• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.55-58
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• 2003

본 논문에서는 유전자 알고리즘을 이용한 무장할당 문제를 제안하였다. 무장할당이란 적의 공격으로부터 방어대상물의 손상을 최소화하거나 적의 공격물 또는 표적의 격추 확률이 최대가 되도록 표적에 대한 방어무기의 적절한 할당을 목적으로 하는 최적화 문제로서, 본 논문에서는 무장할당 문제에 전역 최적화의 강점을 가진 유전자 알고리즘을 적용하였다. 무장할당문제에 적합한 유전자 알고리즘 형태와 파라메타를 선정하는 방법을 제시하였고, 시뮬레이션을 통해서 기존의 전통적인 최적화 기법과의 성능 비교를 수행한 결과, 제안된 방법이 우수함을 입증하였다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.12
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• pp.856-864
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• 2019

The weapon-target assignment (WTA) problem is a matter of effectively allocating weapons to a number of threats. The WTA in a rapidly changing dynamic environment of engagement must take into account both of properties of the threat and the weapon and the effect of the previous decision. We propose a method of applying the Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, a kind of meta-heuristic method, to derive optimal solution for a dynamic WTA problem. Firstly, we define a dynamic WTA problem and formulate a mathematical model for applying the algorithm. For the purpose of the assignment strategy, the objective function is defined and time-varying constraints are considered. The dynamic WTA problem is then solved by applying the GRASP algorithm. The optimal solution characteristics of the formalized dynamic WTA problem are analyzed through the simulation, and the algorithm performance is verified via the Monte-Carlo simulation.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.118-126
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• 2018

In order to dominate the multiple-targets of high threat in the initial stage of combat, it is necessary to maximize the combat effect by rapidly firing as many weapons as possible within a short time. Therefore, it is mandatory to establish the effective weapon allocation and utilize them for the combat. In this paper, we propose a weapon assignment algorithm for rapid reaction in multi-target and multi-weapon environments. The proposed algorithm maximizes the combat effect by establishing the fire plan that enables the rapid action with the operation of low complexity. To show the superiority of our algorithm, we implement the evaluation and verification of performances through the simulation and visualization of our algorithm. Our experimental results show that the proposed algorithm perform the effective weapon assignment, which shows the high target assignment rate within the fast hour even under the large-scale battle environments. Therefore, our proposed scheme are expected to be highly useful when it is applied to real weapon systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.539-544
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• 2003

The weapon-target assignment problem is solved using a genetic algorithm in this paper. The weapon-target assignment is an optimization problem which minimizes damages from enemy s attack or maximizes the kill probability of targets. Genetic algorithm is applied in this paper since it usually converges to a near global optimal solution. A specific structure of genetic algorithm which is suitable for the weapon-target assignment problem is proposed. A guideline selecting associated parameters is investigated through simulations. Comparition of the proposed method with several traditional optimization techniques for the weapon-target assignment problem shows the validity of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.227-231
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• 2004

본 연구에서는 군용선 설계 시 중요한 요소인 무장탑재 및 무장 할당 문제 해결을 위해, ACO(Ant Colony Optimization) 알고리즘과 Lanchester 법칙이 결합된 방법론을 제안하고 적용 결과를 검토하는 것을 내용으로 하고 있다.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.269-270
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• 2019

무장 비행 궤적 간 간섭을 식별하여 아군 무장간 충돌을 방지함으로써 단위 시간 당 교전 효과를 극대화하는 것은 다무장 대지 무기체계 운용에 있어서 필수적이다. 기존 연구에서는 연산 부하의 최소화를 목표로 3차원 무장 비행 궤적을 2차원 평면의 사격선으로 변환하여 간섭을 식별하는 알고리즘을 제안하였다. 그러나 기존 연구는 2차원 평면에서 사격선간 교차점이 발생할 경우 간섭으로 식별하고, 이에 대한 무장 할당을 해제함으로써 무장 활용도를 감소시키는 문제가 있었다. 이를 고려하여 본 논문에서는 교전 효과 최대화를 위한 효율적인 무장 궤적 간 간섭 식별 알고리즘을 제안한다. 제안하는 알고리즘은 2차원 평면에서 사격선간 교차점이 발생할 경우, 해당 사격선을 3차원 평면에서 비행 궤적 간 비교 연산을 수행하여 실제 간섭 여부를 식별함으로써 무장 활용도를 최대화하는 것이 가능하다. 성능 평가 결과, 제안하는 기법은 기존 기법에 비해 궤적 교차수는 최대 52.1% 감소하였으며, 그에 따른 표적 할당율은 최대 6.9% 향상됨을 보임으로써 그 우수성을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.1 no.2
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• pp.183-191
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• 1997

A neural network-based algorithm for the static weapon-target assignment (WTA) problem is Presented in this paper. An optimal WTA is one which allocates targets to weapon systems such that the total expected leakage value of targets surviving the defense is minimized. The proposed algorithm is based on a Hopfield and Tank's neural network model, and uses K x M processing elements called binary neuron, where M is the number of weapon platforms and K is the number of targets. From the software simulation results of example battle scenarios, it is shown that the proposed method has better performance in convergence speed than other method when the optimal initial values are used.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.5
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• pp.365-372
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• 2021

This paper deals with an optimal Weapon-Target Assignment (WTA) algorithm for Closed-In Weapon Systems (CIWS), considering variable burst time. In this study, the WTA problem for CIWS is formulated based on Mixed Integer Linear Programming (MILP). Unlike the previous study assuming that the burst time is fixed regardless of the engagement range, the proposed method utilizes the variable burst time based on the kill probability according to the engagement range. Thus, the proposed method can reflect a more realistic engagement situation and reduce the reaction time of CIWS against targets, compared to the existing method. In this paper, we first reformulate the existing MILP-based WTA problem to accommodate the variable burst Time. The proposed method is then validated through numerical simulations with the help of a commercial optimization tool.

• Journal of the military operations research society of Korea
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• v.36 no.3
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• pp.29-42
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• 2010

In this study, the weapon-target assignment problem arising in military application of operations research is considered. We reformulated the problem in order to simplify the solution methods based on genetic algorithms and heuristics. Since the problem is well known as NP-complete and cannot be solved in polynomial time, such solution methods have been widely used to obtain good solutions. Two chromosome representations--target number representation and permutation representation--in genetic algorithm are compared. In addition, a construction heuristic and three improving heuristics are developed. Several experiments under the condition of transitivity rules in weapon's kill probability have been accomplished. It shows that the construction heuristic and exchange-based improving heuristic guarantees good solutions within a second and the performance of construction heuristic is sensitive to transitivity rules.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.11
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• pp.787-794
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• 2019

In this paper, a Mixed Integer Linear Programming(MILP) approach for solving optimal Weapon-Target Assignment(WTA) problem of multiple dissimilar Closed-In Weapon Systems (CIWS) is proposed. Generally, WTA problems are formulated in nonlinear mixed integer optimization form, which often requires impractical exhaustive search to optimize. However, transforming the problem into a structured MILP problem enables global optimization with an acceptable computational load. The problem of interest considers defense against several threats approaching the asset from various directions, with different time of arrival. Moreover, we consider multiple dissimilar CIWSs defending the asset. We derive a MILP form of the given nonlinear WTA problem. The formulated MILP problem is implemented with a commercial optimizer, and the optimization result is proposed.