A branch‐and‐price algorithm for a targeting problem

In this paper, we consider a new weapon‐target allocation problem with the objective of minimizing the overall firing cost. The problem is formulated as a nonlinear integer programming model, but it can be transformed into a linear integer programming model. We present a branch‐and‐price algorithm for the problem employing the disaggregated formulation, which has exponentially many columns denoting the feasible allocations of weapon systems to each target. A greedy‐style heuristic is used to get some initial columns to start the column generation. A branching strategy compatible with the pricing problem is also proposed. Computational results using randomly generated data show this approach is promising for the targeting problem. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

人工免疫在军事领域的应用

人工免疫学是继神经网络、进化计算之后新的智能计算研究领域和研究热点.为探讨人工免疫可运用的军事领域,给相关科研工作者提供新方法、新思路,对生物免疫系统的一些概念进行了介绍,简要分析了生物免疫系统中的免疫机制,人工免疫系统基本特征,然后着重分析了该学科在信息系统安全、防御系统设计、目标识别、故障诊断、优化、火力分配等方面中的应用.     

改进遗传算法的导弹目标分配方法

针对遗传算法在解决导弹目标分配问题中的困难,将火力单位的目标分配问题变换为针对导弹的目标分配问题,之后应用遗传算法求解,求解后还原为火力单位的分配结果,并用实例验证了应用遗传算法的可行性.之后,引入"优势"基因对标准遗传算法进行改进,大量实例表明,改进后的算法提高约60%的搜索效率.     

Graph distance‐dependent labeling related to code assignment in computer networks

For nonnegative integers d₁, d₂, and L(d₁, d₂)‐labeling of a graph G, is a function f : V(G) → {0, 1, 2, …} such that |f(u) − f(v)| ≥ d_i whenever the distance between u and v is i in G, for i = 1, 2. The L(d₁, d₂)‐number of G, λ(G) is the smallest k such that there exists an L(d₁, d₂)‐labeling with the largest label k. These labelings have an application to a computer code assignment problem. The task is to assign integer “control codes” to a network of computer stations with distance restrictions, which allow d₁ ≤ d₂. In this article, we will study the labelings with (d₁, d₂) ∈ {(0, 1), (1, 1), (1, 2)}. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

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     

基于改进鸽群优化的直升机协同目标分配

针对多直升机协同目标分配问题,建立了基于敌我相对态势的对地打击多目标的分配模型。引入多Agent系统机制,加快了有益信息在系统中的流动;并借助雁群成员在飞行中借鉴雁群整体经验的操作,来修正鸽群算法中地标算子的寻优方向,进而提出了基于多Agent的改进鸽群优化的问题求解策略。最后,以典型的直升机对地作战目标分配问题为背景,仿真验证了模型及其求解算法的合理性和有效性。     

预警机指挥多机群空战的协同战术决策

分析了预警机指挥下的多机群协同空战的战术决策方法,建立起了它的战术编队、威胁评估以及目标分配模型,并进行了仿真计算,结果表明此战术决策方法对于提高多机群协同空战的作战效能有着显著的作用,对于未来无人作战飞机多编队协同作战也具有很好的参考价值.     

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.     

高炮群火力优化分配的指派模型

火力优化分配是高炮群指挥控制的关键环节。依据有严格理论证明和实用价值的指派问题及其解法(匈牙利法),构建了一对一射击和多对一射击情形下的高炮群火力优化分配的指派模型,探讨了该模型的求解,分析了该模型的实际应用,并通过具体实例对该模型进行了初步验证。该模型的解法被证明是多项式时间算法,满足作战实时性要求。实际应用表明该模型用于解决高炮群的火力分配问题是有效的。     

一种改进的量子粒子群算法

量子粒子群算法是将量子计算与粒子群算法相结合的一种新的优化方法。首先利用相位角进行实数编码,将动态量子旋转门引入到粒子群算法中,采用自适应变异,提出了一种改进的量子粒子群算法。然后运用Pe-nalized函数和Ackley函数测试了该算法的性能。最后将该算法应用到武器目标分配模型中,获得了最优的分配方案。仿真研究表明,该算法具有收敛速度快、搜索能力强和稳定性高的特点。