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.

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.

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.

基于强化学习的多对多拦截目标分配方法

针对空中对抗环境中多对多拦截的武器目标分配问题,提出了一种基于强化学习的多目标智能分配方法。在多对多拦截交战场景下,基于交战态势评估构建了目标分配的数学模型。通过引入目标威胁程度和拦截有效程度的概念,充分反映了各目标的拦截紧迫性和各拦截器的拦截能力表征,从而全面评估了攻防双方的交战态势。在目标分配模型的基础上,将目标分配问题构建为马尔可夫决策过程,并采用基于深度Q网络的强化学习算法训练求解。依靠环境交互下的自学习和奖励机制,有效实现了最优分配方案的动态生成。通过数学仿真构建多对多拦截场景,并验证了该方法的有效性,经训练后的目标分配方法能够满足多对多拦截中连续动态的任务分配要求。

基于改进差分进化算法的跨平台武器目标分配方法

现代战争中,跨平台武器单元的协同利用,是合同编队体系的重要内容,作战方式也正由平台级协同向着能力要素级协同转变,这对武器目标分配问题的解决提出了更大挑战。本文将武器单元的最小划分单位细化到能力要素级,以毁伤概率与成本消耗为优化目标,面向多种来袭目标的编队防空场景,提出了跨平台武器目标分配算法。同时,基于混沌映射提出了混沌种群重构(chaotic population reconstruction,CPR)机制,并结合带存档的自适应差分进化(adaptive differential evolution with optional external archive,JADE)算法提出了CPR-JADE算法,利用CPR机制可以帮助算法在解决高维复杂约束问题时跳出局部最优。再将其运用到武器目标分配模型上,实现了对模型的高效求解。最后,通过在多种数据规模下与其他进化优化算法的仿真对比试验分析,验证了所提方法的正确性与有效性。

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。

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.

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。

基于多目标进化算法的防空导弹武器目标分配

有效的武器目标分配(weapon-target assignment,WTA)方法对减少作战损失,提高防御效果具有重要意义。针对防空资源分配问题建立合理的数学模型,以最大化目标毁伤效能和最小化雷达资源消耗为优化目标,同时考虑雷达通道数上限等多个约束,在基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)基础上进行改进,种群进化过程中自适应调整交叉与变异的概率以提高个体的质量,最终得到一组可供决策者使用的最优解集。实验结果表明:与其他多目标进化算法相比,该算法能得到适应度更高且分布性良好的结果,能够为防空导弹武器目标分配问题提供可行方案。

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。

多模式融合的武器目标分配系统模型设计

目标分配是指挥控制流程中的核心环节,分配模式的优化对于提升防空反导作战能力具有重要意义。为提高防空反导目标分配的鲁棒性、适用性以及博弈对抗性等作战性能,以应对当前复杂多变的战场环境,本文提出建立多模式融合的目标分配体系结构,对商用订单式的服务模式进行适应性改进,将“派单”“抢单”“抢派单融合”三种模型改进定义为军事模型;通过使用美国国防部体系结构框架(Department of Defense Architecture Framework,DoDAF)建立“他分配”“自分配”“他分配与自分配结合”的新型目标分配体系结构;引入Perti网模型,构建并分析Petri网模型的可达图,通过仿真实验平台,构建复杂作战场景,验证了分配策略机制的可行性。结果表明三种策略各具优势,本文设计的多策略结合在匹配时间、成功率以及效用值方面相较传统的单一策略具有较大优势。

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。

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。

基于人工蜂群算法的大规模武器目标分配研究

针对大规模武器目标分配问题,本文提出一种改进的多目标武器目标分配模型,该模型将武器平台泛化为武器,并将武器平均飞行时间作为第2个优化目标。为有效解决这类问题,本文还提出了改进的自适应离散多目标人工蜂群算法。该算法基于人工蜂群算法和非支配排序策略,引入了自适应算子操作数、重用蜜源探索信息的变异概率策略,并通过蜜源之间、蜜源与外部解集之间的交互以提高算法的收敛性,通过算子的随机选择保持种群多样性。最后通过不同规模武器目标分配的对比实验,证明了所提自适应算子操作数与重用蜜源探索次数的变异概率策略的有效性,并与MOABC、MOPSO、NSGA-II算法在反向世代距离、超体积、时间3个方面进行比较,本文算法能够在保证时效性的前提下得到质量更好的Pareto解集。

基于改进离散狼群算法的火力分配

随着无人机和智能弹药协同的兴起,进行高效的火力分配成为研究重点。传统的数学模型过于单一,不满足空对地合理分配需求;目前常用的元启发算法迭代较慢且容易陷入局部最优解。为了解决火力分配问题,提出了一种新的数学模型和改进离散狼群算法。模型兼顾摧毁目标价值最大化、己方损耗最小化、弹药协同打击;引入莱维飞行策略、汉明距离和编码段替换改进狼群算法中的智能行为。通过对比仿真,验证了新算法的高效性和可行性。

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。