法的特点,引入ghost网格点区域,设计了一种ghost区域收缩计算策略,显著降低了异构计算资源之间的数据传输开销,负载均衡时CPU端的计算与MPI通信完全和加速器端的计算重叠,提高了异构协同并行性。推导了保证计算正确性的ghost区域的参数,分析了负载均衡的条件。在“CPU(Intel Haswell Xeon E5-2670 12 cores ×2)＋加速器(Xeon Phi 7120A ×2)”的服务器上测得该算法较直接将任务子块整体迁至加速器端计算的异构算法性能平均提升5.9倍,较MPI/OpenMP两级并行算法使用24个纯CPU核的性能,该算法使用单加速器时加速1.27倍,使用双加速器加速1.45倍。讨论和分析了性能瓶颈与存在的问题。     

Clustered coverage orienteering problem of unmanned surface vehicles for water sampling

This study investigates a clustered coverage orienteering problem (CCOP), which is a generalization of the classical orienteering problem. The problem is widely motivated by the emerging unmanned techniques (eg, unmanned surface vehicles and drones) applied to environmental monitoring. Specifically, the unmanned surface vehicles (USVs) are used to monitor reservoir water quality by collecting samples. In the CCOP, the water sampling sites (ie, the nodes) are grouped into clusters, and a minimum number of nodes must be visited in each cluster. With each node representing a certain coverage area of the water, the objective of the CCOP is to monitor as much as possible the total coverage area in one tour of the USV, considering that overlapping areas provide no additional information. An integer programming model is first formulated through a linearization procedure that captures the overlapping feature. A two-stage exact algorithm is proposed to obtain an optimal solution to the problem. The efficiency and effectiveness of the two-stage exact algorithm are demonstrated through experiments on randomly generated instances. The algorithm can effectively solve instances with up to 60 sampling sites.     

On competitive analysis for polling systems

Polling systems have been widely studied, however most of these studies focus on polling systems with renewal processes for arrivals and random variables for service times. There is a need driven by practical applications to study polling systems with arbitrary arrivals (not restricted to time-varying or in batches) and revealed service time upon a job's arrival. To address that need, our work considers a polling system with generic setting and for the first time provides the worst-case analysis for online scheduling policies in this system. We provide conditions for the existence of constant competitive ratios, and competitive lower bounds for general scheduling policies in polling systems. Our work also bridges the queueing and scheduling communities by proving the competitive ratios for several well-studied policies in the queueing literature, such as cyclic policies with exhaustive, gated or l-limited service disciplines for polling systems.     

嵌入遗传算法的顺轨干涉SAR海表流场反演新方法

顺轨干涉SAR海表流场探测技术研究对于实现流场探测的全球化、精细化具有重要作用。为了提高海表流场迭代收敛速度和参数反演精度,依据校正系数的参数特点构建适应度函数的约束关系,将校正系数设计问题转化为仿真干涉相位与实测干涉相位差和雷达、平台参数约束条件下的比例因子选择问题,进而设计遗传算法计算校正系数的技术方法,并嵌入海表流场迭代反演算法,从而构建一种反演新方法。星载SAR数据仿真实验结果表明:海表流向反演的均方根误差优于10.0°,海表流速的均方根误差优于0.1 m/s,这符合海表流场反演精度要求。改进的海表流场反演算法可减少2~3次迭代,有效提高了反演效率。研究对于提高顺轨干涉SAR海表流场探测的实效性和准确性具有重要意义。     

先期毁伤原则下武器单元的射击时机分配

以先期毁伤为原则,首次对武器单元的射击时机分配问题进行数学建模。提出一种使毁伤概率密度在时间上靠前分布的武器单元射击时机分配模型,在保证指定瞬时之前以期望概率毁伤目标的前提下,合理分配各武器单元的射击时机,实现对目标的尽早毁伤。根据射击时机分配模型中约束函数的单调性特点,提出一种改进的连续域蚁群算法,使算法自变量满足约束条件的"优良区域"开始搜索,采用搜索窗口平移的方法逐步寻找最好解,提高了算法的搜索速度。实例分析验证了模型和算法的有效性。     

基于改进型粒子群算法的航空多项目调度方法

多项目并举状况下的资源有限-工期最短问题求解,是提高航空企业资源利用率、缩短项目周期的关键。在项目网络计划的基础上,提出了一种改进型粒子群算法对多项目实施并行资源调度,求解多项目最优资源调度方案。该方法通过改进的交叉和变异操作,提高了解的多样性,保证了算法的全局搜索能力。最后,通过对某型飞机的2个并行装配计划进行实例计算,验证了方法的有效性。     

A bivariate optimal replacement policy for a cold standby repairable system with repair priority

In this article, an optimal replacement policy for a cold standby repairable system consisting of two dissimilar components with repair priority is studied. Assume that both Components 1 and 2, after repair, are not as good as new, and the main component (Component 1) has repair priority. Both the sequence of working times and that of the components'repair times are generated by geometric processes. We consider a bivariate replacement policy (T,N) in which the system is replaced when either cumulative working time of Component 1 reaches T, or the number of failures of Component 1 reaches N, whichever occurs first. The problem is to determine the optimal replacement policy (T,N)* such that the long run average loss per unit time (or simply the average loss rate) of the system is minimized. An explicit expression of this rate is derived, and then optimal policy (T,N)* can be numerically determined through a two‐dimensional‐search procedure. A numerical example is given to illustrate the model's applicability and procedure, and to illustrate some properties of the optimal solution. We also show that if replacements are made solely on the basis of the number of failures N, or solely on the basis of the cumulative working time T, the former class of policies performs better than the latter, albeit only under some mild conditions. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010