基于多种群遗传算法的远程作战体系加油路径优化

为解决加油机在远程作战体系中的高效使用与经济性问题,推导了远程作战体系中加油机和歼击机的油耗方程模型,建立以耗油量最少为目标的空中加油路径优化模型。该模型基于一定的作战任务构想,利用多种群遗传算法进行了求解,从求解结果来看,该算法可以快速搜索到能够在所有满足作战任务要求的任务路径中寻找到最小消耗的位置坐标,以获得最大的作战效能,从而达到优化远程作战体系中的作战和训练中加油机使用问题。     

基于ModelCenter的顶置武器站稳定精度优化

顶置武器站具有系统组成类型复杂、子系统间存在耦合作用的特点,导致多学科协同仿真的优化模型难以建立。通过Adams、Simulink分别建立了顶置武器站机械系统及控制系统模型,并在多学科优化设计平台Model Center中对该机电联合仿真模型进行系统集成;在此基础上,以顶置武器站稳定精度为目标函数,采用一阶差分模型对炮控系统比例系数及积分系数进行了灵敏度分析,并采用设计探索优化器对该参数进行了优化设计。仿真结果表明,所建立的顶置武器站稳定精度多学科协同优化模型设计周期短、计算效率高,为下一步进行顶置武器站多工况、多结构参数的优化设计提供技术支撑。     

基于GA-ML-PMHT的多基站无源协同定位方法

针对无源协同定位系统中低可观测目标的航迹初始及维持问题,提出一种基于遗传算法的极大似然概率多假设的多基站无源协同定位方法。首先,建立多基站无源协同定位系统数学模型。其次,提出基于极大似然概率多假设的无源协同定位航迹初始算法,并首次利用遗传算法解决极大似然概率多假设中的优化求解问题,以提高目标检测跟踪性能。最后,通过滑窗法实现航迹维持。仿真结果表明,所提方法能够有效解决多基站无源协同定位系统中低可观测目标的航迹初始及维持问题。     

Optimal division of inventory between depot and bases

We consider a problem of optimal division of stock between a logistic depot and several geographically dispersed bases, in a two‐echelon supply chain. The objective is to minimize the total cost of inventory shipment, taking into account direct shipments between the depot and the bases, and lateral transshipments between bases. We prove the convexity of the objective function and suggest a procedure for identifying the optimal solution. Small‐dimensional cases, as well as a limit case in which the number of bases tends to infinity, are solved analytically for arbitrary distributions of demand. For a general case, an approximation is suggested. We show that, in many practical cases, partial pooling is the best strategy, and large proportions of the inventory should be kept at the bases rather than at the depot. The analytical and numerical examples show that complete pooling is obtained only as a limit case in which the transshipment cost tends to infinity. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 3–18, 2017     

Non‐zero‐sum nonlinear network path interdiction with an application to inspection in terror networks

A simultaneous non‐zero‐sum game is modeled to extend the classical network interdiction problem. In this model, an interdictor (e.g., an enforcement agent) decides how much of an inspection resource to spend along each arc in the network to capture a smuggler. The smuggler (randomly) selects a commodity to smuggle—a source and destination pair of nodes, and also a corresponding path for traveling between the given pair of nodes. This model is motivated by a terrorist organization that can mobilize its human, financial, or weapon resources to carry out an attack at one of several potential target destinations. The probability of evading each of the network arcs nonlinearly decreases in the amount of resource that the interdictor spends on its inspection. We show that under reasonable assumptions with respect to the evasion probability functions, (approximate) Nash equilibria of this game can be determined in polynomial time; depending on whether the evasion functions are exponential or general logarithmically‐convex functions, exact Nash equilibria or approximate Nash equilibria, respectively, are computed. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 139–153, 2017     

The role of covariate balance in observational studies

Observational data are prevalent in many fields of research, and it is desirable to use this data to make causal inferences. Because this data is nonrandom, additional assumptions are needed in order to construct unbiased estimators for causal effects. The standard assumption is strong ignorability, though it is often impossible to achieve the level of covariate balance that it requires. As such, researchers often settle for lesser balance levels within their datasets. However, these balance levels are generally insufficient to guarantee an unbiased estimate of the treatment effect without further assumptions. This article presents several extensions to the strong ignorability assumption that address this issue. Under these additional assumptions, specific levels of covariate balance are both necessary and sufficient for the treatment effect estimate to be unbiased. There is a trade‐off, however: as balance decreases, stronger assumptions are required to guarantee estimator unbiasedness. These results unify parametric and nonparametric adjustment methods for causal inference and are actualized by the Balance Optimization Subset Selection framework, which identifies the best level of balance that can be achieved within a dataset. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 323–344, 2017     

基于排队论的防空兵力部署优化模型

兵力部署优化问题是防空指控系统的重要研究内容之一。针对多型防空武器多层多区段混合部署问题,对异型空袭武器和防空武器基于作战效能标准化处理,基于排队论给出空袭武器的突防概率计算模型,将防空武器系统综合防御效能作为优化目标,建立多型防空武器扇形部署优化模型。仿真实验验证了优化模型的有效性和合理性,可为科学制定兵力部署方案提供参考。     

基于静止图象的数字水印算法研究

随着因特网和多媒体技术的不断发展,数字媒体已成为人们获取知识的主要手段,但也带来一系列的问题:如作品侵权、非法复制和传播等。而数字水印技术作为版权保护的重要手段,已得到广泛的应用和研究。概述了数字水印技术,并提出了一种新的基于静止图象的数字水印算法。该算法的密钥由原始图象、水印图象和随机数发生器的种子共同决定。而且,该算法能承受有损压缩、旋转、剪切等几种攻击,经实验证明具有良好的鲁棒性。     

线性规划的一种并行修正松弛算法

对求解线性规划问题的松弛算法进行了修正,在此基础上提出了一种基于cluster结构的并行算法,分析了算法的性能;基于曙光3000大规模并行计算机,给出了算法用于求解线性规划问题实例的实验结果.理论分析和实验结果表明,修正算法改进了松弛算法的实际性能,同时具有较好的并行性和稳定性,可用于求解此类大规模科学与工程规划问题的高性能计算.     

给定人员情况下最小化开发时间和成本的软件项目调度

给出了一种软件项目的随机调度模型.它明确地把调度策略作为输入,一旦调度策略确定,模型就可以输出关于项目的完成时间或成本的一个概率分布.利用随机最优化技术,能够计算出软件项目的一个调度策略,它使得项目在人员给定的情况下开发时间和成本最小.