• The KIPS Transactions:PartA
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• v.18A no.2
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• pp.69-74
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• 2011

The minimum dominating set problem of a graph G is to find a smallest possible dominating set. The minimum dominating set problem is a well-known NP-complete problem such that it cannot be solved in polynomial time. Heuristic or approximation algorithm, however, will perform well in certain area of application. In this paper, we suggest three different simulated annealing algorithms and experimentally show better efficiency improvement by applying these algorithms to the graph instances developed by DIMACS.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.111-117
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• 2015

The exam scheduling problem has been classified as nondeterministic polynomial time-complete (NP-complete) problem because of the polynomial time algorithm to obtain the exact solution has been unknown yet. Gu${\acute{e}}$ret et al. tries to obtain the solution using linear programming with $O(m^4)$ time complexity for this problem. On the other hand, this paper suggests chromatic number algorithm with O(m) time complexity. The proposed algorithm converts the original data to incompatibility matrix for modules and graph firstly. Then, this algorithm packs the minimum degree vertex (module) and not adjacent vertex to this vertex into the bin $B_i$ with color $C_i$ in order to exam within minimum time period and meet the incompatibility constraints. As a result of experiments, this algorithm reduces the $O(m^4)$ of linear programming to O(m) time complexity for exam scheduling problem, and gets the same solution with linear programming.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.185-191
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• 2023

To the exact cover problem that remains NP-complete to which no polynomial time algorithm is made available, this paper proposes a linear time algorithm that yields an optimal solution. The proposed algorithm makes use of the set cover problem's major feature which states that "no identical element shall be included in more than one covering set". To satisfy this criterion, the proposed algorithm initially selects a subset with the minimum cardinality and deletes those that contain the cardinality identical to that of the selected subset. This process is repeatedly performed on remaining subsets until the final solution is obtained. Provided that the solution is unattainable, it selects subsets with the maximum cardinality and repeats the same process. The proposed algorithm has not only obtained the optimal solution with ease but also proved its wide applicability on N-queens problems, hence disproving the NP-completeness of the exact cover problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.540-548
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• 2012

The Weapon Target Allocation(WTA) problem is the NP-Complete problem. The WTA problem is that the threatful air targets are assigned by weapon of allies for killing the targets. A good solution of NP-complete problem is heuristic algorithms. Genetic algorithms are commonly used heuristic for global optimization, and it is good solution on the diverse problem domain. But there has been very little research done on the generation of their initial population. The initialization of population is one of the GA step, and it decide to initial value of individuals. In this paper, we propose to the population initialization method to improve a Genetic Algorithm. When it initializes population, the proposed algorithm reflects the characteristics of the WTA problem domain, and inherits the dominant gene. In addition, the search space widely spread in the problem space to find efficiently the good quality solution. In this paper, the proposed algorithm to verify performance examine that an analysis of various properties and the experimental results by analyzing the performance compare to other algorithms. The proposed algorithm compared to the other initialization methods and a general genetic algorithm. As a result, the proposed algorithm showed better performance in WTA problem than the other algorithms. In particular, the proposed algorithm is a good way to apply to the variety of situation WTA problem domain, because the proposed algorithm can be applied flexibly to WTA problem by the adjustment of RMI.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.4
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• pp.253-258
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• 2003

We consider the problem of grouping orders into lots. The problem is modelled by a graph G=(V,E), where each node ${\nu}{\in}V$ denotes order specification and its weight ${\omega}(\nu)$ the orders on hand for the specification. We can construct a lot simply from orders of single specification. For a set of nodes (specifications) ${\theta}{\subseteq}V$, if the distance of any two nodes in $\theta$ is at most d, it is also possible to make a lot using orders on the nodes. The objective is to maximize the number of lots with size exactly $\lambda$. In this paper, we prove that our problem is NP-Complete when $d=2,{\lambda}=3$ and each weight is 0 or 1. Moreover, it is also shown to be NP-Complete when $d=1,{\lambda}=3$ and each weight is 1,2 or 3.

• The KIPS Transactions:PartA
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• v.14A no.5
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• pp.317-326
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• 2007

We propose more enhanced heuristic for the GOSST(Grade of Services Steiner Minimum Tree) problem in this paper. GOSST problem is a variation of Steiner Tree problem and to find a network topology satisfying the G-Condition with minimum network construction cost. GOSST problem is known as one of NP-Hard or NP-Complete problems. In previous our research, we proposed a heuristic employing Direct Steiner Point Locating strategy with Distance Preferring MST building strategy. In this paper, we propose new Steiner point locating strategy, Zigzag Steiner point Locating strategy. Through the results of out experiments, we can assert this strategy is better than our previous works. The Distance Zigzag GOSST method which hires the Distance Preferring MST building strategy and Zigzag Steiner point Locating strategy defrays the least network construction cost and brings 31.5% cost saving by comparison to G-MST, the experimental control and 2.2% enhancement by comparison to the Distance Direct GOSST method, the best GOSST method in our previous research.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.131-136
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• 2015

The chemical tank loading problem has been classified as nondeterministic polynomial time (NP)-complete problem because of the polynomial-time algorithm to find the solution has been unknown yet. Gu$\acute{e}$ret et al. tries to obtain the optimal solution using linear programming package with $O(m^4)$ time complexity for chemical tank loading problem a kind of bin packing problem. On the other hand, this paper suggests the rule of loading chemical into minimum margin tank algorithm with O(m) time complexity. The proposed algorithm stores the chemical in the tank that has partial residual of the same kind chemical firstly. Then, we load the remaining chemical to the minimum marginal tanks. As a result of experiments, this algorithm reduces the $O(m^4)$ of linear programming to O(m) time complexity for NP-complete chemical tank loading problem.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.539-541
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• 1990

A new neural network model, based on the Hopfield's crossbar associative network, is presented and shown to be an effective tool for the NP-Complete problems. This model is applied to a class of layer assignment problems for VLSI routing. The results indicate that this modified Hopfield model improves stability and accuracy.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.341-342
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• 2008

GA(Genetic Algorithm)는 NP-Complete 도메인이나 NP-Hard 도메인 내의 문제들에 대해서 최적의 해를 찾기 위해서 많이 사용되어 지는 진화 컴퓨팅 방법 중 하나이다. 모바일 로봇 기술 중 경로계획은 NP-Complete 도메인 영역의 문제 중 하나로 이를 해결하기 위해서 Dijkstra 등의 그래프 이론을 이용한 연구가 많이 연구되었고 최근에는 GA등 진화 컴퓨팅 기법을 이용하여 최적의 경로를 찾는 연구가 많이 수행되고 있다. 그러나 모바일 로봇이 처리해야 될 공간 정보 크기가 증가함에 따라 기존 GA의 개체의 크기가 증가되어 게산 복잡도가 높아져 시간 지연등의 문제가 발생할 수 있다. 이는 모바일 로봇의 잠재적 오류로 발생될 수 있다. 공간 정보에는 동적이 장애물들이 예측 불허하게 나타 날 수 있는데 이것은 전역 경로 계획을 수립할 때 또한 반영되어야 된다. 본 논문에서는 k-means 클러스터링 기법을 이용하여 장애물 밀집도 및 거리 정보를 기반으로 공간정보를 k개의 군집 공간으로 재분류하여 이를 기반으로 N*M개의 그리드 개체 집단을 생성하여 최적 경로계획을 수립하는 GA를 제시한다.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.7
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• pp.377-383
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• 2002

The TSP(Traveling Salesman Problem) has been known as NP-complete, there have been various studies to find the near optimal solution. The nearest neighbor heuristic is more simple than the other algorithms which are to find the optimal solution. This paper designs and implements a new distributed nearest neighbor heuristic with bounding function for the TSP using the master/slave model of PVM(Parallel Virtual Machine). Distributed genetic algorithm obtains a near optimal solution and distributed nearest neighbor heuristic finds an optimal solution for the TSP using the near optimal value obtained by distributed genetic algorithm as the initial bounding value. Especially, we get more speedup using a new genetic operator in the genetic algorithm.