As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UA...As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.展开更多
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 ...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.展开更多
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 base...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.展开更多
The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-d...The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.展开更多
Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinfo...Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinforcement learning theory,an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed,which uses a double noise mechanism to expand the search range of the action,and introduces a priority experience playback mechanism to effectively achieve data utilization.Finally,the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield.The results of the experiment show that,under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper,compared to the traditional RELU algorithm,the agent trained with reinforcement learning algorithms,such asDeepDeterministic Policy Gradient algorithm,Asynchronous Advantage Actor-Critic algorithm,Deep Q Network algorithm performs better.It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific.In contrast to other reinforcement learning algorithms,the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation,and the use of weapon resources is more efficient.It shows that the model and algorithm have certain superiority and rationality.The results of this paper provide new ideas for solving the problemof weapon-target assignment in air defense combat command decisions.展开更多
The allocation of heterogeneous battlefield resources is crucial in Command and Control(C2).Balancing multiple competing objectives under complex constraints so as to provide decisionmakers with diverse feasible candi...The allocation of heterogeneous battlefield resources is crucial in Command and Control(C2).Balancing multiple competing objectives under complex constraints so as to provide decisionmakers with diverse feasible candidate decision schemes remains an urgent challenge.Based on these requirements,a constrained multi-objective multi-stage weapon-target assignment(CMOMWTA)model is established in this paper.To solve this problem,three constraint-feature-guided multi-objective evolutionary algorithms(CFG-MOEAs)are proposed under three typical multi-objective evolutionary frameworks(i.e.,NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D)to obtain various high-quality candidate decision schemes.Firstly,a constraint-feature-guided reproduction strategy incorporating crossover,mutation,and repair is developed to handle complex constraints.It extracts common row and column features from different linear constraints to generate the feasible offspring population.Then,a variable-length integer encoding method is adopted to concisely denote the decision schemes.Moreover,a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population.Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness.The superior algorithm CFG-NSGA-Ⅱamong three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms,and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-Ⅱ.展开更多
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,includ...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.展开更多
针对区域防空反导作战中各要素复杂耦合所导致的战场态势快速演变、来袭目标数量动态变化等难题,提出一种基于可动态扩展且带时空推理的QMIX(QMIX with Dynamic extension and Spatiotemporal reasoning, QMIX-DS)的火力分配方法,以火...针对区域防空反导作战中各要素复杂耦合所导致的战场态势快速演变、来袭目标数量动态变化等难题,提出一种基于可动态扩展且带时空推理的QMIX(QMIX with Dynamic extension and Spatiotemporal reasoning, QMIX-DS)的火力分配方法,以火力单元作为智能体构建决策网络,生成火力分配策略。核心改进为:为每个智能体的决策网络设计可动态扩展特征编码模块,自适应处理数量变化的来袭目标,并引入对比学习突出目标类别属性,形成差异化特征表征;构建两层多头自注意力机制捕捉不同类别目标间的动态时空依赖关系,快速推理任务过程中的态势演变,优化火力分配策略。基于墨子平台不同规模的仿真结果表明,所提出的火力分配方法能够在动态变化的战场条件下生成有效的防空反导策略,与基线算法及其他主流算法相比,所提QMIX-DS算法在目标拦截率、阵地存活率、导弹消耗数量等指标上均体现出了优势,并在不同场景中展现出较高的扩展性和泛化性。展开更多
It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the com...It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the computing nodes on the battlefield are vulnerable to be attacked and the communication environment is usually unreliable.To solve the WTA problems in unreliable environments,this paper proposes a scheme based on decentralized peer-to-peer architecture and adapted artificial bee colony(ABC)optimization algorithm.In the decentralized architecture,the peer computing node is distributed to each weapon units and the packet loss rate is used to simulate the unreliable communication environment.The decisions made in each peer node will be merged into the decision set to carry out the optimal decision in the decentralized system by adapted ABC algorithm.The experimental results demonstrate that the decentralized peer-to-peer architecture perform an extraordinary role in the unreliable communication environment.The proposed scheme preforms outstanding results of enemy residual value(ERV)with the packet loss rate in the range from 0 to 0.9.展开更多
基金supported by the National Natural Science Foundation of China (No. 62073267)。
文摘As a crucial process in the coordinated strikes of unmanned aerial vehicles(UAVs), weapon-target assignment is vital for optimizing the allocation of available weapons and effectively exploiting the capabilities of UAVs. Existing weapon-target assignment methods primarily focus on macro cluster constraints while neglecting individual strategy updates. This paper proposes a novel weapon-target assignment method for UAVs based on the multi-strategy threshold public goods game(PGG). By analyzing the concept mapping between weapon-target assignment for UAVs and multi-strategy threshold PGG, a weapon-target assignment model for UAVs based on the multi-strategy threshold PGG is established, which is adaptively complemented by the diverse cooperation-defection strategy library and the utility function based on the threshold mechanism. Additionally, a multi-chain Markov is formulated to quantitatively describe the stochastic evolutionary dynamics, whose evolutionary stable distribution is theoretically derived through the development of a strategy update rule based on preference-based aspiration dynamic. Numerical simulation results validate the feasibility and effectiveness of the proposed method, and the impacts of selection intensity, preference degree and threshold on the evolutionary stable distribution are analyzed. Comparative simulations show that the proposed method outperforms GWO, DE, and NSGA-II, achieving 17.18% higher expected utility than NSGA-II and reducing evolutionary stable times by 25% in large-scale scenario.
基金This project was supported by the National Defense Pre-Research Foundation of China
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(61673209,71971115)。
文摘The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.
基金funded by the Project of the National Natural Science Foundation of China,Grant Number 62106283.
文摘Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinforcement learning theory,an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed,which uses a double noise mechanism to expand the search range of the action,and introduces a priority experience playback mechanism to effectively achieve data utilization.Finally,the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield.The results of the experiment show that,under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper,compared to the traditional RELU algorithm,the agent trained with reinforcement learning algorithms,such asDeepDeterministic Policy Gradient algorithm,Asynchronous Advantage Actor-Critic algorithm,Deep Q Network algorithm performs better.It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific.In contrast to other reinforcement learning algorithms,the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation,and the use of weapon resources is more efficient.It shows that the model and algorithm have certain superiority and rationality.The results of this paper provide new ideas for solving the problemof weapon-target assignment in air defense combat command decisions.
基金supported by the National Science Fund of China under Grant Nos.62425304 and 62025301the Basic Science Center Programs of NSFC under Grant No.62088101+1 种基金Shanghai Municipal Science and Technology Major Project under Grant No.2021SHZDZX0100Shanghai Municipal Commission of Science and Technology Project under Grant No.19511132101。
文摘The allocation of heterogeneous battlefield resources is crucial in Command and Control(C2).Balancing multiple competing objectives under complex constraints so as to provide decisionmakers with diverse feasible candidate decision schemes remains an urgent challenge.Based on these requirements,a constrained multi-objective multi-stage weapon-target assignment(CMOMWTA)model is established in this paper.To solve this problem,three constraint-feature-guided multi-objective evolutionary algorithms(CFG-MOEAs)are proposed under three typical multi-objective evolutionary frameworks(i.e.,NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D)to obtain various high-quality candidate decision schemes.Firstly,a constraint-feature-guided reproduction strategy incorporating crossover,mutation,and repair is developed to handle complex constraints.It extracts common row and column features from different linear constraints to generate the feasible offspring population.Then,a variable-length integer encoding method is adopted to concisely denote the decision schemes.Moreover,a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population.Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness.The superior algorithm CFG-NSGA-Ⅱamong three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms,and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-Ⅱ.
基金Supported by the National Natural Science Foundation of China(Grant No.60374069)the Foundation of the Key Laboratory of Complex Systems and Intelligent Science,Institute of Automation,Chinese Academy of Sciences(Grant No.20060104)
文摘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.
文摘针对区域防空反导作战中各要素复杂耦合所导致的战场态势快速演变、来袭目标数量动态变化等难题,提出一种基于可动态扩展且带时空推理的QMIX(QMIX with Dynamic extension and Spatiotemporal reasoning, QMIX-DS)的火力分配方法,以火力单元作为智能体构建决策网络,生成火力分配策略。核心改进为:为每个智能体的决策网络设计可动态扩展特征编码模块,自适应处理数量变化的来袭目标,并引入对比学习突出目标类别属性,形成差异化特征表征;构建两层多头自注意力机制捕捉不同类别目标间的动态时空依赖关系,快速推理任务过程中的态势演变,优化火力分配策略。基于墨子平台不同规模的仿真结果表明,所提出的火力分配方法能够在动态变化的战场条件下生成有效的防空反导策略,与基线算法及其他主流算法相比,所提QMIX-DS算法在目标拦截率、阵地存活率、导弹消耗数量等指标上均体现出了优势,并在不同场景中展现出较高的扩展性和泛化性。
基金This work was supported by the Foundation for Distinguished Young Scholars of Fujian Agriculture and Forestry University(xjq201809)MOST of Taiwan(107-2623-E-009-006-D).
文摘It is of great significance for headquarters in warfare to address the weapon-target assignment(WTA)problem with distributed computing nodes to attack targets simultaneously from different weapon units.However,the computing nodes on the battlefield are vulnerable to be attacked and the communication environment is usually unreliable.To solve the WTA problems in unreliable environments,this paper proposes a scheme based on decentralized peer-to-peer architecture and adapted artificial bee colony(ABC)optimization algorithm.In the decentralized architecture,the peer computing node is distributed to each weapon units and the packet loss rate is used to simulate the unreliable communication environment.The decisions made in each peer node will be merged into the decision set to carry out the optimal decision in the decentralized system by adapted ABC algorithm.The experimental results demonstrate that the decentralized peer-to-peer architecture perform an extraordinary role in the unreliable communication environment.The proposed scheme preforms outstanding results of enemy residual value(ERV)with the packet loss rate in the range from 0 to 0.9.
