Günsel, Emrah · OpenMETU (Middle East Technical University) · 2019
The Weapon-Target Assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research. Optimizing the WTA is about the selection of the most appropriate weapon for each target in the problem. Basically the aim is to have the maximum effect on targets. Different algorithms; branch and bound (B&B), genetic algorithm (GA), variable neighborhood search (VNS), are used to solve this problem. In this thesis, a more complex version of this problem is defined and adapted to fire support automation (Command Control Communication Computer Intelligence, C4I) systems. For each target, a weapon with appropriate ammunition, fuel, timing, status, risk is moved to an appropriate ammunitions, economy of fuel, risk analysis and time scheduling are all integrated into the solution. B&B, GA and VNS are used to solve static and dynamic WTA problem. Simulations have shown that GA and VNS are the best suited methods to solve the WTA problem.
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SONUC, Emrullah · Data Archiving and Networked Services (DANS) · 2024
This dataset corresponds to the Static Weapon Target Problem (WTA), the WTA Problem aims to find best assignment of weapons to targets, to minimize the expected damage of the defended area in order to increase chances of survival. This supports the paper: Emrullah Sonuc, Baha Sen, Safak Bayir (2017) A Parallel Simulated Annealing Algorithm For Weapon-Target Assignment Problem
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Jay Michael Rosenberger, Hee Sung Hwang, Ratna P. Pallerla et al. · Defense Technical Information Center (DTIC) · 2005
Dynamic command and control and battle management functions require fast and effective decision aids to provide optimal allocation of resources (object/sensor pairing, weapon/target assignment) for effective engagement and real-time battle damage assessment. The basic Weapon Target Assignment (WTA) problem considers the assignment of a set of platforms/weapons to a set of targets such that the overall expected effect is maximized. In the present study, we extend the basic WTA problem by allowing for multiple target assignments per platform, subject to the number of weapons available and their effectiveness. We formulate the problem as a linear integer programming problem and investigate two solution methods. The first method is a greedy approach based on the sequential application of the auction algorithm that was generalized for assigning n assets/resources to m targets. The second method is built on a branch-and-bound framework that enumerates feasible tours of assets/resources a process that can become computationally intensive with increasing number of sources and targets but will find an optimal solution. We provide results of Monte Carlo experiments and provide comparative evaluation of the two solution methods. Finally, we extend the brand-and bound technique to assigning multiple platforms per target and thereby demonstrate its utility for collaborative asset planning. While this study focuses on weapon target pairing for illustration purposes, the methods and results herein are readily applicable to sensor tasking and similar resource allocation problems.
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Patrick Hosein, Michael Athans, Decision Systems. · 1989
Abstract : We present a progress report on our recent results on the dynamic version of the Weapon to Target Assignment (WTA) problem. In this paper we will present result for the Asset-Based problem. In the static Asset-Based WTA problem, the offense launches missiles at valuable assets of the defense. The defense must assign its weapons to these missiles so as to minimize the damage they incur. In the dynamic version, this allocation is done in time stages such that the outcomes of the previous engagements can be used in making future assignments. We will provide a suboptimal algorithm for the Static Asset-Based WTA problem for the case of a single class of defense weapons. We will also propose a heuristic for the dynamic version of the problem. Numerical results will be provided to show that a dynamic strategy offers a significant cost improvement over a static one.
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Patrick Hosein, Michael Athans · DSpace@MIT (Massachusetts Institute of Technology) · 1989
Caption title. "To appear in Proc. 1989 Symposium on C2 research, Washington, D.C.
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Tolga Önel, Elif Bozkaya · DergiPark (Istanbul University) · 2021
Network-Centric Warfare (NCW) envisions taking the advantage of information technology developments by replacing traditional war management strategies. It affects both the fast and accurate decision-making process by providing organization and information exchange between military forces in geographically different locations. Combat management systems help to manage and maintain components such as sensors, weapons, platforms to create the tactical picture. As a result of the threat evaluation with a network-centric approach, effective defense strategies can be developed through the problem of weapon assignment and sensor allocation among military forces. In this paper, threat evaluation concept and parameters are firstly explained, and the static and dynamic target-based weapon assignment and sensor allocation problem is defined. Then, the concept of NCW and the problems that may be encountered are detailed.
Cited by 1Open access
Pederson, Debbie · Defense Technical Information Center (DTIC) · 2008
Weapon Target Assignment (WTA): Given a set of weapon batteries and a set of incoming targets, what is the assignment of interceptors to targets that will maximize the value of targets killed? *Difficult problem to solve optimally for large numbers of weapons/targets. *May need to be re-solved repeatedly as battlespace evolves. *Need good, quick solution method. Goal: Compute a schedulable allocation of interceptors to threats within a relatively short amount of time.
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Jiuyong Zhang, Xiaojing Wang, Chuanqing Xu et al. · Open Journal of Applied Sciences · 2012
Weapon Target Assignment is not only an important issue to use firepower, but also an important operational decision-making problem. As new intelligent algorithms, Genetic algorithm and ant colony algorithm are applied to solve Weapons-Target Assignment Problem. This paper introduces the Weapon-Target Assignment (WTA) and the mathematical model, and proposes ACGA algorithm which is the integration of genetic algorithm and ant colony algorithm then use ACGA algorithm to solve the Weapon-Target Assignment Problem. Calculations show that: when ACGA algorithm is used to solve Weapon -Target Assignment Problem, it has fast convergence and high accuracy.
Cited by 14Open access
Shang Gao, Zaiyue Zhang, Xiaoru Zhang et al. · 2007
An Immune Genetic Algorithm (IGA) is used to solve weapon-target assignment problem (WTA). The used immune system serves as a local search mechanism for genetic algorithm. Besides, in our implementation, a new crossover operator is proposed to preserve good information contained in the chromosome. A comparison of the proposed algorithm with several existing search approaches shows that the IGA outperforms its competitors on all tested WTA problems.
Cited by 18
Jaeoh Kim, Hong Han, Jae‐Jin Lee et al. · SSRN Electronic Journal · 2025
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Kuk-Kwon Park, Tae Young Kang, Chang-Kyung Ryoo et al. · Journal of the Korean Society for Aeronautical & Space Sciences · 2019
무장할당(Weapon-Target Assignment, WTA) 문제는 다수 위협과 다종의 무장을 효과적으로 할당하는 문제이다. 실제 급변하는 교전환경에서의 무장할당은 위협과 무장의 특성과 위협-무장 선정에 따른 영향성을 모두 고려해야한다. 본 논문에서는 동적 무장할당 문제에서의 최적해 도출을 위해 메타휴리스틱 방법의 일종인 Greedy Randomized Adaptive Search Procedure (GRASP) 알고리즘 적용 방안을 제안한다. 먼저 동적 무장할당 문제를 정의하고 알고리즘 적용을 위해 수학적 모델을 정식화한다. 무장할당 전략을 수립하기 위하여 목적함수를 정의하고 시간변화를 고려한 구속조건을 설정한다. 이를 바탕으로 GRASP 알고리즘을 동적 무장할당 문제에 적용한다. 교전 시뮬레이션을 통해 정식화한 무장할당 문제의 최적해 특성을 분석하며, Monte-Carlo 시뮬레이션을 통해 알고리즘 성능 검증을 수행한다.
Cited by 2
Erdi Acar, Saim Hatipoğlu, İhsan Yılmaz · Engineering Applications of Artificial Intelligence · 2023
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İsa Avcı, Mehmet YILDIRIM · Tehnicki vjesnik - Technical Gazette · 2022
The weapon target problem is a combinatorial optimization problem. It aims to have the weapons on target properly assigned for the intended purposes. When focused on its target, it does things with its effective attack research in mind. It is an ongoing problem program to minimize survivors. This study, using the weapon target assignment model calculates the expected probabilities on the target with the salp model. The nature of this SHA model is designed to be appropriately predicted for this particular use. The Salp Swarm Algorithm (SSA) is a metaheuristic method of methods approaching the solution set as an approximation. Optimum solution or optimum example is in a working example. This study was done with 12 problem examples (200 training and 200 targets with pleasure to observe, to test the efficiency of SSA). In the problem, the iteration resulted in optimum results at the end of the definite usage time. Best value included 48.355 for WTA1, 92.654 for WTA2, 174.432 for WTA3, 155.658 for WTA4, 250.784 for WTA5, 284.967 for WTA6, 247.458 for WTA7, 362.636 for WTA8, 524.732 for WTA9, 548.580 for WTA10, 601.654 for WTA11, and WTA16812. It was obtained by finding in 200,000 iterations and the result value was 50. After 200000 improvements, it was observed to relax to increase iteration. The use of barter when generating new solutions to the problem. To find out the fitness values, mean, best, and worst values were found.
Cited by 8Open access
Volle, Kyle · SMARTech Repository (Georgia Institute of Technology) · 2019
Weapon target assignment (WTA) is a combinatorial optimization problem in which a set of weapons must selectively engage a set of targets in order to minimize the expected survival value of the targets. In its distributed form, it is also an important problem in autonomous, multi-agent robotics. In this work, distributed methods are explored for a modified weapon target assignment problem in which weapons seek to achieve a specified probability of kill on each target. Three novel cost functions are proposed which, in cases with low agent-to-target ratios, induce behaviors which 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, as an example, airborne autonomous weapons. Simulation results demonstrate that the proposed cost functions achieve the specified desired behaviors in cases with low agent-to-target ratios where efficient use of weapons is particularly important. Additionally, a multi-objective version of the WTA problem is considered in which the quality of an assignment is dependent on both the total effectiveness of the weapons assigned to each target, and the relative timing of agents' arrival at their targets. Such timing constraints may be important in real-world scenarios where a mission planner wishes to enforce an element of surprise on each target. A fourth cost function is presented which couples weapon effectiveness and timing metrics into a combined cost. In cases where weapon-target closing speeds are limited to a certain range, this combined cost allows the inclusion of arrival time constraints in the assignment decision process. The performance of this new cost function is demonstrated through theoretical analysis and simulation. Results show that the proposed cost function balances the dual goals of optimizing effectiveness and arrival time considerations under closing speed limitations, and that a user-defined tuning parameter can be used to adjust the priority of the dual goals of sequenced arrival and achieving the desired probability of kill.
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Jun Chen, Jianwen Yang, Guanfeng Ye · 2015
This article takes the multi-weapon formation coordination attack ground target as the background. We have established the mathematical model of the ground maneuvering target with the constraint space factor. We design an anytime algorithm for solving this kind of optimization problem based on the idea of distributed cooperative auction algorithm and give the method to optimize the auction algorithm for DWTA problem. Our computational results show that by using these algorithms, relatively large instances of the DWTA problem can be solved near-optimally in a few seconds on a standard PC.
Cited by 9
J. P. Li, Li Yang, Jinwen Wang et al. · 2024
The Weapon-Target Assignment (WTA) problem represents a complex optimization issue in battlefield scenarios. Evolutionary-based approaches show potential in addressing the WTA problem, but they face challenges related to computational efficiency and the risk of getting trapped in local optima. To address these concerns, we propose a Subtraction-Average Assignment Optimization(SAAO) algorithm specifically designed to solve the WTA problem, which is based on the Subtraction-average-based optimization(SABO) algorithm. Through numerical experiments, we demonstrate that the SAAO algorithm exhibits significant advantages in algorithm optimization and convergence performance compared to the particle swarm optimization and SABO algorithms.
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Yuanfei Wei, Qifang Luo, Yongquan Zhou · Journal of Physics Conference Series · 2021
Abstract 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.
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Zeng XP(曾祥萍), Zhu YL(朱云龙), Nan L(南琳) et al. · SIA OpenIR (Chinese Academy of Sciences) · 2006
This paper presents a discrete particle swarm optimization (DPSO) to solve weapon-target assignment (WTA) problem. The proposed algorithm sponges the advantages of PSO and GA. Originally the greedy searching strategy is introduced into DPSO in which a priority set is constructed to control the local search and converge to the global optimum efficiently. Then the particles would be updated based on the information of priority set. Furthermore, the concept of "permutation" is employed to the update strategy. Finally, particles will be reinitialized as long as they are stagnated in the search space. The experimental results illustrate that the DPSO is a promising optimization method, which is especially useful for optimization problem with discrete variables.
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Jinho Lee, Myoungin Shin · Journal of the Korean Operations Research and Management Science Society · 2016
We consider a model that minimizes the total cost incurred by assigning available weapons to existing targets in order to reduce enemy threats, which is called the weapon target assignment problem (WTAP). This study addresses the stochastic versions of WTAP, in which data, such as the probability of destroying a target, are given randomly (i.e., data are identified with certain probability distributions). For each type of random data or parameter, we provide a stochastic optimization model on the basis of the expected value or scenario enumeration. In particular, when the probabilities of destroying targets depending on weapons are stochastic, we present a stochastic programming formulation with a simple recourse. We show that the stochastic model can be transformed into a deterministic equivalent mixed integer programming model under a certain discrete probability distribution of randomness. We solve the stochastic model to obtain an optimal solution via the mixed integer programming model and compare this solution with that of the deterministic model.
Cited by 2
曾祥萍, 朱云龙, 南琳 et al. · 2006
This paper presents a discrete particle swarm optimization (DPSO) to solve weapon-target assignment (WTA) problem. The proposed algorithm sponges the advantages of PSO and GA. Originally the greedy searching strategy is introduced into DPSO in which a priority set is constructed to control the local search and converge to the global optimum efficiently. Then the particles would be updated based on the information of priority set. Furthermore, the concept of permutation is employed to the update strategy. Finally, particles will be reinitialized as long as they are stagnated in the search space. The experimental results illustrate that the DPSO is a promising optimization method, which is especially useful for optimization problem with discrete variables.
Cited by 0