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。

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。

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。

基于粒子群遗传禁忌的武器目标分配优化算法

针对传统的粒子群算法与遗传算法在解决武器目标分配优化时存在收敛精度不高的问题,提出了一种基于粒子群遗传禁忌的武器目标分配优化算法WTAO-PGT。通过引入自适应选择比例调控种群多样性,并改进遗传算法的交叉、变异算子以及禁忌搜索的邻域动作,使算法具有较强跳出局部极值的能力。仿真实验结果表明,所提算法在收敛精度上较粒子群禁忌混合搜索等算法有较大提升。

空地多目标攻击武器-目标分配与制导序列优化

武器-目标分配(WTA)与空地导弹的接力制导规划是远距离空地多目标攻击中亟需解决的难题,具有参数复杂、约束多、非线性强等特点。为此,建立多目标、多约束武器-目标分配与制导序列优化模型,优化目标为目标综合生存概率最小和总用弹量最少,约束条件涉及攻击机导弹配置、导弹毁伤能力、目标毁伤要求、制导站性能。对带精英策略的非支配排序遗传算法(NSGA-Ⅱ)进行改进,提出基于双序列编码的多种群NSGA-Ⅱ(DSMPNSGA-Ⅱ),通过优化WTA序列和制导站序列,实现WTA方案及各导弹制导序列的优化。在DSMPNSGA-Ⅱ中,使用深度优先搜索-Dijkstra算法搜索空地导弹制导序列,改进交叉和变异操作,以减少非可行解的产生,引入多种群策略提升算法性能。仿真结果表明,DSMPNSGA-Ⅱ能够获得有效的WTA与空地导弹接力制导方案,并且求解质量优于单种群NSGA-Ⅱ和多目标粒子群优化算法。

武器目标分配问题研究进展:模型、算法与应用

武器目标分配问题是指挥控制与任务规划领域的关键难点之一,也是军事运筹领域的基础研究课题。经过多年研究,武器目标分配问题在陆海空天电等领域都得到了广泛研究,涌现出了大量模型和算法。系统梳理武器目标分配问题的典型作战样式、建模方法、求解算法和实验验证,掌握当前该领域的研究现状,在此基础上,结合智能化、无人化战争带来的新挑战,分析武器目标分配的发展趋势,为后续研究提供参考。

基于匈牙利-模拟退火算法的多阶段武器目标分配方法

针对现代化战争中远程精确制导武器成本高昂以及武器发射平台数量有限的问题,研究了多阶段武器目标分配问题,以最小化武器成本为优化目标,建立了混合整数非线性规划模型。设计了一种集成匈牙利和模拟退火的混合智能搜索算法,首先根据待攻击目标的时空状态与毁伤特性确定打击阶段、标记可松弛性。在模拟退火的框架下,基于目标的松弛性调整攻击阶段,再通过匈牙利算法计算每个阶段武器和目标的精确匹配方案。通过启发式算法和精确求解技术的结合,在保证求解质量的前提下,极大减少了计算时间。通过计算实验,与变邻域搜索算法进行了对比分析,验证了该算法在求解多阶段武器目标分配问题的有效性。仿真结果表明,所提算法在计算时间和求解效果上优于变邻域搜索算法。

基于粒子群优化的多导弹动态武器目标分配算法

针对复杂多变的未来战场环境,对空防御系统需要实现对多个目标进行武器分配。由于传统静态武器目标分配(Static weapon target assignment,SWTA)模型受到很多因素的限制,无法适应战场态势的快速变化。为了解决多导弹的动态武器目标分配(Dynamic weapon target assignment,DWTA)问题,将对空防御过程离散为多个阶段,并根据战场实时态势数据构建了DWTA的数学模型,提出了一种改进的粒子群优化算法,引入了武器转火时间窗等约束条件,在算法中考虑拦截概率和导弹耗费等多个指标。最后通过大量仿真实验,验证了粒子群算法进行多导弹目标分配的合理性和有效性。

改进蛙跳算法求解武器目标分配问题

针对武器目标分配问题,提出一种改进蛙跳算法来求解空间受限的武器目标分配。首先,基于武器目标分配原则建立多约束条件下武器目标分配模型,并将多目标优化问题转化为单目标优化问题;其次,采用基于非支配等级和拥挤度因子的精英选择策略改进初始种群的多样性和均匀度,提升算法最优解的质量;最后,通过合理的想定背景进行仿真计算,结果表明:该方法可有效平衡搜索时间和全局最优解质量,可作为编队防空作战时武器目标分配的一个不错选择,通过与SFLA算法和遗传算法进行比对分析,表明该算法相对SFLA算法求解的最优解质量高,相对遗传算法搜索效率高。

基于SDP的动态武器目标分配决策算法研究

武器目标分配是水面舰艇防空反导作战指挥决策的核心环节,决策结果的优劣直接影响了防空反导作战的最终防御效果与水面舰艇的生存能力。针对当前武器目标分配算法较少考虑拦截结果的不确定性和多梯次拦截的序贯分配需求,难以适应战场动态变化的问题,设计一种基于随机动态规划的动态武器目标分配决策算法。通过进制变换编码并计算目标状态和武器状态的变化规律,基于战场态势转移概率采用逆向迭代算法生成序贯拦截方案。最终精确求解面向多梯次拦截的动态武器目标分配最优策略,回答“谁来打”“打多少”和“何时打”的水面舰艇防空反导作战决策问题。通过算例验证和数值仿真表明,使用该算法能够获得比静态武器目标分配算法更好的拦截效能,在最好情况下能够降低空中来袭目标72%的威胁期望。

基于改进人工蜂群算法的地面作战武器-目标分配

为了提高地面分队火力分配的科学性,构建基于打击效益的地面作战武器目标分配(WTA)模型,针对战场上打击的收益和代价,制定合理的优化函数。通过对目标的威胁评估,判定打击的迫切性和必要性;通过对目标的战场价值判断,计算毁伤价值收益;通过目标对武器的毁伤概率,预估我方武器的损失;通过分析敌我兵力对比和弹药储备情况,量化弹药的价值;通过分析敌我双方的战术意图,衡量打击的必要性和战术意图暴露的代价。针对WTA模型求解问题,提出一种改进人工蜂群算法,以提高算法的搜索方向性和迭代后期跳出局部最优能力,同时采用基于武器目标组合库的种群初始化策略,提高了算法初期种群质量。仿真算例表明了所提模型的科学性以及新算法在收敛速度、收敛精度和鲁棒性方面的优势。