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This article focuses on air defense in maritime environment, which involves protecting friendly naval assets from aerial threats. Specifically, we define and address the Naval Air Defense Planning (NADP) problem, which consists of maneuvering decisions of the ships and scheduling weapons and sensors to the threats in order to maximize the total expected survival probability of friendly units. The NADP problem is more realistic and applicable than previous studies, as it considers features such as sensor assignment requirements, weapon and sensor blind sectors, sequence‐dependent setup times, and ship's infrared/radar signature. In this study, a mixed‐integer nonlinear programming model of the NADP problem is presented and heuristic solution approaches are developed. Computational results demonstrate that these heuristic approaches are both fast and efficient in solving the NADP problem. [ABSTRACT FROM AUTHOR]

The weapon-target assignment problem is the key to the combat mission of the UAV against the enemy in the battlefield environment. The purpose is to find a reasonable weapon target assignment scheme based on the threat, value and damage probability of the target, so as to improve the combat efficiency. Aiming at the problem that the current multi-objective optimization algorithm has slow convergence speed and poor convergence stability when solving the static weapon-target assignment problem, and it is difficult to adapt to the high real-time performance of the current battlefield, an improved non-dominated sorting genetic algorithm based on reference points is proposed. The initial population is optimized by binary coding attack scheme, and adaptive mutation and crossover strategy as well as population optimization update strategy is introduced. Based on the threat matrix and advantage matrix obtained by evaluating the battlefield situation, the target attack scheme is generated after multiple iterations of the population. Finally, the Pareto solution set satisfying the constraint condition is calculated, and the relative optimal solution in the Pareto frontier is taken as the attack scheme of multi-UAV. Multiple experiments show that under good conditions, the improved algorithm reduces convergence time by 46.74%, reduces target threat value by 50.5%, reduces total flight range by 26.46%, and increases the number of killing targets by 11.76% compared with the original algorithm. It is proved that the algorithm is reasonable and efficient in solving the problem of target assignment of multi-UAV air-to-ground strike mission.

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Weapon target assignment (WTA) is a typical problem in the command and control of modern warfare. Despite the significance of the problem, traditional algorithms still have shortcomings in terms of efficiency, solution quality, and generalization. This paper presents a novel multi-objective evolutionary optimization algorithm (MOEA) that integrates a deep Q-network (DQN)-based adaptive mutation operator and a greedy-based crossover operator, designed to enhance the solution quality for the multi-objective WTA (MO-WTA). Our approach (NSGA-DRL) evolves NSGA-II by embedding these operators to strike a balance between exploration and exploitation. The DQN-based adaptive mutation operator is developed for predicting high-quality solutions, thereby improving the exploration process and maintaining diversity within the population. In parallel, the greedy-based crossover operator employs domain knowledge to minimize ineffective searches, focusing on exploitation and expediting convergence. Ablation studies revealed that our proposed operators significantly boost the algorithm performance. In particular, the DQN mutation operator shows its predictive effectiveness in identifying candidate solutions. The proposed NSGA-DRL outperforms state-and-art MOEAs in solving MO-WTA problems by generating high-quality solutions. [ABSTRACT FROM AUTHOR]

Target assignment is the core part in the command and control process, and the optimization of the allocation mode is of great significance for improving the air defense and anti-missile combat capability. In order to improve the robustness, applicability, game antagonism and other combat performance of air defense and anti-missile target allocation, so as to cope with the current complex and changeable battlefield environment, this paper proposes the target allocation architecture establishment of multi-mode fusion, and adaptively improve the commercial order-type service mode. The improvement of three kinds of dispatching models, that are “dispatching order” “grabbing order” and “dispatching- grabbing order fusion”, are defined as military models. By using the US department of defense architecture framework (DoDAF) to establish a new target allocation architecture of “other allocation” “self-allocation”and “combination of other allocation and self-allocation”. With the introduction of Perti net model, the reachable map of Petri net model is constructed and analyzed. Through the simulation experiment platform, the complex combat scenario is constructed to verify the feasibility of the distribution strategy mechanism. The results show that each of the three strategies has its own advantages. The multi-strategy combination designed in this paper has greater advantages than the traditional single strategy in matching time, success rate and utility value.

Weapon target assignment (WTA) is a typical problem in the command and control of modern warfare. Despite the significance of the problem, traditional algorithms still have shortcomings in terms of efficiency, solution quality, and generalization. This paper presents a novel multi-objective evolutionary optimization algorithm (MOEA) that integrates a deep Q-network (DQN)-based adaptive mutation operator and a greedy-based crossover operator, designed to enhance the solution quality for the multi-objective WTA (MO-WTA). Our approach (NSGA-DRL) evolves NSGA-II by embedding these operators to strike a balance between exploration and exploitation. The DQN-based adaptive mutation operator is developed for predicting high-quality solutions, thereby improving the exploration process and maintaining diversity within the population. In parallel, the greedy-based crossover operator employs domain knowledge to minimize ineffective searches, focusing on exploitation and expediting convergence. Ablation studies revealed that our proposed operators significantly boost the algorithm performance. In particular, the DQN mutation operator shows its predictive effectiveness in identifying candidate solutions. The proposed NSGA-DRL outperforms state-and-art MOEAs in solving MO-WTA problems by generating high-quality solutions.

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Weapon target assignment (WTA) problem is an important problem in the field of combat command and decision-making, which aims to determine a reasonable weapon allocation scheme for the target to be attacked based on the different damage probability of the weapon to the incoming target, in order to achieve the goal of minimizing the damage degree of the weapon to the incoming target. Based on the construction of WTA problem model, an elite retention and immune mutation local search strategy was adopted in grey wolf optimization (GWO) to improve the local search ability of the traditional GWO algorithm. The improved grey wolf optimization (IGWO) firstly selects some high-quality elite individuals from the gray wolf population, and then further optimizes the elite individuals by using two immune local search strategies, random point mutation and receptor editing, to further optimize the elite individuals, thereby improving the shortcomings of premature convergence and easy to fall into local optimization of traditional GWO algorithms. Aiming at three scales of WTA problems, IGWO is compared with the cross entropy algorithm and traditional GWO algorithm, the calculation results show that the quantiles of fitness values obtained by the IGWO algorithm are significantly higher than those of the comparison algorithm, thereby verifying the effectiveness of the IGWO algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the air defense and antimissile operations of traditional kinetic energy weapons and modern high-energy laser weapons, a dynamic weapon target assignment model of heterogeneous defense weapons with multiple tactical targets is established; Considering the commander's choice preference, the optimization framework of weapon target allocation based on improved r-NSGA-Ⅱ algorithm is proposed, and the specific optimization process is given; Based on the specific operational scenario, the design experiment is designed to compare the improved r-NSGA-Ⅱ algorithm with NSGA-Ⅱ, NSGA-Ⅲ, MOEA/D and other classical algorithms in terms of convergence, distribution, time complexity and other performance indicators. The experimental results show that the improved r-NSGA-Ⅱ algorithm can better reflect the commander's operational preferences and intentions while ensuring the computational efficiency.

Based on the air defense and antimissile operations of traditional kinetic energy weapons and modern high-energy laser weapons, a dynamic weapon target assignment model of heterogeneous defense weapons with multiple tactical targets is established; Considering the commander's choice preference, the optimization framework of weapon target allocation based on improved r-NSGA-Ⅱ algorithm is proposed, and the specific optimization process is given; Based on the specific operational scenario, the design experiment is designed to compare the improved r-NSGA-Ⅱ algorithm with NSGA-Ⅱ, NSGA-Ⅲ, MOEA/D and other classical algorithms in terms of convergence, distribution, time complexity and other performance indicators. The experimental results show that the improved r-NSGA-Ⅱ algorithm can better reflect the commander's operational preferences and intentions while ensuring the computational efficiency. [ABSTRACT FROM AUTHOR]

Optimal assignment of equipment for disposal of targets, often referred to as a weapon assignment problem, has become one of the main centers of modern military thought. Weapon Target Assignment (WTA) is based on consideration of the principle of saving on resources, without reducing the power of systems and systems always to protect vital infrastructure. The critical infrastructure consists of the physical assets of a system, resulting in a significant disruption to operational and operational systems. In this study, a Stackelberg a mathematical model is presented to manage battle scenes. The Stackelberg model is considered to be a strategic game and an incomplete competition. The game is considered to consist of two actors (Enemy and Power), each striving to optimize its goals. Considering air and ground forces simultaneously, consider the assets of both the friendly and the enemy, including the innovations considered in this study. In this study, a two- level Stackelberg model has been presented and after linearization, the two- level model has been developed using Karush–Kuhn–Tucker conditions (KKT) to the ordinary one –level model. Finally to show the performance of the model, some examples are solved using GAMS software.

The weapon target assignment is a crucial issue for firepower resources optimization in modern warfare. Such a problem is complicated, multi-constrained, strongly nonlinear, NP-complete and the existing studies did not consider the suitability between different weapons and targets. In this paper, a novel weapon target assignment model is established that involves the weapon-target suitability and is closer to the real combat scenarios. Then, in view of that the conventional weapon target assignment methods are difficult to be applied in the large-scale problems efficiently, this work proposes a dynamic Gaussian mutation beetle swarm optimization algorithm with rule-based chaotic initialization. With the assistance of the dynamic parameter adjustment strategies and Gaussian mutation, the improved algorithm has fast convergence speed and high convergence accuracy, and it can solve the weapon target assignment problems with excellent optimization capabilities. Besides, the rule-based chaotic initialization strategy is embedded in this algorithm to generate high-quality population with better diversity. Finally, two comparative simulation cases of different initialization methods and algorithms for solving the large-scale weapon target assignment problems are designed. The results demonstrate that the proposed approach can provide more superior assignment schemes than its competitors with enhanced efficiency. • An improved beetle swarm optimization algorithm is proposed for weapon target assignment. • The weapon-target suitability is considered in the optimization model. • The dynamical parameters and Gaussian mutation are employed to enhance the algorithm efficiency. • A new initialization strategy is presented to improve the population diversity and quality. [ABSTRACT FROM AUTHOR]

Since chemical, biological, radiological, nuclear, and high yield explosive (CBRNE) attacks can cause catastrophic damage, it is important to detect and eliminate the means of attack at the origin. In surveillance operations, efficient allocation of friendly intelligence assets and enemy targets is critical for continuous and reliable monitoring. In this research, we investigate a mathematical model for exchanging target–asset pairs when there are sudden changes in various operational environments. For this task, we refer to the kidney exchange model as a benchmark. In particular, the methods for constructing and solving the target–asset exchange problem in near realtime are presented. Additionally, we introduce the methodology and results for obtaining a feasible solution of the weapon target assignment problem using the exchange model. Our method can facilitate decisions in reconnaissance operations, especially when countless targets and assets are intricately intertwined in future battlefield scenarios. [ABSTRACT FROM AUTHOR]

An inherent difficulty in dynamic distributed constraint optimization problems (dynamic DCOP) is the uncertainty of future events when making an assignment at the current time. This dependency is not well addressed in the research community. This paper proposes a reinforcement-learning-based solver for dynamic distributed constraint optimization. We show that reinforcement learning techniques are an alternative approach to solve the given problem over time and are computationally more efficient than sequential DCOP solvers. We also use the novel heuristic to obtain the correct results and describe a formalism that has been adopted to model dynamic DCOPs with cooperative agents. We evaluate this approach in dynamic weapon target assignment (dynamic WTA) problem, via experimental results. We observe that the system dynamic WTA problem remains a safe zone after convergence while satisfying the constraints. Moreover, in the experiment we have implemented the agents that finally converge to the correct assignment. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new bi-objective model for dynamic weapon-target assignment problem. We consider that the initial weapon assignment plan of defense is disrupted during engagement because of a destroyed air target, breakdown of a weapon system or a new incoming air target. The objective functions are defined as the maximization of probability of no-leaker and the maximization of stability in engagement order of weapon systems. Stability is defined as assigning same air target in sequence in engagement order of a weapon system so that reacquisition and re-tracking of air target are not required by sensors. We propose a new solution procedure to generate updated assignment plans by maximizing efficiency of defense while maximizing stability through swapping weapon engagement orders. The proposed solution procedure generates non-dominated solutions from which defense can quickly choose the most-favored course of action. We solve a set of representative problems with different sizes and present computational results to evaluate effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The classical weapon target allocation (WTA) problem has been evaluated within the scope of electronic warfare (EW) threat assessment with an electromagnetic effect-based jammer-tactical radio engagement approach. As different from the literature, optimum allocation of non-directional jammers operating at different operating UHF frequencies under constraints to RF emitters is aimed in this study. The values of the targets are modelled using an original threat assessment algorithm developed that takes into account operating frequencies, jamming distance, and weather conditions. The computed jammer-target effect matrix has been solved under different scenarios according to the efficiency and cost constraints. It is seen at the end of the simulations that the allocation results for EW applications largely depend on the effect ratio used. The better results are taken in the case of under 0.5 effect ratio. Finally, jammer-radio allocation problem specified at the suggested model is solved successfully and effectively. [ABSTRACT FROM AUTHOR]

The multi-objective weapon-target assignment problem, which aims to generate reasonable assignment to meet the objectives, is a typical optimization problem with complex constraints. In order to get close to the actual air combat, the game process between both sides at war is introduced to construct a three objective mathematical model, which includes the damage of the enemy, the cost of missiles, and the damage value of fighting capacity. Considering the NP-complete nature of multi-objective weapon-target assignment problem, an improved intelligent algorithm (named as D-NSGA-III-A) on the basis of non-dominated sorting genetic algorithm III (NSGA-III) is proposed. In this improved algorithm, first, the non-dominated sorting based on dominance degree matrix is proposed to reduce the unnecessary or repetitive comparisons in ranking schemes, so as to further decrease the time consumption. Second, diversity and convergence are taken into account resorting to the niching information and the dominance ratio when selecting individuals. Third, the adaptive operator selection mechanism, which selects the operators adaptively according to the information of generations from a pool where single point crossover and all bits crossover operators are included, is employed to seek a balance between intensification and diversification within the decision space and to improve the quality of Pareto solutions. From the experiments, the combination of above technologies obtains better Pareto solutions and time performance for solving the static multi-objective target assignment (SMWTA) problem than NSGA-III, MP-ACO, NSGA-II, MOPSO, MOEA/D, and DMOEA-εC.

The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution. [ABSTRACT FROM AUTHOR]

Quantum computers, known to have the potential for exponential speedup in solving some problems due to their superposition property, are expected to facilitate the solution of NP-hard optimisation problems. This study proposes a quantum algorithm to solve the weapon target assignment problem (WTAP), one of the NP-hard optimisation problems. The proposed quantum algorithm is a gate-based approach, and scenario examples are executed on Qiskit quantum computing platform and IBM Lima quantum computer with Falcon r4T processor type. The results manifest that the proposed quantum algorithm has low space and time complexity, demonstrating its memory and computational resources efficiency. • A quantum algorithm is proposed for the weapon target assignment problem. • The proposed quantum algorithm is a gate-based approach. • Scenario examples are utilised to demonstrate the effectiveness of the quantum algorithm. • It has lower space and time complexity than the other methods herein. [ABSTRACT FROM AUTHOR]

In this work, we consider the static weapon target assignment problem which is a very hard combinatorial optimization problem has not only defense-related applications but business world applications. The aim of the weapon target assignment problem is to find an assignment of weapons to targets with the minimum total survival value of the targets. The weapon target assignment problem is known to be NP-complete problem. In this paper, we propose a hybrid artificial bee colony algorithm with simulated annealing algorithm to give an efficient solution to the weapon target assignment problem. We tested the proposed algorithm with benchmark problem instances and compared it with some other meta-heuristics in the literature. Computational tests show that our algorithm is competitive and satisfactory. [ABSTRACT FROM AUTHOR]

Various objective functions in the operation process of unmanned ground combat vehicles (UGVs) have an important impact on the equilibrium of the system. Unbalanced scheduling of unmanned ground combat vehicles and poor target strikes exist in complex urban battlefields. A new multi-weapon target assignment architecture and a multi-objective artificial bee colony (MOABC) algorithm with an elite strategy are proposed to solve these problems. Considering the influence of mutation operator on multi-objective assignment, by introducing the action mechanism of the self-adaptive variation operator and combining the state representation of the nectar source with the overall allocation scheme, the deep Q-learning network with improved multi-objective artificial bee colony (MOADQN) algorithm is proposed. Through comparative analysis with multi-objective artificial bee colony algorithm, non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization (MOPSO), the multi-objective evolutionary algorithm based on decomposition with electronic countermeasure (ECM-MOEA/D) and the deep Q-learning network with multi-objective artificial bee colony (MOAIQL) algorithm, the proposed MOADQN algorithm can solve the problems such as poor allocation effectiveness and low gain of traditional algorithms. The proposed MOADQN algorithm has significant advantages in solving multi-objective optimization problems and strong expansion performance in the complex urban environment. • A new deep reinforcement learning framework for unmanned ground weapon target assignment is proposed. • The adaptive action operator is proposed for the overall allocation scheme. • The proposed MOADQN algorithm has significant advantages in large-scale complex scenarios. [ABSTRACT FROM AUTHOR]

Aiming at the objective demand that the modern warship formation operation is a system-of-systems one based on information system, in this paper the problem of area antiaircraft weapon target assignment is studied for the warship formation under informationized conditions, it is put forward the concept of air defense interception level and built a weapon target assignment model based on the multilayer defense mode. In order to solve the model, combined with the interactive decision making and genetic algorithm, a human-computer interaction method to solve the model is proposed based on genetic algorithm (GA). In GA design, by reasonable design of chromosome coding, initial population, fitness function, genetic operation and strategy selection, the individual validity was ensured. Searching quality was improved and search speed was sped up. The simulation result showed the effectiveness of the model and algorithm which explores a new way to effectively solve the problem of area antiaircraft weapon target assignment in the modern warship formation system-of-systems operation. [ABSTRACT FROM AUTHOR]

SUMMARY Multichannel cooperative sensing (MCS) is an effective method for dynamic spectrum access in cognitive radio networks. In contrast to most existing work on MCS that considered secondary users with homogeneous sensing ability, this paper studies the MCS problem for secondary users with heterogeneous sensing ability in terms of sensing accuracy. We further take into account different parameters of primary channels such as bandwidth, probability of being idle in each sensing period, and frequency selective fading at the sensing receiver. The MCS problem is formulated as a weapon target assignment problem, where more sensing resources are assigned to channels that are more valuable. This weapon target assignment problem is transformed to an integer generalized network flow problem with convex flow costs to obtain the lower bound solution, and then solved by the branch and bound algorithm with this bound to yield the exact scheme. To reduce computational complexity, a heuristic scheme is also proposed, which has approximate performance compared with the exact scheme. Finally, extensive simulation results for different scenarios illustrate the performance improvements of the proposed schemes over the existing scheme. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A new discrete differential evolution algorithm was proposed to solve the static weapon target assignment (SWTA) problem. Firstly, the integer permutation was applied to building weapon-target assignment pair. Then, the differential mutation was designed by means of the modular operator to restrict the bound offending solutions into the search space. At the same time, the corresponding crossover strategy was presented to acquire the feasible solutions. In the process of crossover, the same assignment pairs of the objective and trial vectors were reserved for utilizing the results of the previous generation. In order to avoid the bias, a random permutation was generated to assure the fairness in the process of deleting repeat integers and reinserting new integers. The experimental results demonstrate that the new DDE is superior to the other two typical DDEs in terms of convergence property and solution quality and solves the static weapon target assignment problem. [ABSTRACT FROM AUTHOR]

The weapon-target assignment problem is a crucial decision support in a Command and Control system. As a typical operational scenario, the major asset-based dynamic weapon target assignment (A-DWTA) models and solving algorithms are challenging to reflect the actual requirement of decision maker. Deriving from the "shoot–look–shoot" principle, an "observe–orient–decide–act" loop model for A-DWTA (OODA/A-DWTA) is established. Focus on the decide phase of the OODA/A-DWTA loop, a novel A-DWTA model, which is based on the receding horizon decomposition strategy (A-DWTA/RH), is established. To solve the A-DWTA/RH efficiently, a heuristic algorithm based on statistical marginal return (HA-SMR) is designed, which proposes a reverse hierarchical idea of "asset value-target selected-weapon decision." Experimental results show that HA-SMR solving A-DWTA/RH has advantages of real-time and robustness. The obtained decision plan can fulfill the operational mission in the fewer stages and the "radical-conservative" degree can be adjusted adaptively by parameters. [ABSTRACT FROM AUTHOR]