C3I时隙分配优化设计

用线性规划方法讨论了Ｃ３Ｉ通信网络中时隙分配的优化设计问题     

高级程序设计语言综述

为读者选择语言提供了总体思路,给出了高级语言的发展的趋势。     

Otsu算法在Canny算子中的应用

Canny算子应用于细胞图像分割,只要选择适当的参数就可以取得好的分割效果。在分析细胞图像直方图后,利用类间方差最大化阈值分割算法(Otsu)可以实现Canny算子中门限值的自适应选择。试验结果表明,Otsu算法应用于Canny算子中的门限选择,改善了算法在细胞图像中的分割效果,提高了算法的自适应性能。     

引入优化算子的无人机遗传航路规划算法研究

在大范围真实地形环境中规划出满足任务要求、导航、安全性等约束的较优航路,对提高无人机(UAV)的武器系统性能有重要意义。在基于直接航路编码的遗传算法中引入优化操作算子,将复杂约束合理地应用于遗传进化过程,可以较好地克服一般遗传操作的不足。采用均匀设计的试验方法确定较优的控制参数,并通过算例验证了算法的有效性。     

基于CZT算法的水声扩频多普勒匹配方法

阐述了基于DS/SS(直接序列扩频)的高分辨率扩频自导声纳系统的一种多普勒匹配方法。通过理论分析和仿真计算,证明基于CZT多普勒频偏匹配算法可以在极低信噪比条件下有效估计多普勒频偏和目标速度。     

最大面积避障矩形问题的解法

建立了在一个含有若干障碍点的矩形母域内求一个最大面积避障矩形问题的数学模型.通过分析避障矩形的几何特性,找到了求解这一含有0-1变量的非线性规划数学模型的一种特殊解法.     

Cyclic best first search: Using contours to guide branch‐and‐bound algorithms

The cyclic best‐first search (CBFS) strategy is a recent search strategy that has been successfully applied to branch‐and‐bound algorithms in a number of different settings. CBFS is a modification of best‐first search (BFS) that places search tree subproblems into contours which are collections of subproblems grouped in some way, and repeatedly cycles through all non‐empty contours, selecting one subproblem to explore from each. In this article, the theoretical properties of CBFS are analyzed for the first time. CBFS is proved to be a generalization of all other search strategies by using a contour definition that explores the same sequence of subproblems as any other search strategy. Further, a bound is proved between the number of subproblems explored by BFS and the number of children generated by CBFS, given a fixed branching strategy and set of pruning rules. Finally, a discussion of heuristic contour‐labeling functions is provided, and proof‐of‐concept computational results for mixed‐integer programming problems from the MIPLIB 2010 database are shown. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 64–82, 2017     

Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty

We propose a novel simulation‐based approach for solving two‐stage stochastic programs with recourse and endogenous (decision dependent) uncertainty. The proposed augmented nested sampling approach recasts the stochastic optimization problem as a simulation problem by treating the decision variables as random. The optimal decision is obtained via the mode of the augmented probability model. We illustrate our methodology on a newsvendor problem with stock‐dependent uncertain demand both in single and multi‐item (news‐stand) cases. We provide performance comparisons with Markov chain Monte Carlo and traditional Monte Carlo simulation‐based optimization schemes. Finally, we conclude with directions for future research.     

Exact algorithms and bounds for the dynamic assignment interdiction problem

We study a multi‐stage dynamic assignment interdiction (DAI) game in which two agents, a user and an attacker, compete in the underlying bipartite assignment graph. The user wishes to assign a set of tasks at the minimum cost, and the attacker seeks to interdict a subset of arcs to maximize the user's objective. The user assigns exactly one task per stage, and the assignment costs and interdiction impacts vary across stages. Before any stage commences in the game, the attacker can interdict arcs subject to a cardinality constraint. An interdicted arc can still be used by the user, but at an increased assignment cost. The goal is to find an optimal sequence of assignments, coupled with the attacker's optimal interdiction strategy. We prove that this problem is strongly NP‐hard, even when the attacker can interdict only one arc. We propose an exact exponential‐state dynamic‐programming algorithm for this problem as well as lower and upper bounds on the optimal objective function value. Our bounds are based on classical interdiction and robust optimization models, and on variations of the DAI game. We examine the efficiency of our algorithms and the quality of our bounds on a set of randomly generated instances. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 373–387, 2017     

多机协同目标与火力资源分配

在协同空战中,快速正确的空战决策是己方战机少受敌方伤害并取得战争胜利的前提。目标与火力资源分配是决策过程的重要部分。多机空战与单机空战相比有明显的不同,不同之处是面临多个敌方目标,根据我方资源最优分配作战对象和火力,基于遗传算法实现了两种算法的有人无人目标与火力资源分配。仿真结果表明,带有毁伤概率门限的算法既节省火力资源又快速有效。