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.

Evolutionary decision-makings for the dynamic weapon-target assignment problem

The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general ＂virtual＂ representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.

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。

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。

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。

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。

基于改进NSGA-III算法的多无人机协同目标分配

武器-目标分配问题是战场环境下无人机对敌方执行打击任务的关键,其目的是基于目标的威胁、价值和我方武器的毁伤概率,寻找合理的武器目标分配方案,以提高作战效率。针对当前多目标优化算法解决静态武器-目标分配问题时收敛速度慢、收敛稳定性差,难以适应当前战场高度实时性的问题,提出一种改进的基于参考点的非支配排序遗传算法。通过二进制编码打击方案并优化初始种群,引入自适应变异与交叉策略以及种群寻优更新策略,基于对战场态势进行评估得到的威胁矩阵和优势矩阵,种群多次迭代后生成目标打击方案。最后计算满足约束条件的Pareto解集,并将Pareto前沿中的相对最优解作为多无人机的打击方案。多次实验证明,在较好情况下改进算法相比于原始算法的收敛时间减少46.74%,目标威胁值降低50.5%,总飞行航程减少26.46%,杀伤目标数增加11.76%,证明该算法在解决多无人机空对地打击任务目标分配问题时具有合理性和高效性。

基于指数型累积毁伤律的武器目标分配问题快速寻优

针对联合火力打击背景,基于考虑毁伤累积效果的指数型累积毁伤律,建立了多发次武器对目标的毁伤能力量化函数,给出了相应武器目标分配问题的数学模型。为提高求解效率和性能,根据个体的适应度值,采用自适应调整交叉率和变异率策略的自适应遗传算法,解决标准遗传算法早熟、收敛速度慢的缺点。针对包括5种武器类型(共41发)和6个目标的具体实例,采用自适应遗传算法进行求解仿真,仿真结果表明:与标准遗传算法相比,自适应遗传算法能求解出更优、更收敛的近似最优解,能实现联合火力打击场景下考虑毁伤累积的武器目标分配问题的快速寻优。

基于合同网协议的分布式反导资源分配算法

资源分配算法是实现一体化防空反导的关键技术。针对弹道导弹防御过程中“传感器-武器-目标”动态分配问题,提出一种基于合同网协议的分布式分配方法。首先,对“传感器-武器-目标”动态分配问题进行形式化建模,模型考虑传感器和武器空间能力、传感器制导通道和武器拦截弹数量等实际约束,并设计目标函数以实现弹道导弹防御中尽早拦截和尽可能提高拦截成功概率这两个主要原则。然后,在合同网协议框架的基础上,构建反导资源协同动态分配流程,并针对传感器和武器的任务特点分别设计反导资源投标与中标策略。其中,武器资源投标策略设计中考虑对已分配目标的置换,并在估计被置换对象重招标所带来效能损失的基础上提出基于校正效能的置换对象选择方法。计算机仿真实验结果表明,所提算法能够实现分配方案的动态调整,且与传统反导体系下的分配算法相比,对导弹的拦截时间更早,成功概率更高,平均反导效能提高43.7%。

面向动态毁伤概率和目标价值的武器目标分配方法

面向毁伤和目标价值动态变化条件下的大规模武器目标动态分配问题,提出了一种基于深度强化学习的武器目标分配求解方法。该方法采用双神经网络结构,基于武器目标分配目标函数设计了一套简单、直观的状态与奖励建模方法。通过仿真实验对所提方法进行了验证,结果表明,所提方法能够较快实现收敛,且整体毁伤和计算效率上优于基于粒子群的方法。所提方法能够有效应对毁伤概率和目标价值动态变化条件下的武器目标分配问题,说明了其良好的拓展性。该方法可应用于作战任务规划、仿真单元自动交火等场景下的武器目标分配快速求解。

基于遗传蚁群算法的武器目标分配优化方法

针对演化算法求解武器目标分配求解精度不高的问题,提出了结合蚁群优化的遗传蚁群算法。使用混沌映射随机初始化方法,通过逻辑混沌映射初始化种群,提升初始种群的多样性;通过多样性交换策略,交换染色体部分区域值,进一步提高算法的多样性;基于蚁群优化思想,提出蚁群重组准则,利用蚂蚁的搜索行为增强算法的收敛性。使用12个测试用例进行消融实验,证明了各策略的有效性;与7种典型方法进行对比实验,验证了所提方法的优越性。

基于加权窗口特征的多目标火力集中分配和动态调整算法

针对现有的火力打击规划方法在战前筹划时存在局部收敛性和全局优化能力差、缺乏对战中调整的临机处理和时效性考虑等问题,提出了基于加权窗口特征的火力集中分配和动态调整算法。首先,建立了基于加权窗口特征的分配模型以量化打击效益和资源代价,从而适应对不同作战需求的火力分配;然后,针对算法参数敏感和先验知识缺失的问题,提出了一种自适应参数的基于密度的聚类算法,可自适应寻优参数选择;最后,考虑到战中调整的实时性需求,提出了改进匈牙利算法以减少代价矩阵的迭代次数。试验表明,该算法能够有效解决战前规划和战中调整问题,且在大规模数据集上表现出比其他方法更加优越的性能,验证了该算法的有效性和灵活性。

空面多目标攻击中武器-目标最优分配

在多目标空对地攻击中,合理高效的武器-目标分配能显著提升飞机的攻击效率,降低飞机在战场滞留的时间,对于提升飞机的作战效率与生存性能有着重要的意义。本文基于攻击模式、目标毁伤概率、武器成本、飞行航路等约束,构建武器-目标最优分配模型,设计了一种强化局部搜索能力的自适应遗传算法,解决了求解复杂规划问题时的过量冗余迭代与停滞问题。对多组随机目标进行了仿真验证,仿真结果证明,改进算法能够为作战任务提供优质的武器-目标分配方案,且明显提升了求解的运行效率与质量。

基于模糊多目标规划的防空反导火力分配

针对现有多目标火力分配(weapon target assignment,WTA)方法很难适用于不确定情况下防空反导作战的问题,提出了基于模糊多目标规划的防空反导WTA方法。首先,采用三角模糊数刻画不确定的目标威胁度,在考虑防空反导作战特点的基础上,基于模糊多目标规划建立了WTA模型;然后,根据必要性测度原理将含有模糊参数的目标函数进行了等价清晰化;接着,提出了具有单/双势阱的多目标量子行为粒子群算法用于求解WTA模型,该算法采用了单/双势阱位置更新方式、粒子混合随机变异方法、领导粒子两阶段选取方法;最后,通过实例仿真验证了模型的合理性和算法的有效性。