改进的微分进化算法求解空战火力分配问题

为了提高空战火力分配问题的求解性能,提出了一种新的基于微分进化算法的求解方法.首先介绍了基本微分进化算法求解火力分配问题的思路;然后指出了基本微分进化算法存在的不足,并进行了必要的改进;接着给出了改进的微分进化算法求解空战火力分配问题的一般流程;最后进行了遗传算法、基本微分进化算法对比测试,结果表明改进的微分进化算法对于求解空战火力分配问题更加有效.     

匈牙利算法在多目标分配中的应用

在多目标攻击决策中 ,根据 Harold提出的目标优势函数 ,分析了使所有目标机的总优势函数为指派问题 ,运用匈牙利算法对 n对 n的最优目标分配指派问题进行求解 ,并把它推广至 n对 m的多目标分配中。仿真结果表明匈牙利算法对于此类多目标分配指派问题的求解是十分有效的。     

改进的人工蜂群算法求解武器目标分配问题

为了提高武器目标分配问题求解的效率与性能,提出一种求解武器目标分配问题的改进人工蜂群算法。针对武器目标分配问题模型的离散性特点,设计了解的编码方案,保证种群个体编码满足约束条件;通过控制种群编码熵的大小保证了初始化种群的离散性,加强了种群前期搜索的多样性;引领蜂采用同时保留最优蜜源与次优蜜源的方式,增大了种群局部寻优能力。仿真结果表明,在求解武器目标分配问题时,改进蜂群算法与传统优化算法相比收敛速度更快,求解精度更高,具有很好的应用价值。     

基于小生境蝙蝠算法的联合远程打击武器目标分配问题建模与求解

针对联合远程打击作战筹划中武器目标分配问题,使用数学建模与仿真分析相结合的方法,研究了联合远程精确打击武器目标分配基本原则,构建了武器目标分配问题的多目标优化数学模型;对目标函数和约束条件进行处理,将该模型转化为单目标优化问题;提出了一种结合小生境淘汰思想的改进蝙蝠算法,用来求解武器目标分配的近似最优解。实验分析表明:该算法能够有效改善蝙蝠算法的收敛特性,适用于联合远程打击作战武器目标分配问题的求解。     

系统可靠性分配的研究进展

系统可靠性分配是可靠性设计的重要任务之一,其主要作用是为可靠性设计提供辅助决策。给出了可靠性分配流程,研究了可靠性分配模型的发展状况,重点介绍了可靠性分配问题的求解算法,指出了各类算法在分配中的应用及其需进一步解决的问题,同时分析了国内外典型的分配软件,总结了各软件中可靠性分配的主要功能,最后对可靠性分配提出了进一步的研究展望。     

模糊优化理论的武器-目标分配模型及求解算法

武器-目标分配问题是一个典型的NP完全问题,随着武器和目标数量的增多,以及分配结果评价标准多样性的存在,传统的优化求解算法如隐枚举法、割平面法、分支定界法等很难进行有效地求解。介绍一种基于模糊优选技术的多目标混合优化理论,运用该理论建立了多个指标下的最佳武器-目标分配模型,并将蚁群算法应用于对模型的求解,为解决复杂的武器-目标分配问题提供了一种有效方法。     

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.     

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。     

人工免疫算法在火力分配上的应用

人工免疫算法是新兴的智能计算方法,已被用于解决网络安全、模式识别、函数优化、遗传算法等领域的问题.为解决传统火力分配模型不易求解的问题,将人工免疫算法引入其中.简要分析了火力分配问题的数学模型,采用类比的方法构造了相应的人工免疫算法,比较了人工免疫算法和遗传算法的异同,并用人工免疫算法求解了该问题.结果表明该方法计算速度快、精度高,为解决火力分配问题提供了参考.     

联合防空目标分配方案优化研究

目标分配是联合防空作战指挥决策的核心内容,是各级联合防空指挥中心的重要工作,其模型的合理性与可信性会对作战结局产生重大影响。为提高目标分配的合理性与可信性,对联合防空作战混合部署下的目标优化分配问题进行了详细分析,建立了相应的数学规划决策模型。为了有效获得问题的全局最优解,将改进遗传算法(GA)应用于数学规划决策模型的求解过程中,并给出了模型求解的方法和步骤,经实例应用,取得了满意的结果。     

Flowshop sequencing problem with ordered processing time matrices: A general case

The ordered matrix flow shop problem with no passing of jobs is considered. In an earlier paper, the authors have considered a special case of the problem and have proposed a simple and efficient algorithm that finds a sequence with minimum makespan for a special problem. This paper considers a more general case. This technique is shown to be considerably more efficient than are existing methods for the conventional flow shop problems.     

Optimal selection from a random sequence with observation errors

A form of sequential decision problem is introduced in which options are presented in sequence. with no recall of rejected options (as in the secretary problem), but in which the value of each option may only he inferred from experiments. Decisions have thus to be made concerning both the acceptance and rejection of each option and the degree of experimentation. General properties of the optimal policy are derived, and an algorithm is obtained for the solution in a special case. This special case suggests a heuristic rule for more general situations. the performance of which rule has been investigated by a Monte Carlo study.     

A special case of the 3 × n flow shop problem

Johnson [2] in 1954 solved the two machine flow shop problem by giving an argument for a sufficient condition of optimality and by stating an efficient algorithm which produces a solution via satisfaction of the sufficient condition. Moreover, Johnson solved two special cases of the corresponding three machine flow shop problem. Since that time, six other special cases have been solved, two contributed by Arthanari and Mukhopadhyay [1], two by Smith, Panwalkar, and Dudek [3], and two of a different nature by Szwarc [5]. This paper contributes an extension to one of the classes described by Szwarc.     

An efficient heuristic procedure for the capacitated warehouse location problem

This paper introduces an efficient heuristic procedure for solving a special class of mixed integer programming problem called the capacitated warehouse (plant) location problem. This procedure parallels the work reported earlier in [9] on the uncapacitated warehouse location problem. The procedure can be viewed as tracing a judiciously selected path of the branch and bound tree (from the initial node to the terminal node) to arrive at a candidate solution. A simple backtracking scheme is also incorporated in the procedure to investigate possible improvement in the solution. Computational results on problems found in the literature look quite encouraging.     

零扰动下一类RN 上临界增长的椭圆方程正解的存在性

本文讨论了N维欧氏空间R~N上一类临界增长的拟线性椭圆型方程—div(|Du|~(p-2)Du)+k(x)u~)p-1)=K(x)U~(p-1),u∈W~(1,p)(R~N)∩L~p(R~N)的正解的存在性。其中4≤p~2≤N,p=Np/(N—p)。在微分几何与物理学等领域起重要作用的Yamabe问题就是其特例(p=2)。本文运用集中紧引理,证明了问题的正解的存在性。     

An easy solution for a special class of fixed charge problems

The fixed charge problem is a mixed integer mathematical programming problem which has proved difficult to solve in the past. In this paper we look at a special case of that problem and show that this case can be solved by formulating it as a set-covering problem. We then use a branch-and-bound integer programming code to solve test fixed charge problems using the setcovering formulation. Even without a special purpose set-covering algorithm, the results from this solution procedure are dramatically better than those obtained using other solution procedures.     

A primal simplex algorithm to solve a rectilinear distance facility location problem

This paper considers the problem of locating m new facilities in the plane so as to minimize a weighted rectangular distance between the new facilities and n existing facilities. A special purpose primal simplex algorithm is developed to solve this problem. The algorithm will maintain at all times a basis of dimension m by m; however, because of the triangularity of the basis matrix, it will not be necessary to form a basis inverse explicitly.     

Solving generalized transportation problems via pure transportation problems

This paper investigates certain issues of coefficient sensitivity in generalized network problems when such problems have small gains or losses. In these instances, it might be computationally advantageous to temporarily ignore these gains or losses and solve the resultant “pure” network problem. Subsequently, the optimal solution to the pure problem could be used to derive the optimal solution to the original generalized network problem. In this paper we focus on generalized transportation problems and consider the following question: Given an optimal solution to the pure transportation problem, under what conditions will the optimal solution to the original generalized transportation problem have the same basic variables? We study special cases of the generalized transportation problem in terms of convexity with respect to a basis. For the special case when all gains or losses are identical, we show that convexity holds. We use this result to determine conditions on the magnitude of the gains or losses such that the optimal solutions to both the generalized transportation problem and the associated pure transportation problem have the same basic variables. For more general cases, we establish sufficient conditions for convexity and feasibility. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 666–685, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10034     

Cinema,drone warfare and the framing of counter-terrorism

The study of the cinematic representation is extremely useful in framing of counter-terrorism policies, whether in the US or elsewhere. This paper examines cinema’s interest in drone warfare as well as the lives and personalities of drone pilots. It argues that drone warfare suffers a considerable image problem that has been brought out in several recent features and it is unlikely that any major cinematic myth of drone warfare will easily develop, certainly in comparison to myths concerning special forces and special operations.     

Time minimizing flows in directed networks

An important class of network flow problems is that class for which the objective is to minimize the cost of the most expensive unit of flow while obtaining a desired total flow through the network. Two special cases of this problem have been solved, namely, the bottleneck assignment problem and time-minimizing transportation problem. This paper addresses the more general case which we shall refer to as the time-minimizing network flow problem. Associated with each arc is an arc capacity (static) and a transferral time. The objective is to find a maximal flow for which the length (in time) of the longest path carrying flow is minimized. The character of the problem is discussed and a solution algorithm is presented.