Genetic Fuzzy Tree Based Learning Algorithm Toward the Weapon-Target Assignment Problem
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- Jie Li 40 ,
- Rui Wang 41 ,
- Sulemana Nantogma 40 &
- Yang Xu 40
Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 861))
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- International Conference on Autonomous Unmanned Systems
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Weapon-target assignment (WTA) is essential ability for command and control (C2) systems. The requirement for real-time decision-making, heterogeneous combat platforms are required to make effective weapon-target assignment decisions to achieve interception of fast and multi-batch targets. Since it is difficult to form an accurate modeling of the incoming target ability and obtain a large amount of training data in actual combat exercises, this problem has become a representative problem of real-time decision-making under the constraints of small training samples. Inspired by the use of rules to make coordinated air defense decisions when manned, we propose a practical rule-based machine learning approach to solve this problem in this paper. Firstly, we model heterogeneous combat platforms into multi-agents system and use genetic fuzzy trees (GFT) to make weapon-target assignment decisions. Genetic algorithm (GA) is then employed to learn fuzzy rules and tune membership functions. To evaluate the performance of the proposed algorithm, we build a typical Surface Unmanned System air defense simulation scenario that employs an auto-fire strategy as baseline. The simulation results show that our approach demonstrates a superior performance over the auto-fire strategy and can greatly improve the interception efficiency with a small amount of training data.
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University of Electronic Science and Technology of China, Chengdu, China
Jie Li, Sulemana Nantogma & Yang Xu
Beijing Aerospace Automatic Control Institute, National Key Laboratory of Science and Technology on Aerospace Intelligence Control, Beijing, China
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Beijing HIWING Scientific and Technological Information Institute, Beijing, China
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Li, J., Wang, R., Nantogma, S., Xu, Y. (2022). Genetic Fuzzy Tree Based Learning Algorithm Toward the Weapon-Target Assignment Problem. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_165
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Since the end of last century, intelligent optimization algorithm has been developing vigorously with the maturity of computer technology. Among them, genetic algorithm (GA) is the earliest and most mature optimization algorithm, and has been well applied in solving weapon target assignment (WTA) problem. In this paper, the implementation of GA is introduced. Aiming at the defect that ...
Weapon target assignment (WTA) problem is the problem of assigning weapons to targets with the objective of minimizing the expected damage of targets. In this work, a GA with a novel crossover operator is proposed for the particular WTA problem. ... "Efficiently solving general weapon-target assignment problem by genetic algorithms with genetic ...
Research addressing the Weapon Target Assignment (WTA) Problem, the problem of assigning weapons to targets while considering their effective probability of kill, began with Manne's seminal work in 1958. ... An improved genetic algorithm for target assignment, optimization of naval fleet air defense. The Sixth World Congress on Intelligent ...
The Weapon-Target Assignment (WTA) problem is of military importance; it computes an optimal assignment of m weapons to n targets such that the expected total damage of the targets is maximized (or equivalently, the expected total survival possibility of the targets is minimized). ... The genetic algorithm with elite retention strategy is used ...
The sensor-weapon-target assignment (S-WTA) problem is a crucial decision issue in C4ISR. The cooperative engagement capability (CEC) of sensors and weapons depends on the S-WTA schemes, which can greatly affect the operational effectiveness. In this paper, a mathematical model based on the synthetical framework of the S-WTA problem is established, combining the dependent and independent ...
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 ...
A general weapon-target assignment (WTA) problem is to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force asset. Genetic algorithms (GAs) are widely used for solving complicated optimization problems, such as WTA problems. In this paper, a novel GA with greedy eugenics is proposed. Eugenics is a process of improving the quality of ...
Weapon target assignment (WTA) problem is the problem of assigning weapons to targets with the objective of minimizing the expected damage of targets. In this work, a GA with a novel crossover operator is proposed for the particular WTA problem. The idea is to use a benefit level to identify good genes. The proposed algorithm is implemented and ...
Adaptive immune genetic algorithm for weapon system portfolio optimization in military big data environment. Cluster Computing. 19, 3 (Jul. 2016), 1359--1372. Google Scholar Digital Library; Wang, W., Cheng, S. C., Zhang, Y. Z. 2008. Research on approach for a type of weapon target assignment problem solving by genetic algorithm.
Abstract. Weapon target assignment (WTA) problem is the problem of assigning weapons to targets with the objective of minimizing the expected damage of targets. In this work, a GA with a novel ...
A Genetic Algorithm with Domain Knowledge for Weapon-Target Assignment Problems 295 Sandell, N. R., and LeBlanc, R., 1997, "A Decision Aid for Theater Missile Defense," Pro-
The Weapon-Target Assignment (WTA) problem aims to assign a set of weapons to a number of assets (targets), such that the expected value of survived targets is minimized. The WTA problem is a nonlinear combinatorial optimization problem known to be NP-hard. This paper applies several existing techniques to linearize the WTA problem. One linearization technique (Camm et al. in Oper Res 50(6 ...
1. Introduction. The Weapon-Target Assignment (WTA) problem is of military importance. In this problem, we have a set of m weapons, W, and a set of n targets, T; the objective is to find an optimal assignment of weapons to targets such that the expected total damage of the targets is maximized (or equivalently, the expected total survival possibility of the targets is minimized).
In the Weapon-Target Assignment Problem, m enemy targets are inbound, each with a value V j representing the damage it may do. The defense has n weapons, and the probability that weapon i will kill target j is p ij.The problem is to assign the weapons to targets so as to reduce as much as possible the total expected value of the targets.
In air defense weapon-target assignment problem, the sensors are various radars, the actuators are weapon fire device, and the performance means to minimize the expected targets value. ... Lee, Z., Su, S., Lee, C.: A genetic algorithm with domain knowledge for weapon-target assignment problems. J. Chin. Inst. Engineers 25(3), 287-295 (2002 ...
On this basis, the genetic algorithm is used to solve the problem of antiaircraft weapon-target optimal assignment. Aiming at the slow convergence rate of genetic algorithm(GA), the individual and group extremum updating in particle swarm optimization(PSO) is used as the best individual preserving strategy in genetic algorithm, so as to improve ...
Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem Mathematical Problems in Engineering, Vol. 2019 Weapon-Target Assignment for Multi-to-Multi Interception With Grouping Constraint
In this paper, a novel genetic algorithm, including domain specific knowledge into the crossover operator and the local search mechanism for solving weapon‐target assignment (WTA) problems is proposed. The WTA problem is a full assignment of weapons to hostile targets with the objective of minimizing the expected damage value to own‐force ...
They considered this specific S-WTA problem as a kind of dynamic weapon-target assignment (DWTA) [23] problem and proposed an anytime algorithm based on decentralized cooperative auction to solve this problem. Chen et al. [24] proposed an improved particle swarm optimization (PSO) to solve a similar dependent S-WTA problem. In this algorithm ...
The Weapon-Target Assignment (WTA) problem can be formulated as a nonlinear integer programming problem and is known to be NP-complete. Generic algorithm and heuristic algorithm are widely used for solving it but hardly be good enough considering the disadvantages of each. We firstly transform the nonlinear integer constrained WTA problem into a linear integer problem and suggest genetic ...
Abstract: The sensor-weapon-target assignment (S-WT A) problem is a crucial decision issue. in C4ISR. The cooperative engagement capability (CEC) of sensors and weapons depends on the. S-WTA ...
The weapon target assignment (WTA) problem is a critical problem in command and control (Li, Huai & Wang, 2017); it was originally introduced by Manne (1958). ... Wang (2019) proposed a genetic algorithm based on variable value control to solve the problem. Although the performance of the proposed algorithm was improved compared with genetic ...
This is typically modeled using the Multiple Traveling Salesman Problem (MTSP) [7] [8], Mixed Integer Linear Programming (MILP) [9], and the Weapon-Target Assignment (WTA) model [10] [11], with the WTA model being particularly suitable for large-scale UAV swarms and extensive target task allocations. Modeling should closely reflect actual ...
Deng et al. (2010) proposed a version of differential evolution with the dual population as a genetic algorithm for WTAP. They considered their proposed approach with a population size of 30 and a generation size of 50. In the scenario, they used to show the algorithm's effectiveness, 3-3-1-1-4-2 weapon types were assigned to the target types, respectively, and a cost value of 1.4 was obtained.