Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm

A weapon target assignment （WTA） model satisfying expected damage probabilities with an ant colony algorithm is proposed. In order to save armament resource and attack the targets effectively, the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted. The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result. Ant colony algorithm has been successfully used in many fields, especially in combination optimization. The ant colony algorithm for this WTA problem is described by analyzing path selection, pheromone update, and tabu table update. The effectiveness of the model and the algorithm is demonstrated with an example.

Dynamic Weapon Target Assignment Based on Intuitionistic Fuzzy Entropy of Discrete Particle Swarm

Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization(IFDPSO) and makes it applied to Dynamic Weapon Target Assignment(WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.

Survey of the research on dynamic weapon-target assignment problem

The basic concepts and models of weapon-target assignment （WTA） are introduced and the mathematical nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The present research on WTA is focused on models and algorithms. In the research on models of WTA, the static WTA models are mainly studied and the dynamic WTA models are not fully studied in deed. In the research on algorithms of WTA, the intelligent algorithms are often used to solve the WTA problem. The small scale of static WTA problems has been solved very well, however, the large scale of dynamic WTA problems has not been solved effectively so far. Finally, the characteristics of dynamic WTA are analyzed and directions for the future research on dynamic WTA are discussed.

An Air Defense Weapon Target Assignment Method Based on Multi-Objective Artificial Bee Colony Algorithm

With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive operations,a reasonable air defense weapon assignment strategy is a key step.In this paper,a multi-objective and multi-constraints weapon target assignment(WTA)model is established that aims to minimize the defensive resource loss,minimize total weapon consumption,and minimize the target residual effectiveness.An optimization framework of air defense weapon mission scheduling based on the multiobjective artificial bee colony(MOABC)algorithm is proposed.The solution for point-to-point saturated attack targets at different operational scales is achieved by encoding the nectar with real numbers.Simulations are performed for an imagined air defense scenario,where air defense weapons are saturated.The non-dominated solution sets are obtained by the MOABC algorithm to meet the operational demand.In the case where there are more weapons than targets,more diverse assignment schemes can be selected.According to the inverse generation distance(IGD)index,the convergence and diversity for the solutions of the non-dominated sorting genetic algorithm III(NSGA-III)algorithm and the MOABC algorithm are compared and analyzed.The results prove that the MOABC algorithm has better convergence and the solutions are more evenly distributed among the solution space.

Improved MOEA/D for Dynamic Weapon-Target Assignment Problem

Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.

ACGA Algorithm of Solving Weapon - Target Assignment Problem

Weapon Target Assignment is not only an important issue to use firepower, but also an important operational decision-making problem. As new intelligent algorithms, Genetic algorithm and ant colony algorithm are applied to solve Weapons-Target Assignment Problem. This paper introduces the Weapon-Target Assignment (WTA) and the mathematical model, and proposes ACGA algorithm which is the integration of genetic algorithm and ant colony algorithm then use ACGA algorithm to solve the Weapon-Target Assignment Problem. Calculations show that: when ACGA algorithm is used to solve Weapon – Target Assignment Problem, it has fast convergence and high accuracy.

面向装甲分队战法运用的两阶段WTA模型

针对目前武器-目标分配模型无法满足装甲分队火力打击战法要求的问题,分析了装甲分队火力打击战法的运用方式及其对分配结果的影响,建立一种面向装甲分队战法运用的两阶段WTA模型,模型第一阶段解决了"单武器高命中概率削弱"的问题,模型第二阶段可以实现装甲分队火力打击战法对于分配结果的多种不同要求。仿真结果证明,该模型解算结果合理,符合装甲分队战法运用要求。

基于遗传算法的联合火力WTA问题研究

研究联合火力优化分配问题。联合火力是多种作战力量参战条件下的多武器-多目标对抗。由于火力分配受到多种条件的限制,分配方案又决定作战效果,而武器-目标分配(Weapon Target Assignment,WTA)是多个装备同类型武器的作战单元联合抗击多个目标进行的分配方法。针对WTA问题求解规模大和精度高的特点,传统算法均不能满足速度和精度的要求,在基本遗传算法的基础上,采用精英选择和动态遗传算子的改进算法,避免了过早收敛。利用WTA的数学模型进行仿真。仿真结果验证了算法的有效性。

基于免疫记忆的蚁群算法的WTA问题求解

武器-目标分配(WTA)是影响武器系统作战有效性的重要因素之一。该文在蚁群算法中增加一个额外的记忆库,利用免疫记忆和克隆选择的思想和方法,提出了基于免疫记忆的蚁群算法(IMBACA),并用于求解武器-目标分配问题。分别用给定数据集和随机数据集的WTA问题进行实验,并与传统蚁群算法和蚁群算法的混合算法进行比较,结果显示IMBACA在解的质量和时间性能上均取得了较好的效果。

用离散粒子群优化算法求解WTA问题

为了提高武器-目标分配(WTA)问题的求解效率和性能,提出了一种用离散粒子群(DPSO)算法求解此问题的新方法.对粒子群算法中的速度和位置进行了重新定义,使其可求解WTA这类离散组合优化问题,并采用贪心的启发式策略对迭代产生的方案进行调整,以利于快速找到最优或次优的分配方案.算法测试表明新算法执行速度快,结果令人满意,提出的算法能快速给出WTA问题的最优或近优分配方案.

武器-目标分配(WTA)问题研究进展

对武器-目标分配(W TA)问题的研究现状与进展进行了总结与述评。介绍了W TA问题的概念、基本模型、数学性质以及W TA问题研究的基本内容。目前W TA问题的研究内容主要集中在模型研究与算法研究两个方面。模型研究以静态模型的研究为主,但动态模型的研究还不够深入;算法研究则主要采用智能算法对W TA问题进行求解。目前基本上已经解决了小规模的静态W TA问题,但尚未有效解决大规模的动态W TA问题。

基于模糊火力适度原则的坦克分队WTA模型研究

为缩短静态WTA模型与坦克分队实际战术运用之间的差距,通过建立打击效率模糊集,对打击效率满意度进行了评判,进而实现了坦克分队战术中的火力适度分配原则。在此基础上,通过对解空间重新编码、采用关系算子,原模型转化成为了无约束优化问题,为模型求解提供了便利。从仿真计算的结果来看,模型的解算结果科学合理。此模型不仅符合坦克分队的战术原则,并且易于算法求解。

求解WTA问题的智能算法评价准则

目前求解火力优化配置(WTA)问题的智能算法层出不穷,急需统一的算法评价准则来比较各算法的寻优能力,以得到相对更高精度、更高效率的算法。针对上述问题建立了完善的评价准则体系,从算法代码编制质量、算法精准度和算法执行效率3个方面对智能算法的寻优能力深入讨论,并给出了2个标准测试模型及其初始化参数。实例说明本评价准则科学合理,具有很高的实用价值。

动态武器目标分配问题的DWTA-GA算法

首先介绍求解静态武器目标分配问题的贪心算法，它是求解动态武器目标分配ＤＷＴＡ（Ｄｙｎａｍｉｃ Ｗｅａｐｅｎ Ｔａｒｇｅｔ Ａｓｓｉｇｎｍｅｎｔ） 问题的基础。然后，笔者提出了基于遗传算法ＧＡ（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍ） 的动态武器目标分配策略ＤＷＴＡ－ ＧＡ。实验结果表明，新提出的基于遗传算法的动态武器目标分配策略是有效的。

解WTA问题群智能优化算法的种群初始化问题研究

为提高解武器—目标分配问题(WTA)的群智能优化算法初始种群性能,从提高初始种群多样性的角度提出了基于密码筒的种群初始化方法,从提高初始种群解质量的角度分别提出了基于目标威胁—打击效果混合排序的种群初始化方法和装甲分队战术种群初始化方法。最后,通过一系列不同规模WTA算例的仿真实验对初始化方法进行评估,结果表明三种种群初始化方法均达到了设计要求,且各具优势。

防空作战WTA问题优化仿真

研究武器-目标分配(Weapon-Target Assignment,WTA)问题,是以消耗最少弹药、拦截最多目标。传统方法采用典型的NP算法,受各种约束条件的限制。为提高WTA问题的求解速度和效益,提出了一种改进变异的直觉模糊差分进化算法。首先,,建立带有资源约束的WTA优化模型;其次,定义了目标函数与约束函数的隶属度和非隶属度函数,将模型转化为直觉模糊优化模型;运用算子k调节差分进化算法前后期的变异率,增强了求解直觉模糊WTA问题的全局和局部寻优能力。最后,通过与DE、Csa DE、AIS算法仿真比较分析,验证了该方法具有较快的寻优速度及较优的效益值。

基于NSGA-Ⅱ的WTA多目标优化(英文)

将多目标遗传算法NSGA-(改进的非支配排序遗传算法)应用于求解武器-目标分配(WTA)问题。首先,针对以往在建立防空型WTA问题的优化模型上的片面性,把WTA问题看做多目标优化问题,建立了综合考虑作战效能和防御效能的WTA双目标优化模型。然后在此基础上,研究和应用了NSGA-来求解WTA问题。最后由仿真算例验证了NSGA-在WTA问题中的应用可行性,表明了NSGA-可以快速地搜索到WTA多目标优化的Pareto最优解集,从而为求解WTA问题提供了一条有效途径。

基于IF-HPSO算法的防空作战WTA问题研究

武器-目标分配问题(WTA)是防空作战指挥决策中的一个关键环节,属于典型的NP难问题。为提高其求解速度和精度,提出一种基于直觉模糊的混合粒子群优化算法(IF-HPSO)。首先,以弹药消耗最小、拦截率最大为目标函数,建立火力资源受限条件下WTA的优化模型;采用实数编码,引入直觉模糊领导力函数选择出较优粒子参与速度更新;定义种群同构因子函数来调节惯性权重和学习因子;此外,将基于精英保留策略的遗传操作混合PSO算法进行迭代寻优,加快收敛速度;最后,对WTA问题实例进行仿真并将所提算法与AIA,GA,HDPSO算法进行比较,结果表明所提算法具有更快的寻优速度和更高的求解精度。

改进遗传算法求解防空作战WTA问题

武器目标分配问题是防空作战指挥控制的核心和关键。针对求解防空作战WTA存在容易早熟和收敛较慢的问题,提出了一种改进遗传算法。引入直觉模糊集理论,定义了WTA问题的目标函数和约束函数的隶属度和非隶属度函数,通过"最小最大"算子构建了直觉模糊WTA问题模型;针对遗传算法中变异概率固定的竞争和子代种群缺乏父代优良个体的问题,采用自适应变异概率和模拟退火Meta-Lamarckian学习策略改进算法,并求解防空作战WTA问题,与其他算法进行仿真比较,结果表明改进遗传算法求解防空作战WTA的有效性。

基于直觉模糊熵的改进粒子群算法求解WTA问题

为了提高求解武器目标分配问题的效率和性能,提出了一种基于直觉模糊熵的改进粒子群算法(IFEIPSO)。首先,针对WTA问题的多约束条件建立了整数编码方案,降低了问题的复杂性;其次,采用一种交换操作和模拟退火机制对粒子群算法的局部最优解进行更新,从而得到更优的局部最优解和全局最优解,以增加算法的局部搜索能力;最后,以直觉模糊熵作为种群多样性的测度,根据熵值大小对种群进行变异操作,提高种群的多样性,增加算法的全局搜索性能。仿真实验结果表明,该算法很好地提高了粒子群算法的寻优能力,有效地解决了WTA问题。