• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.91-99
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• 2014

TThis paper suggests an heuristic polynomial time algorithm to solve the optimal solution for QAP (quadratic assignment problem). While Hungarian algorithm is most commonly used for a linear assignment, there is no polynomial time algorithm for the QAP. The proposed algorithm derives a grid type layout among unit distances of a distance matrix. And, we apply max-flow/min-distance approach to assign this grid type layout in such a descending order way that the largest flow is matched to the smallest unit distance from flow matrix. Evidences from implementation results of the proposed algorithm on various numerical grid type QAP examples show that a solution to the QAP could be obtained by a polynomial algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.5
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• pp.427-436
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• 2010

This paper deals with a task assignment problem of multiple UAVs performing multiple tasks on multiple targets. The task assignment problem of multiple UAVs is a kind of combinatorial optimization problems such as traveling salesman problem or vehicle routing problem, and it has NP-hard computational complexity. Therefore, computation time increases as the size of considered problem increases. To solve the problem efficiently, approximation methods or heuristic methods are widely used. In this study, the problem is formulated as a mixed integer linear program, and is solved by a mixed integer linear programming and a genetic algorithm, respectively. Numerical simulations for the environment of the multiple targets, multiple tasks, and obstacles were performed to analyze the optimality and efficiency of each method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.211-220
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• 2013

This paper proposes an algorithm that easily finds an optimal solution for linear bottleneck assignment problems. It is either threshold or augmenting path algorithm that is generally used to solve the bottleneck assignment problem. This paper proposes a reverse-delete algorithm that follows 2 steps. Firstly, the algorithm deletes the maximum cost in a given matrix until it renders a single row or column. Next, the algorithm improves any solution that contains a cost exceeding the threshold value $c^*_{ij}$. Upon its application to 28 balanced assignment problems and 7 unbalanced problems, the algorithm is found to be both successful and simple.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.1133-1138
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• 2000

최소비용 선형 망 흐름 문제가 동등한 할당 문제로 변환될 수 있으며, 또한 선형 망 흐름 문제를 해결하기 위한 전통적 방법은 단순 싸이클 흐름을 변환시킴으로서 원시 비용을 개선하는 방법이다. Auction 알고리즘이 각각의 반복에서 분산계산을 제시하는 개체와 초기의 대상 가격을 선택하기 위하여 몇 개의 특별한 법칙과 함께 동일한 문제에 적용이 될 때, 개체는 ${\varepsilon}$-이완법의 형태에서 획득할 수 있다. 본 논문은 할당 문제를 해결하기 위한 방법으로 최소비용 흐름 문제를 일반화 시켜, 전형적인 반복에 최소비용 흐름 문제, 수학적 등가에 의한 최소비용 흐름 문제를 연구하였고, 최소비용 흐름 측면에서 수송문제의 확장과 ${\varepsilon}$-이완법을 도출하여 이를 PCB 설계에 응용하고자 하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1C
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• pp.90-97
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• 2009

The advantages of the orthogonal frequency division multiplexing (OFDM) are high spectral efficiency, resiliency to RF interference, and lower multi-path distortion. To further utilize vast channel capacity of the multiuser OFDM, one has to find the efficient adaptive subchannel and bit allocation among users. In this paper, we propose an 0-1 integer programming model formulating the optimal subchannel and bit allocation problem of the multiuser OFDM. We employ a dual-decomposition method that provides a tight linear programming (LP) relaxation bound. Simulation results are provided to show the effectiveness of the 0-1 integer programming model. MATLAB simulation on a system employing M-ary quardarature amplitude modulation (MQAM) assuming a frequency-selective channel consisting of three independent Rayleigh multi-paths are carried with the optimal subchannel and bit allocation solution generated by 0-1 integer programming model.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.37-45
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• 2022

In this paper, a multi agent-multi task assignment problem, which is a representative problem of combinatorial optimization, is presented. The objective of the problem is to determine the coordinated agent-task assignment that maximizes the sum of the achievement rates of each task. The achievement rate is represented as a concave down increasing function according to the number of agents assigned to the task. The problem is expressed as an NP-hard problem with a non-linear objective function. In this paper, to solve the assignment problem, we propose a hybrid cross-entropy algorithm as an effective and efficient solution methodology. In fact, the general cross-entropy algorithm might have drawbacks (e.g., slow update of parameters and premature convergence) according to problem situations. Compared to the general cross-entropy algorithm, the proposed method is designed to be less likely to have the two drawbacks. We show that the performances of the proposed methods are better than those of the general cross-entropy algorithm through numerical experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11A
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• pp.1095-1104
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• 2008

In multiuser OFDM systems, subcarrier and bit allocation plays an important role for the efficient resource use. However, in multiuser adaptive allocation as a non-linear problem, it is impractical to compute all to get the best solution because of the complexity. We set the goal of minimizing the transmit power while satisfying the BER and minimum bits required to transmit through the highest fitness combination of subcarriers and users. The proposed improved genetic algorithm employs the diversity of adaptive allocation more than existing genetic algorithm. Therefore, from the numerical simulation results, we find that the proposed heuristic algorithm has more performance than the existing algorithms.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.10
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• pp.60-66
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• 2004

Redundant thrusters are generally adopted to satellite designs for a highly reliable attitude control system. So efficient redundancy management is required to take advantage of these redundant thrusters. In this paper, control allocation method is proposed as a method for controlling redundant thrusters. Control allocation is a method to calculate optimal distribution on redundant controls for realizing desired forces/torques. It is shown that a control allocation problem for redundant thrusters is formulated as a linear programming problem which minimizes fuel consumptions with thrusters, constraints. We also show that the proposed method is more efficient than an existing method by numerical examples.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.61-69
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• 2022

A multi-agent system is a system that aims at achieving the best-coordinated decision based on each agent's local decision. In this paper, we consider a multi agent-multi task assignment problem. Each agent is assigned to only one task and there is a completion probability for performing. The objective is to determine an assignment that maximizes the sum of the completion probabilities for all tasks. The problem, expressed as a non-linear objective function and combinatorial optimization, is NP-hard. It is necessary to design an effective and efficient solution methodology. This paper presents an approximation algorithm using submodularity, which means a marginal gain diminishing, and demonstrates the scalability and robustness of the algorithm in theoretical and experimental ways.