Weapon Target Assignment Research Articles

Solving Cooperative Anti-Missile Weapon-Target Assignment Problems using Hybrid Algorithms based on Particle Swarm and Tabu Search

Taking soft and hard weapon of a single warship cooperative anti-missile as background, under the fully research on the shipboard hard and soft weapon layered defense model, the firepower unit correlation matrix is put forward, which could simplify the problem complexity, minimizing the negative impact of the combination of firepower units while fighting against the same direction incoming missile targets. At the same time, in traditional weapon-target assignment, the maximum probability of joint damage or the minimum total expected survival rate of the target is usually set as the optimization goal, which result in firepower resources great waste. Aim to this problem, the minimum resource consumption model is established as the optimization goal, and we take the kill probability to the targets as an important constraint condition. The value of kill probability to each target can be set according to the incoming targets threaten degree and the firepower resources consumption. The minimum resource consumption model is solved by the hybrid optimized algorithm based on particle swarm and tabu search, the simulation results show that this method is more reasonable and feasible, in line with the realistic scenarios due to the algorithm could stop calculate at any time and give the current best solution.

Weapon‐Target Assignment Problem by Multiobjective Evolutionary Algorithm Based on Decomposition

The weapon‐target assignment (WTA) problem is a key issue in Command & Control (C2). Asset‐based multiobjective static WTA (MOSWTA) problem is known as one of the notable issues of WTA. Since this is an NP‐complete problem, multiobjective evolutionary algorithms (MOEAs) can be used to solve it effectively. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a practical and promising multiobjective optimization technique. However, MOEA/D is originally designed for continuous multiobjective optimization which loses its efficiency to discrete contexts. In this study, an improved MOEA/D is proposed to solve the asset‐based MOSWTA problem. The defining characteristics of this problem are summarized and analyzed. According to these characteristics, an improved MOEA/D framework is introduced. A novel decomposition mechanism is designed. The mating restriction and selection operation are reformulated. Furthermore, a problem‐specific population initialization method is presented to improve the efficiency of the proposed algorithm, and a novel nondominated solution‐selection method is put forward to handle the constraints of Pareto front. Appropriate extensions of four MOEA variants are developed in comparison with the proposed algorithm on some generated scenarios. Extensive experiments demonstrate that the proposed method is effective and promising.

An Application of Quantum-inspired Genetic Algorithm for Weapon Target Assignment Problem

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the...

MOQPSO-D/S for Air and Missile Defense WTA Problem under Uncertainty

Aiming at the shortcomings of single objective optimization for solving weapon target assignment (WTA) and the existing multiobjective optimization based WTA method having problems being applied in air and missile defense combat under uncertainty, a fuzzy multiobjective programming based WTA method was proposed to enhance the adaptability of WTA decision to the changes of battlefield situation. Firstly, a multiobjective quantum-behaved particle swarm optimization with double/single-well (MOQPSO-D/S) algorithm was proposed by adopting the double/single-well based position update method, the hybrid random mutation method, and the two-stage based guider particles selection method. Secondly, a fuzzy multiobjective programming WTA model was constructed with consideration of air and missile defense combat’s characteristics. And, the uncertain WTA model was equivalently clarified based on the necessity degree principle of uncertainty theory. Thirdly, with particles encoding and illegal particles adjusting, the MOQPSO-D/S algorithm was adopted to solve the fuzzy multiobjective programming based WTA model. Finally, example simulation was conducted, and the result shows that the WTA model constructed is rational and MOQPSO-D/S algorithm is efficient.

Dynamic weapon target assignment based on intuitionistic fuzzy entropy of discrete particle swarm

Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm (DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization (IFDPSO) and makes it applied to Dynamic Weapon Target Assignment (WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.

Optimization of weapon-target assignment problem by intuitionistic fuzzy genetic algorithm

Aiming at the shortcoming of precocity and slow convergence in the application of traditional algorithms to solve the Weapon-Target Assignment (WTA) problem, this paper proposed an intuitionistic fuzzy genetic algorithm that combined with simulated annealing Meta-Lamarckian learning strategy and adaptive mutation to improve the efficiency and speed of solving WTA problem. Firstly, it considered the various constraint functions of WTA problem, in which make the threat of remaining targets minimum and the damage from attacks maximum, established the mathematical model. Next, it defined the membership and non-membership functions of object and constraint function, and built the intuitionistic fuzzy WTA model on the basis of the “min-max” operator. Then, this paper designed a strategy of Meta-Lamarckian learning for simulated annealing and adaptive mutation to enhance the capability of local search and the speed of upper convergence for the algorithm. Finally, this method is effective via the simulation and the analysis of comparison with GA, PSO.

A Parallel Simulated Annealing Algorithm for Weapon-Target Assignment Problem

Weapon-target assignment (WTA) is a combinatorial optimization problem and is known to be NP-complete. The WTA aims to best assignment of weapons to targets to minimize the total expected value of the surviving targets. Exact methods can solve only small-size problems in a reasonable time. Although many heuristic methods have been studied for the WTA in the literature, a few parallel methods have been proposed. This paper presents parallel simulated algorithm (PSA) to solve the WTA. The PSA runs on GPU using CUDA platform. Multi-start technique is used in PSA to improve quality of solutions. 12 problem instances (up to 200 weapons and 200 targets) generated randomly are used to test the effectiveness of the PSA. Computational experiments show that the PSA outperforms SA on average and runs up to 250x faster than a single-core CPU.

A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem

The weapon-target assignment (WTA) problem, known as an NP-complete problem, aims at seeking a proper assignment of weapons to targets. The biobjective WTA (BOWTA) optimization model which maximizes the expected damage of the enemy and minimizes the cost of missiles is designed in this paper. A modified Pareto ant colony optimization (MPACO) algorithm is used to solve the BOWTA problem. In order to avoid defects in traditional optimization algorithms and obtain a set of Pareto solutions efficiently, MPACO algorithm based on new designed operators is proposed, including a dynamic heuristic information calculation approach, an improved movement probability rule, a dynamic evaporation rate strategy, a global updating rule of pheromone, and a boundary symmetric mutation strategy. In order to simulate real air combat, the pilot operation factor is introduced into the BOWTA model. Finally, we apply the MPACO algorithm and other algorithms to the model and compare the data. Simulation results show that the proposed algorithm is successfully applied in the field of WTA which improves the performance of the traditional P-ACO algorithm effectively and produces better solutions than the two well-known multiobjective optimization algorithms NSGA-II and SPEA-II.

A New Approach to Weapon-Target Assignment in Cooperative Air Combat

A new approach to solving weapon-target assignment (WTA) problem is proposed in this paper. Firstly, relative superiority that lays the foundation for assignment is calculated based on the combat power energy of the fighters. Based on the relative superiority, WTA problem is formulated. Afterwards, a hybrid algorithm consisting of improved artificial fish swarm algorithm (AFSA) and improved harmony search (HS) is introduced and furthermore applied to solve the assignment formulation. Finally, the proposed approach is validated by eight representative benchmark functions and two concrete cooperative air combat examples. The results show that the approach proposed in this paper achieves good performances in solving WTA problem in cooperative air combat.

A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for Cooperative Air Combat DWTA

A hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm is proposed to solve cooperative air combat dynamic weapon target assignment (DWTA). First, based on the threshold of damage probability and time window constraints, a new cooperative air combat DWTA multiobjective optimization model is presented, which employs the maximum of the target damage efficiency and minimum of ammunition consumption as two competitive objective functions. Second, in order to tackle the DWTA problem, a mixed MODPSO and neighborhood search algorithm is proposed. Furthermore, the repairing operator is introduced into the mixed algorithm, which not only can repair infeasible solutions but also can improve the quality of feasible solutions. Besides, the Cauchy mutation is adopted to keep the diversity of the Pareto optimal solutions. Finally, a typical two-stage DWTA scenario is performed by HMODPSO and compared with three other state-of-the-art algorithms. Simulation results verify the effectiveness of the new model and the superiority of the proposed algorithm.

Designing a Framework for Target-Site Assignment in Naval Combat Management

<p>In this study, using operational research techniques, a model has been presented to assess battlefield threat, to prioritise aggressive targets, to evaluate the capability of own sites and the risks of the conflict with the targets, to define conflict scenarios and finally to select the best scenario using an assignment model. The above proceedings were added as an intermediate phase of target-site assignment, called ‘deciding the best conflict scenario’, to the ‘threat assessment’ and ‘weapon-target assignment’ in the naval combat management system. For each of the own site, the data collected from the environment together with the panels of experts are shown in a two-dimensional matrix, in which the four areas of the matrix represent the conflict scenarios. Considering that the study was done in a simulated environment, the expert’s verification and the convergence of the results in Monte Carlo method were used to validate the research. The proposed model can offer optimised decision to the operational commander through predicting the battlefield and managing the site’s capacity and the interaction in between during the combat.</p>

명중률의 불확실성을 고려한 추계학적 무장-표적 할당 문제

명중률의 불확실성을 고려한 추계학적 무장-표적 할당 문제

Decentralized Cooperative Control Methods for the Modified Weapon–Target Assignment Problem

Weapon–target assignment is a combinatorial optimization problem in which a set of weapons must selectively engage a set of targets. In its decentralized form, it is also an important problem in autonomous multi-agent robotics. In this work, decentralized methods are explored for a modified weapon–target assignment problem in which weapons seek to achieve a prespecified probability of kill on each target. Three novel cost functions are proposed that, in cases with low agent-to-target ratios, induce behaviors that may be preferable to the behaviors induced by classical cost functions. The performance of these proposed cost functions is explored in simulation of both homogeneous and heterogeneous engagement scenarios using airborne autonomous weapons. Simulation results demonstrate that the proposed cost functions achieve desired behaviors in cases with low agent-to-target ratios where efficient use of weapons is particularly important.

The solution of target assignment problem in command and control decision-making behaviour simulation

ABSTRACTC2 (command and control) has been understood to be a critical military component to meet an increasing demand for rapid information gathering and real-time decision-making in a dynamically changing battlefield environment. In this article, to improve a C2 behaviour model’s reusability and interoperability, a behaviour modelling framework was proposed to specify a C2 model’s internal modules and a set of interoperability interfaces based on the C-BML (coalition battle management language). WTA (weapon target assignment) is a typical C2 autonomous decision-making behaviour modelling problem. Different from most WTA problem descriptions, here sensors were considered to be available resources of detection and the relationship constraints between weapons and sensors were also taken into account, which brought it much closer to actual application. A modified differential evolution (MDE) algorithm was developed to solve this high-dimension optimisation problem and obtained an optimal assignment plan with high efficiency. In case study, we built a simulation system to validate the proposed C2 modelling framework and interoperability interface specification. Also, a new optimisation solution was used to solve the WTA problem efficiently and successfully.

Developing Mathematical Formulations for the Integrated Problem of Sensors, Weapons, and Targets

Although sensor resource management and weapon–target assignment problems have been well studied, there is currently no well-posed mathematical formulation that captures the interactions of these problems in the context of a missile defense scenario. This paper gives a representative example to illustrate this fact; then, it poses a method for navigating the abstraction space for the integrated problem of sensors, weapons, and targets based on the specification of information sharing between subproblems. This method begins by outlining a set of formulation classes to specify the way in which information is shared; from this set, some formulation schemata (particular instantiations of a mathematical models that subscribe to the rules of a formulation class) are suggested. A set of goodness criteria for selecting which formulation classes and formulation schemata best address the problem-absent simulation results is considered. Last, the benefits and shortcomings of these formulation classes and formulation schemata, as well as areas for future work, are discussed.

A Mathematical Approach to Neuromarketing: A Weapon – Target Assignment Model

This study developed a nonlinear mathematical optimization model. The aim of the study is to look neuromarketing from a mathematical perspective. The model originates from the wellknown weapon target assignment problem in military operations research. This study proposes that the model can be properly adapted to neuroscience and business applications. The objective of the model is maximizing the total expected satisfaction ratio of people by understanding the functioning of their brain activities under neuroscientific and budget constraints. The recipe is applied to neuromarketing by a hypothetical example which is solved by MS Excel’s Solver.

Adaptive chaos parallel clonal selection algorithm for objective optimization in WTA application

This paper presents a novel objective optimization approach based on clonal selection algorithm (CSA) to solve the problems of weapon-target assignment (WTA) in warship formation antiaircraft application. The proposed CSA, namely adaptive chaos parallel clonal selection algorithm (ACPCSA), combines the benefits of chaos theory and parallel population classification to realize the population initialization and population update. In this algorithm, Chaos regeneration and Chaos disturbance are creatively employed to design population initialization operator and hyper-mutation operator. And parallel population classification is adopted to design parallel mechanism for all sub-populations, which can keep the population diversity according to affinity. Besides, CSA is improved by adaptive clonal proliferation operator, antibody inhibition operator and antibody circulation supplement operator, where operators can improve global optimization ability and local searching ability. Finally, typical scenario is performed and compared by implementation of other algorithms. Simulation results show that the proposed ACPCSA has good optimization performance in terms of search accuracy and convergence flexibility, which can provide an effective way to solve WTA in warship formation antiaircraft application.

Optimal Threshold Policy for Sequential Weapon Target Assignment

We investigate a variant of the classical Weapon Target Assignment (WTA) problem, wherein N targets are sequentially visited by a bomber carrying M homogenous weapons. We are interested in the optimal assignment of weapons to targets. A weapon launched by the bomber destroys the jth target with probability pj and upon successful elimination, the bomber earns a positive reward rj. There is feedback in that the bomber, upon deploying a weapon, is notified whether or not it successfully destroyed the target. Whereupon, it decides whether to move on to the next target or allocate an additional weapon to the current target. We provide a tractable solution method for computing the optimal closed loop control policy that results in maximal expected total reward. Moreover, we show that a thresholding policy is optimal, wherein a weapon from an inventory of k weapons is dropped on the jth target iff k exceeds a stage dependent threshold value, κ(j).

Effect-based weapon-target assignment optimization with collateral damage under control

In this work we optimize an effect-based weapon-target assignment that does not exceed a pre-determined upper bounds P1 ,P 2 ,...,P k for the collateral damage of k types of assets. That is, for given P1 ,P 2 ,...,P k we optimize weapon-target assignments with respect to kill overhead by minimizing overkill of targets and assuring that the kill probabilities of friendly asset types do not exceed P1 ,P 2 ,...,P k. Mathematics Subject Classification: 90B80, 90C27, 68W25, 68Q25

Effect-based weapon-target assignment with minimised collateral damage

We present an advanced algorithm that assigns weapons to targets in such a way that the desired given effects are satisfied with minimal overkill (i.e., minimal excess beyond the desired effects) and minimal collateral damage. The most obvious and natural effects on targets are represented by kill probabilities or percentages of damage these targets. Hence, our algorithm optimises weapon-target assignments with respect to the given desired effects by minimising overkill and minimising collateral damage. Our computation results included in this paper suggest that the benefit gain of decreased collateral damage obtained by our algorithm decisively overcompensates the loss/sacrificed benefit of optimised assignment related to overkill.