基于模态分析的变速箱振动测点优化配置

针对坦克变速箱振动信号在状态监测中测点的选择问题,提出了根据不同测点的频响特性优化振动测点的方法。通过分析变速箱箱体的有限元动力学模型,得到箱体的各阶固有频率和振型,选择模态分析中总位移较大的位置作为最优的测点安装传感器,为试验测点布置以及状态监测和评估提供理论支持。研究结果表明：位于风扇联动装置齿轮箱附近的传感器对变速箱振动信号的强弱变化比较敏感。     

一种考虑权重的多专业流水式批量维修任务调度模型

针对维修专业不同的设置模式和运行模式,对多专业流水式批量维修任务调度问题进行了分析和建模,在考虑任务权重的基础上将该问题分成两步求解。首先,建立了初始专业领域内批量维修任务调度及优化模型;其次,建立了考虑任务权重的多专业流水式批量维修任务调度模型。实例验证表明：该方法更贴近于实际,可快速、有效地解决批量维修任务调度问题。     

战场环境下无人车辆战术机动路径规划方法

针对不同类型威胁体存在的战场环境中无人车辆战术机动路径规划问题,提出了一种基于威胁代价地图的粒子群优化（Particle Swarm Optimization,PSO）方法。借助极坐标系中关键点的极角进行路径描述,并使用分段3次Hermite插值方法形成光滑路径,将路径规划问题转化为关键点极角的参数优化问题。针对基本PSO（BPSO）算法存在的早熟收敛和后期迭代效率低的缺陷,借鉴以群集方式生活的物种按照不同任务对种群进行分工的机制,提出了一种基于多任务子群协同的改进粒子群优化（Particle Swarm Optimization based on the Multi—tasking Subpopu—lation Cooperation,PSO-MSC）算法。借助该算法的快速收敛和全局寻优特性实现了最优路径规划。实验结果表明：该算法可以快速有效地实现战场环境下无人车辆的战术机动路径规划,且规划路径安全、平滑。     

基于自适应变异粒子群算法的火力分配方法研究

针对最优火力分配的特点,分析了火力分配优化问题的数学模型,设计了一种求解该类问题的自适应变异粒子群算法（Adaptive Mutation Particle Swarm Optimization, AMPSO）。该算法采用十进制编码方法和基于数值运算的个体更新方法;为平衡算法的局部搜索能力和全局收敛性能,设计了一种关键参数自适应调整方法;为增强种群在进化后期的多样性,提出了一种变异策略。仿真结果表明,所提AMPSO算法具有良好的寻优性能,是优化火力分配的一种有效算法。     

军队会计流程存在的问题及优化策略

在新军事变革的背景下,军队会计流程存在不适应新管理体制需要、信息技术应用对会计流程再造力度不够、军队会计科目设置影响流程等问题。本文从界定军队会计流程的含义入手,提出了优化军队会计流程、建立智能化军队会计信息系统的四项策略,即重塑军队会计流程体系、重构军队会计信息系统、调整军队会计科目体系以及完善各流程会计处理方法。优化军队会计流程、建设智能化军队会计信息系统,不仅能够反映军事经济活动的历史信息,还能为不同类型的信息使用者提供预测、决策等支持,并有利于改善军队会计核算质量,准确反映各类资产负债的价值信息和单位的真实财务状况,对于提高军队会计信息质量具有一定的借鉴意义。     

Optimization of buckling load for laminated composite plates using adaptive Kriging-improved PSO: A novel hybrid intelligent method

An effective hybrid optimization method is proposed by integrating an adaptive Kriging (A-Kriging) into an improved partial swarm optimization algorithm (IPSO) to give a so-called A-Kriging-IPSO for maxi-mizing the buckling load of laminated composite plates (LCPs) under uniaxial and biaxial compressions. In this method, a novel iterative adaptive Kriging model, which is structured using two training sample sets as active and adaptive points, is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process. The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples. The cell-based smoothed discrete shear gap method (CS-DSG3) is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets. The buckling load of the LCPs is maximized by utilizing the IPSO algorithm. To demonstrate the efficiency and accuracy of the proposed methodology, the LCPs with different layers (2, 3, 4, and 10 layers), boundary conditions, aspect ratios and load patterns (biaxial and uniaxial loads) are investigated. The results obtained by proposed method are in good agreement with the literature results, but with less computational burden. By applying adaptive radial Kriging model, the accurate optimal results-based predictions of the buckling load are obtained for the studied LCPs.     

对流层散射无源监视系统最优布局方法

为提高对流层散射无源监视系统对辐射源的定位精度,利用改进粒子群优化算法对分布式监视节点进行最优布局设计。推导了基于电磁波方位到达角定位机制下的几何精度因子。在改进粒子群优化算法中,采用混沌理论初始化所有粒子的初始参数;通过自适应变化的惯性权重和学习因子来提高算法寻优能力;为防止粒子陷入局部最优,利用双子群机制进行寻优,并在两子群之间进行交叉变异操作,以增加粒子的多样性。仿真结果表明:相对于几种常规的布站方式,所提算法能够明显提高监视系统对辐射源的定位精度,运行时间较遍历寻优算法有所减少。     

Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.     

动态威胁环境下单无人机测向定位航迹优化算法

为解决单架无人机在动态战场环境下的测向定位问题,提出了一种基于动态窗口法的单机测向定位航迹优化算法。以最大化Fisher信息矩阵行列式为测向定位评价准则,在由动态探测雷达和静/动障碍构成的动态战场环境中,基于动态窗口法思想,将测向定位航迹优化评价准则由传统的单步最优原则扩展到对多步预测航迹的评价,同时考虑雷达探测和静/动障碍环境对预测航迹的影响,通过滚动时域方法控制无人机最优航向。仿真结果表明,所提方法能够使无人机在有效逃避雷达探测威胁以及规避环境中静/动障碍的条件下保证对目标的高精度测向定位,为解决动态战场环境下的单架无人机测向定位问题提供了新思路。     

巡航导弹攻击路径组的优化与评估

为保证对敌方目标实施多次有效攻击,巡航导弹攻击路径选择的关键在于寻找一组优化路径而不是一条优化路径(如果存在),既满足性能指标要求,又满足作战应用要求.在作战区域内任意两个匹配区之间飞行航迹已知(专题讨论)的情况下,就巡航导弹攻击路径组的优化与评估问题进行了探讨,分析了导弹作战运用要求和性能指标要求,建立了攻击路径组的优化与评估模型,计算结果表明:所给出优化与评估模型是可行的,能够满足巡航导弹实际作战要求.