Weapon Target Assignment Research Articles

MOFA/D-URAM for Solving the Air and Missile Defense Problem Based on Uncertainty Theory

For the uncertain factors in the fire allocation process of air and missile defense problem, the uncertainty theory is used to deal with the uncertain factors in the problem, and an uncertain multi-objective dynamic weapon target assignment model is proposed. In order to deal with the above model, a multi-objective evolutionary algorithm based on decomposition is proposed, which adds the displacement mechanism of firefly algorithm and uniformly randomly adaptive weights mechanism. Then, the simulation results show that the proposed algorithm has good convergence and distribution uniformity for solving multi-objective optimization problem. Lastly, using the algorithm to solve the above model, the results verify the rationality of the model.

Research on the weapon target assignment problem in the combined air defense missile system for training simulation

In modern warfare, when the weapon system and the targets are constantly being improved and upgraded, ensuring the distribution of firepower to optimally destroy the target will help the commander to make quick and accurate decisions, thereby improving combat effectiveness. This paper proposes a method to build a command-control automatic system based on solving the weapon target assignment (WTA) problem in a combination of short and medium-range air defense missile systems so that the total damage of targets is maximum and the damage of protected area is minimum. Based on combinatorial optimization algorithms, the probability of kill, linear programming method using Hungarian algorithm, the paper presents a mathematical model of WTA and its optimal solution for short- and medium-range air defense missile systems serving the training simulation problem, thereby giving the results of evaluating the effectiveness of the algorithm.

Self-Adaptive Multi-Task Differential Evolution Optimization: With Case Studies in Weapon–Target Assignment Problem

Multi-task optimization (MTO) is related to the problem of simultaneous optimization of multiple optimization problems, for the purpose of solving these problems better in terms of optimization accuracy or time cost. To handle MTO problems, there emerges many evolutionary MTO (EMTO) algorithms, which possess distinguished strategies or frameworks in the aspect of handling the knowledge transfer between different optimization problems (tasks). In this paper, we explore the possibility of developing a more efficient EMTO solver based on differential evolution by introducing the strategies of a self-adaptive multi-task particle swarm optimization (SaMTPSO) algorithm, and by developing a new knowledge incorporation strategy. Then, we try to apply the proposed algorithm to solve the weapon–target assignment problem, which has never been explored in the field of EMTO before. Experiments were conducted on a popular MTO test benchmark and a WTA-MTO test set. Experimental results show that knowledge transfer in the proposed algorithm is effective and efficient, and EMTO is promising in solving WTA problems.

Devising a procedure for justifying the need for samples of weapons and weapon target assignment when using a reconnaissance firing system

This paper proposes an algorithm to substantiate the need for weapons samples, as well as targeting when using a reconnaissance firing system taking into consideration the peculiarities of functioning of such systems. The algorithm essentially implies streamlining the stages in determining the magnitude of the reduction of the enemy's combat potential and, on its basis, the formation of the need for the number of weapons by type. The algorithm makes it possible to take into consideration the nonlinearity of functions that describe both different types of weapons and targets. In addition, this algorithm is based on a modified method of nonlinear programming (two functions). The modification involves the use of a normalized share of the weight of each target as weight coefficients. This allows for targeting while taking into consideration the established level of the combat potential of an enemy. A procedure for determining the need for samples of weapons and targeting in the use of reconnaissance firing systems has been devised. It was determined that in order to achieve the goal of enemy fire damage, it is not typically necessary to use all weapons samples. In general, the procedure makes it possible to take into consideration the peculiarities of the samples of weapons and their suitability to hit a certain target. That could prevent problems with overspending of resources, failures in the detection-defeat cycle, non-fulfillment (not fully performing) tasks during enemy fire damage. In general, the algorithm and procedure for determining the need for the samples of weapons and targeting when using a reconnaissance firing system testify to devising a methodology for justifying the need for weapons samples and targeting. The performance and adequacy of this procedure have been tested by considering an example of determining the need for weapons samples and targeting and obtaining the result confirmed by the experience in the use of reconnaissance firing systems

Weapon-target assignment in unreliable peer-to-peer architecture based on adapted artificial bee colony algorithm

Weapon-target assignment in unreliable peer-to-peer architecture based on adapted artificial bee colony algorithm

The Weapon Target Assignment Problem: Rational Inference of Adversary Target Utility Valuations from Observed Solutions

Identifying an adversary’s strategic goals and values requires deliberate and unbiased analysis. This research is motivated by the premise that, if one observes an adversary’s actions or planned actions, it is possible to draw reasonable inferences about their values, thereby reducing misperceptions and informing better decisions. Within the context of the static weapon target assignment problem, this research develops and empirically compares alternative methods to rationalize an adversary’s value hierarchy over targets that informs their observed decisions. Such methods either identify the extreme points of a polytope within a unit simplex of relative target values that encompasses all possible relative target values based on a weak dominance criterion or a subset of points within the polytope. This research characterizes the solution methods’ practical tractability for use on larger-sized problems and their generalizability to other problems. Even for the superlative technique examined, testing illustrates the computationally challenging nature of identifying the defining polytope of relative target values, and the work concludes with suggestions for metaheuristic technique development.

Solving the Dynamic Weapon Target Assignment Problem by an Improved Multiobjective Particle Swarm Optimization Algorithm

Dynamic weapon target assignment (DWTA) is an effective method to solve the multi-stage battlefield fire optimization problem, which can reflect the actual combat scenario better than static weapon target assignment (SWTA). In this paper, a meaningful and effective DWTA model is established, which contains two practical and conflicting objectives, namely, maximizing combat benefits and minimizing weapon costs. Moreover, the model contains limited resource constraints, feasibility constraints and fire transfer constraints. The existence of multi-objective and multi-constraint makes DWTA more complicated. To solve this problem, an improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed in this paper. Various learning strategies are adopted for the dominated and non-dominated solutions of the algorithm, so that the algorithm can learn and evolve in a targeted manner. In order to solve the problem that the algorithm is easy to fall into local optimum, this paper proposes a search strategy based on simulated binary crossover (SBX) and polynomial mutation (PM), which enables elitist information to be shared among external archive and enhances the exploratory capabilities of IMOPSO. In addition, a dynamic archive maintenance strategy is applied to improve the diversity of non-dominated solutions. Finally, this algorithm is compared with three state-of-the-art multiobjective optimization algorithms, including solving benchmark functions and DWTA model in this article. Experimental results show that IMOPSO has better convergence and distribution than the other three multiobjective optimization algorithms. IMOPSO has obvious advantages in solving multiobjective DWTA problems.

Weapon-Target Assignment Problem Using Modified Water Wave Optimization Algorithm

The weapon-target assignment (WTA) is a classic problem. The WTA mathematical model is that warship formations are reasonably equipped with weapons resources for each weapon system to attack the air threaten targets. The purpose of targets optimization is to maximize combat effectiveness, that is to say, the mathematical expectation is maximum. We adopt the greedy strategy and improved propagation operation is to strengthen the water wave optimization (WWO) search performance. This article elaborates a modified water wave optimization (MWWO) to solve the WTA problem, which can detect optimized allocation decision matrix and search for the maximum mathematical expectation. Based on parameter optimization, the overall performance of the MWWO is more stable, the search speed is accelerated and the accuracy is improved. The experiment results indicate that the MWWO are verified and avoids local optimum, and can be more convenient for solving the WTA and obtain better performance.

Two-stage hybrid heuristic search algorithm for novel weapon target assignment problems

The objective of the weapon target assignment (WTA) problem is to maximize the total damage of targets or minimize the total consumption of weapons. However, the existing studies of the WTA problem did not consider the uncertainty of weapon adequacy in practical battlefield scenarios. In this paper, we study a novel WTA problem, called the WTA problem with uncertainty (WTAU), and the uncertainty refers to the uncertainty of weapon adequacy. The objective of the WTAU is to find a WTA scheme to maximize the total value of destroyed targets by using as few weapons as possible in situations where the adequacy of weapons is uncertain. To address the uncertainty of weapons adequacy, we formulate the WTAU problem as a nonlinear integer programming model. To improve the computing efficiency, we design a two-stage hybrid heuristic search (TSHHS) algorithm based on prior WTAU knowledge. In the first stage of the TSHHS algorithm, a logarithmic transformation method is employed to obtain the optimal target destruction (TD) scheme and an initial WTA scheme. In the second stage, a constructive heuristic method based on the maximum marginal return (MMRBH) and a simple optimization (SimpleO) method are developed to obtain a promising WTA scheme. Moreover, we perform extensive experiments to analyze the performance of the proposed method. The results of numerical experiments show that the proposed method can provide a high-quality WTA scheme with a short computing time in scenarios involving adequate or inadequate weapons.

가변 연속사격 시간을 고려한 근접 방어 시스템의 최적 무장 할당 알고리듬

본 논문에서는 가변 연속사격 시간을 고려한 근접 방어 시스템(Closed-In Weapon System, CIWS)의 최적 무장 할당 알고리듬에 대해 다룬다. 본 연구에서 근접 방어 시스템의 무장 할당 문제는 혼합정수계획법(Mixed Integer Linear Programming, MILP)을 기반으로 정식화된다. 제안한 방법은 연속사격 시간을 교전 사거리와 관계없이 일정하다고 가정한 기존 연구와 달리 사거리에 따른 요격확률을 바탕으로 가변 연속사격 시간을 고려한다. 따라서 기존의 방법대비 보다 현실적인 교전상황을 반영할 수 있을 뿐만 아니라 표적 대응시간을 향상할 수 있다. 본 논문에서는 가변연속사격 시간이 반영되도록 기존의 혼합정수계획법 기반 무장 할당 문제를 변형시켰으며, 상용최적화 프로그램을 활용한 수치 시뮬레이션을 통해 제안한 방법의 성능을 검증하였다.

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

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.

Weapon-target assignment in UAV cluster based on pheromone heuristic wolf pack algorithm

Weapon-target assignment in UAV cluster based on pheromone heuristic wolf pack algorithm

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent systems control. In this paper, we explore the application of Learning Classifier System and Artificial Immune models for coordinated self-learning air defense systems. In particular, this paper presents a scheme that implements an autonomous cooperative threat evaluation and weapon assignment learning approach. Taking into account uncertainties in a successful interception, target characteristics, weapon type and characteristics, closed-loop coordinated behaviors, we adopt a hierarchical multi-agent approach to coordinate multiple combat platforms to achieve optimal performance. Based on the combined strengths of learning classifier system and artificial immune-based algorithms, the proposed scheme consists of two categories of agents; a strategy generation agent inspired by learning classifier system, and strategy coordination inspired by Artificial Immune System mechanisms. An experiment in a realistic environment shows that the adopted hybrid approach can be used to learn weapon-target assignment for multiple unmanned combat systems to successfully defend against coordinated attacks. The presented results show the potential for hybrid approaches for an intelligent system enabling adaptable and collaborative systems.

Bi-objective dynamic weapon-target assignment problem with stability measure

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.

A Modified MOEA/D Algorithm for Solving Bi-Objective Multi-Stage Weapon-Target Assignment Problem

In command of modern intelligent operations, in addition to solving the problem of multi-unit coordinated task assignment, it is also necessary to obtain a suitable plan according to the needs of decision makers. Based on these requirements, we established a multi-stage bi-objective weapon-target assignment model, and designed a new algorithm with niche and region self-adaptive aggregation (named MOEA/ D-NRSA) based on the decomposition-based multi-objective evolutionary algorithm (MOEA/D) to obtain richer solutions that meet the preferences of different decision makers. Compared with MOEA/D, MOEA/ D-NRSA has advantages in improving the convergence and maintaining the distribution of the solution. On the one hand, it contains a population evolution method based on niche technology to obtain better offspring; on the other hand, it has a new neighborhood selection and update strategy. This strategy first clusters the individuals in the objective space to divide into different regions, in which the subproblems can independently select the appropriate aggregation mode according to the clustering density of the region and update its neighborhood. This strategy can improve the uneven distribution of individuals and maintain the diversity and distribution of the population. Numerical experiments selected state-of-the-art algorithms for comparison, which proved the superiority of MOEA/D-NRSA.

A Novel Elitism Co-Evolutionary Algorithm for Antagonistic Weapon-Target Assignment

The Antagonistic Weapon-Target Assignment (AGWTA) problem is a crucial decision issue in Command & Control (C2). Since this is a minimax problem, co-evolutionary algorithms can be used to solve it effectively. However, the co-evolutionary algorithm is originally designed for continuous minimax problems which loses its efficiency to discrete contexts. In this paper, a novel elitism co-evolutionary algorithm is proposed to solve the AGWTA. Firstly, an improved AGWTA model for air combat based on the attack and evasion strategies is proposed. Secondly, an elite cooperative genetic algorithm based on the framework of the co-evolutionary algorithm is put forward. In this proposed algorithm, a problem-specific coding method and evolution operator are designed. Meanwhile, an elite individual update mechanism is presented. Finally, based on the analysis of the relationship between the feasible solutions under the air combat environment, an evaluation index is proposed. Experiments show that the proposed algorithm has higher accuracy than traditional co-evolutionary algorithms for solving AGWTA problems.

A dynamic weapon target assignment based on receding horizon strategy by heuristic algorithm

Weapon-target assignment problem is the crucial decision support in Command & Control system. As a typical operational scenario, asset defense has less research on the issue of asset-based dynamic weapon target assignment, and the major models of A-DWTA are challenging in practical application. In this paper, an A-DWTA model is first established by receding horizon decomposition strategy, which closely reflects the operational requirement. Then a heuristic algorithm based on statistical marginal return is proposed to solve the A-DWTA model. Experimental results show that the proposed algorithm for solving the A-DWTA model has advantages of real-time and robustness. The obtained decision plan can complete the operational mission in fewer stages and be adjusted adaptively by model and algorithm parameters.

Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic

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.

Research on Dynamic Weapon Target Assignment Based on Cross-Entropy

The weapon target assignment (WTA) is a classical problem of defense-related applications which is proved to be a NP-complete problem. In this paper, a practical and available dynamic weapon target assignment (DWTA) formulation is given which incorporates two meaningful and conflicting objectives, that is, minimizing weapon costs and maximizing combat benefits. As we know, heuristic methods have some shortcomings such as slow convergence speed and local optimum in solving the nonlinear integer optimization problem. To this end, a novel DWTA algorithm based on cross-entropy (CE) method is introduced, where the resources requirement condition for targets is taken into consideration. The CE method associates an estimation problem with the DWTA optimization problem, and then, the estimation problem is transformed into a convex optimization problem. The Karush–Kuhn–Tucker conditions are applied to solve the convex optimization problem, and the iteration formulas to find the optimal solution are deducted. Furthermore, in order to verify the performance of CE method in dealing with the DWTA problem, several simulations in different combat scenarios are implemented. The results reveal that, compared with the benchmark heuristic and Monte-Carlo (MC) methods, there are some notable advantages in solving the DWTA problem based on CE method with regard to the solution quality and time consumption.

Development of an accelerating hungarian method for assignment problems

The Hungarian method is a well-known method for solving the assignment problem. This method was developed and published in 1955. It was named the Hungarian method because two theorems from two Hungarian mathematicians were used. In 1957, it was noticed that this algorithm is strongly polynomial and has a complexity of order O(n 4 ) This is the reason why the Hungarian method is also known as the Kuhn-Munkres algorithm. Later on, in 1971 the complexity of the method was improved to order O(n 3 ) A smallest uncovered element is selected to create a single zero at every iteration. This is a weakness and is alleviated by selecting more than one smallest uncovered element thus creating more than one zero at every iteration to come up with what we now call the Accelerating Hungarian (AH) method. From the numerical illustration of the Hungarian method given in this paper, we require 6 iterations to reach optimality. It can also be shown that selecting a single smallest uncovered element (e s ) makes the Hungarian method inefficient when creating zeros. From the same numerical illustration of the proposed algorithm (AH) also given in this paper, it can be noted that only one iteration is required to reach optimality and that a total of six zeros are created in one iteration. Assignment model and the Hungarian method have application in addressing the Weapon Target Assignment (WTA) problem. This is the problem of assigning weapons to targets while considering the maximum probability of kill. The assignment problem is also used in the scheduling problem of physicians and medical staff in the outpatient department of large hospitals with multi-branches. The mathematical modelling of this assignment problem results in complex problems. A hybrid meta-heuristic algorithm SCA–VNS combining a Sine Cosine Algorithm (SCA) and Variable Neighbourhood Search (VNS) based on the Iterated Hungarian algorithm is normally used.