Weapon-target Allocation Research Articles

Weapon Target Allocation Based on GA-APSO Algorithm

Weapon Target Allocation Based on GA-APSO Algorithm

Uncertain Sensor–Weapon–Target Allocation Problem Based on Uncertainty Theory

The sensor–weapon–target allocation (S-WTA) is a typical collaborative task allocation problem involved in network-centric warfare (NCW). The existing related studies have a limitation to the nature of cooperation and uncertainty in an air defense battle scenario, and most existing models have the assumption that they are determinate, i.e., the parameters in them are known certainly. For the actual battlefield environment, the asymmetric information in it could lead to the failure of the above assumption, and there are many uncertainties whose frequency can not be evaluated objectively. Based on uncertainty theory, this paper studied the S-WTA problem in an indeterminate battlefield environment. First, we analyze the uncertain factors existing in the actual battlefield environment and their influence on the S-WTA problem, and by considering the threat value of the target, the deviation parameters of the sensor tracking performance and weapon interception performance as uncertain variables, we then establish an uncertain S-WTA (USWTA) model, where the destruction value to targets is regarded as an objective function and four categories of typical constraints are set. Further, an equivalent transformation is presented to convert the unsolvable model into a determinate one by the expected value principle. To solve the proposed model efficiently, a permutation-based representation for the allocation scheme of the USWTA problem is introduced firstly, which can construct a feasible solution efficiently, and on this basis, a constructive heuristic algorithm based on maximum marginal return rule (MMRCH) is designed to construct a feasible solution with high quality. Additionally, a local search (LS) operation is proposed to explore for the better solution locally and further improve the quality of solution obtained by MMRCH. Finally, a set of instances are set to be solved by the designed algorithm, and the simulation experiment demonstrates the superiority of the designed algorithm and the feasibility of the proposed model.

Bi-Level Fuzzy Expectation-Based Dynamic Anti-Missile Weapon Target Allocation in Rolling Horizons

The weapon target allocation (WTA) problem is a crucial issue in anti-missile command decisions. However, the current anti-missile weapon target allocation models ignore the dynamic complexity, cooperation, and uncertainty in the actual combat process, which results in the misclassification and omission of targets. Therefore, we propose a bi-level dynamic anti-missile weapon target allocation model based on rolling horizon optimization and marginal benefit reprogramming to achieve rapid impact on static and dynamic uncertainties in the battlefield environment. Further, we also propose an improved bi-level recursive BBO algorithm based on hybrid migration and variation to perform fast and efficient optimization of the model objective function. A simulation analysis demonstrate that the model is suitable for larger-scale, complex, dynamic anti-missile operations in uncertain environments, while the algorithm achieves better solution efficiency and solution time compared with the same type of heuristic algorithm, which meet the requirements of solution accuracy and timeliness. In addition, we obtain better rolling horizon parameters to further optimize its performance.

Modelling and Optimisation of UHF band EW Based WTA Problem within the Scope of Threat Assessment

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.

Uncertain multi-objective dynamic weapon-target allocation problem based on uncertainty theory

<abstract><p>The weapon-target allocation (WTA) problem is a fundamental subject of defense-related applications research, and previous studies assume that the parameters in the model are determinate. For the real battlefield, asymmetric information usually leads to the failure of the above assumption, and there are uncertain factors whose frequency is hard to pinpoint. Based on uncertainty theory, we study a WTA problem in indeterminate battlefield in this paper. First, we analyze the uncertain factors in indeterminate battlefield and their influence on WTA problem. Then, considering the target threat value, the protected asset value and the extra cost of interception as uncertain variables, the uncertain multi-objective dynamic WTA (UMDWTA) model is established, where three indices including the value of destruction of targets, the value of surviving assets and the cost of operation are regarded as objective functions, and on this basis, an equivalent transformation is presented to convert the UMDWTA model into a determinate multi-objective programming (MOP) problem by expected value and standard deviation principle. To solve the proposed model efficiently, an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) is designed, which employs three new evolutionary operators and the weight vectors adaptation mechanism to improve the convergence and uniformity of the Pareto front obtained. Finally, a case of the UMDWTA problem is carried out to be solved by the designed algorithm, and the results verify the feasibility of the proposed model.</p></abstract>

Research on Weapon Target Allocation of Synthetic Detachment Considering Emergency Target

In view of the problem of emergency target handling and reasonable attack in the combat process of synthetic detachment, this paper analyzes the main characteristics of emergency target, judges the threat degree of emergency target with “threat factor function”, and controls the output of the solution by solving the objective function of “threat factor function”, so as to ensure that the emergency target with great influence can be handled quickly. By improving the weapon target allocation model and using the solution control strategy, the simulation results show that this method can effectively improve the overall strike efficiency and the processing speed of emergency targets.

Multi-stage attack weapon target allocation method based on defense area analysis

For better reflecting the interactive defense between targets in practical combat scenarios, the basic weapon-target allocation (WTA) framework needs to be improved. A multi-stage attack WTA method is proposed. First, a defense area analysis is presented according to the targets' positions and the radii of the defense areas to analyze the interactive coverage and protection between targets' defense areas. Second, with the coverage status and coverage layer number, a multi-stage attack planning method is proposed and the multi-stage attack objective function model is established. Simulation is conducted with interactive defense combat scenarios, the traditional WTA method and the multi-stage WTA method are compared, and the objective function model is validated with the Monte-Carlo method. The results suggest that if the combat scenario involves interactive coverage of targets' defense areas, it is imperative to analyze the defense areas and apply the multi-stage attack method to weakening the target defense progressively for better combat effectiveness.

Iterated Local Search in Combinatorial Optimization Problem

Iterated local search (ILS) is a very powerful optimization method for continuous-valued numerical optimization. However, ILS has seldom been used to solve combinatorial integer-valued optimization problems. In this paper, the iterated local search (ILS) with random restarts algorithm is applied to solve combinatorial optimization problems, e.g., the classical weapon-target allocation (WTA) problem which arises from the military operations research. The mathematical model of the WTA problem is explained in detail. Then the idea of ILS with random restarts is explained. A comparison of the algorithm with several existing search approaches shows that the ILS outperforms its competitors on the tested WTA problem.

Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

Weapon–Target-Allocation Strategies in Multiagent Target–Missile–Defender Engagement

A cooperative interception scenario between multiple maneuvering targets, their defenders, and missiles attacking the targets is considered. To assign defenders to missiles, several computationally efficient weapon–target-allocation algorithms are developed. The algorithms use the adjoint system representation and are based on evaluating a figure of merit for the possible allocations of defenders to missiles, given that the allocation of missiles to targets is known. Furthermore, an optimal cooperative guidance law for the target is developed and, together with the weapon–target-allocation algorithms, evaluated in a nonlinear simulation setting. A comparison to the optimal allocation demonstrates the fact that the proposed weapon–target-allocation algorithms can provide close-to-optimal performance with a significant computational runtime improvement.

Novel Compact Genetic Algorithm for WTA Problem

The paper proposed a novel compact genetic algorithm which is named as pseudo-parallel compact genetic algorithm. There are two populations in the process of evolution, and the two subpopulation can exchange information between each other. The experimental results show that the novel algorithm performs better than simple genetic algorithm. Then it is used to solve weapon target allocation (WTA) problem, and the simulation result shows that it is more efficient comparing with other methods. Because the compact genetic algorithm is easy to operate and take up less memory, so the algorithm exhibit a better quality of solution and the required less time than before.

무기할당문제에서 유전자 알고리즘의 성능을 개선하기 위한 population 초기화 방법에 관한 연구

무기할당 문제(Weapon Target Allocation : WTA)는 전형적인 NP-Complete 문제로 공중에서 위협하는 표적에 대해 아군의 무기를 적절히 할당하는 문제이다. 이러한 NP-Complete 문제들은 주로 휴리스틱 알고리즘을 이용하여 최적해를 찾는다. 유전자 알고리즘은 대표적인 휴리스틱 알고리즘으로 다양한 도메인에서 우수한 성능을 보여주는 휴리스틱 알고리즘이다. 유전자 알고리즘의 단계 중에 population 초기화는 최초 염색체를 결정하는 문제로 유전자 알고리즘의 해의 질을 높일 수 있고, 탐색성능을 높일 수 있으나 많은 연구가 이루어지고 있지 않는 분야이다. 따라서 본 논문에서는 WTA 문제를 해결하기 위해 유전자 알고리즘의 성능을 향상시키기 위한 population 초기화 알고리즘을 제안하고자 한다. 제안하는 알고리즘은 초기화할 때 WTA 문제 도메인의 특성을 반영하고, 우성유전자를 상속받는다. 또한, 문제 공간에서의 탐색 공간을 넓게 선정하여 질이 좋은 해를 효율적으로 찾을 수 있도록 하였다. 본 논문에서는 제안하는 알고리즘과 다른 알고리즘과의 다양한 속성의 비교분석 및 실험을 통해 성능을 분석하여 제안하는 알고리즘의 우수성을 검증하였다. 실험 결과 제안하는 알고리즘이 WTA 문제 해결에서 다른 방법들에 비해 좋은 성능을 보였다. 특히, 제안하는 알고리즘은 문제 상황에 따라 RMI 수치를 조정하여 적응성 있게 적용할 수 있기 때문에, 문제의 상황이 다양한 WTA 문제 도메인에 적용하기 적합한 알고리즘이다. The Weapon Target Allocation(WTA) problem is the NP-Complete problem. The WTA problem is that the threatful air targets are assigned by weapon of allies for killing the targets. A good solution of NP-complete problem is heuristic algorithms. Genetic algorithms are commonly used heuristic for global optimization, and it is good solution on the diverse problem domain. But there has been very little research done on the generation of their initial population. The initialization of population is one of the GA step, and it decide to initial value of individuals. In this paper, we propose to the population initialization method to improve a Genetic Algorithm. When it initializes population, the proposed algorithm reflects the characteristics of the WTA problem domain, and inherits the dominant gene. In addition, the search space widely spread in the problem space to find efficiently the good quality solution. In this paper, the proposed algorithm to verify performance examine that an analysis of various properties and the experimental results by analyzing the performance compare to other algorithms. The proposed algorithm compared to the other initialization methods and a general genetic algorithm. As a result, the proposed algorithm showed better performance in WTA problem than the other algorithms. In particular, the proposed algorithm is a good way to apply to the variety of situation WTA problem domain, because the proposed algorithm can be applied flexibly to WTA problem by the adjustment of RMI.

A branch‐and‐price algorithm for a targeting problem

AbstractIn this paper, we consider a new weapon‐target allocation problem with the objective of minimizing the overall firing cost. The problem is formulated as a nonlinear integer programming model, but it can be transformed into a linear integer programming model. We present a branch‐and‐price algorithm for the problem employing the disaggregated formulation, which has exponentially many columns denoting the feasible allocations of weapon systems to each target. A greedy‐style heuristic is used to get some initial columns to start the column generation. A branching strategy compatible with the pricing problem is also proposed. Computational results using randomly generated data show this approach is promising for the targeting problem. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007

Dynamic WTA optimization model of air defense operation of warships' formation

Abstract WTA (weapon-target allocation) of air defense operation is a very complicated problem and current models focus on static and restricted WTA problem mostly. Based on the dynamic characteristics of air defense operational command and decision of warships' formation, a dynamic WTA model is established. Simulation results show that switch fire and repetition fire of anti-air weapon systems affect the result of the air defense operation remarkably and the dynamic model is more satisfying than static ones. Related results are gained based on the analysis of the simulation results and the results are accordant with the intuitionistic tactical judgment. The model is some reference for the research of air defense C 3I system of warships' formation.

Lagrangian relaxation approach to the targeting problem

In this paper, we consider a new weapon–target allocation problem with the objective of minimizing the overall firing cost. The problem is formulated as a nonlinear integer programming model. We applied Lagrangian relaxation and a branch-and-bound method to the problem after transforming the nonlinear constraints into linear ones. An efficient primal heuristic is developed to find a feasible solution to the problem to facilitate the procedure. In the branch-and-bound method, three different branching rules are considered and the performances are evaluated. Computational results using randomly generated data are presented. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 640–653, 1999