• Proceedings of the Korea Information Processing Society Conference
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• 2000.10b
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• pp.1373-1376
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• 2000

외판원 문제(Traveling Salesman Problem)는 주어진 n개의 도시들과 그 도시들간의 거리 비용이 주어졌을 매, 처음 출발도시에서부터 정확히 한 도시는 한 번씩만 방문하여 다시 출발도시로 돌아오면서 방문한 도시들을 연결하는 최소의 비용이 드는 경로를 찾는 문제로 최적해(optimal value)를 구하는 것은 전형적인 NP-완전 문제중의 하나이다[2,4,5, 8]. 따라서 이들의 수행시간을 줄이고자 하는 연구가 많이 진행된다. 본 논문에서는 외판원 문제의 최적의 해를 구하는데. 휴리스틱 알고리즘인 최근접 휴리스틱을 이용한다. 물론 수행 시간을 줄이고자 최적화 문제에서 좋은 성능을 보이는 유전 알고리즘 (Genetic Algorithm)으로 얻은 근사해(near optimal)를 초기 분기 함수로 사용하고, 근거리 통신망(Local Area Network)에 기반한 분산 처리 환경에서 여러 프로세서에 분산시켜 병렬성을 살린다.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.1151-1154
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• 2001

외판원 문제(Traveling Salesman Problem)는 주어진 n개의 도시들과 그 도시들간의 거리비용이 주어졌을 때, 모든 도시들을 정확히 한번씩만 방문하면서 걸린 비용이 최소가 드는 경로를 찾는 문제로 최적해(optimal)을 구하는 것은 전형적인 NP-완전 문제중의 하나이다. 따라서 외판원 문제를 해결하는 다양한 알고리즘들이 개발되고 있다. 특히 요즈음은 실제 생체 분자(bio-molecule)를 계산의 도구로 사용하는 새로운 계산 방법인 DNA 컴퓨팅은 DNA 분자가 잠재적으로 가지고 있는 막대한 병렬성을 이용해서 NP-완전 문제들을 해결하고자 하는 연구들이 땀이 진행되고 있다. 그러나 아직 실제 생체 분자의 특성을 잘 반영하는 계산 모델이나 분자 생물학에서 사용하는 연산들이 많이 개발되지 알아 계산 효율이 비교적 좋지 않다. 따라서 본 논문에서는 외판원 문제를 해결하기 위한 DAN컴퓨팅의 새로운 중합 효소 연쇄 반응(Polymerase Chain Reaction, PCR) 연산을 개발하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.783-786
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• 2008

외판원문제(Traveling Salesman problem: TSP)는 전형적인 조합최적화 문제로 위치하는 n개의 모든 지점을 오직 한번씩만 방문하는 순회경로를 결정하는 과정에서 순회비용 또는 순회거리를 최소화한다. 따라서 본 논문에서는 종래의 NP-hard문제로 널리 알려진 TSP를 해결하기 위해서 메타 휴리스틱기법 중에서 가장 널리 이용되고 있는 유전 알고리즘(Genetic Algorithm: GA)을 이용한다. 마지막으로, 유전 알고리즘을 이용해 외판원문제에 적합한 성능을 보이는 유전 연산자를 찾아내기 위해 수치 실험을 통해 그 성능에 대한 평가를 한다.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.636-643
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• 2020

The differential evolution algorithm is one of the meta-heuristic techniques developed to solve the real optimization problem, which is a continuous problem space. In this study, in order to use the differential evolution algorithm to solve the traveling salesman problem, which is a discontinuous problem space, a random key representation method is applied to the differential evolution algorithm. The differential evolution algorithm searches for a real space and uses the order of the indexes of the solutions sorted in ascending order as the order of city visits to find the fitness. As a result of experimentation by applying it to the benchmark traveling salesman problems which are provided in TSPLIB, it was confirmed that the proposed differential evolution algorithm based on the random key representation method has the potential to solve the traveling salesman problems.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.19-27
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• 2007

We have implemented a WWW homepage which finds an optimal route for users. There already exist many web sites which provide the optimal route when a start and a destination cities are given. However, none of them can find the optimal route when a number of cities to be visited. The problem of finding the optimal route starting at a given start city and visiting through all the given intermediate cities and finally returning to the start city is called Travelling Sales Person(TSP) problem. TSP is a well known exponential time complexity problem. We have implemented an artificial intelligent search algorithm for TSP on our homepage. The main feature of our algorithm is that the destination may not be the same as the start city whereas all of the existing heuristic algorithms for TSP assume that the start and the destination cities are the same. The web page asks a user to select all the cities he or she wants to visit(including start and destination city), then it finds a sequence of the cities such that the user would travel minimum distance if he or she visits the cities in the order of the sequence. This paper presents algorithms used in the homepage.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.75-82
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• 2013

This paper proposes a $O(n^2)$ polynomial time algorithm to obtain optimal solution for Traveling Salesman problem that is a NP-complete because polynomial time algorithm has been not known yet. The biggest problem in a large-scale Traveling Salesman problem is the fact that the amount of data to be processed is $n{\times}n$, and thus as n increases, the data increases by multifold. Therefore, this paper proposes a methodology by which the data amount is first reduced to approximately n/2. Then, it seeks a bi-directional route at a random point. The proposed algorithm has proved to be successful in obtaining the optimal solutions with $O(n^2)$ time complexity when applied to TSP-1 with 26 European cities and TSP-2 with 46 cities of the USA. It could therefore be applied as a generalized algorithm for TSP.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.17-24
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• 2020

The traveling salesman problem is an algorithmic problem tasked with finding the shortest route that a salesman visits, visiting each city and returning to the started city. Due to the exponential time complexity of TSP, it's hard to implement on cases like amusement park or delivery. Also, TSP is hard to meet user's demand that is associated with multi-dimensional attributes like travel time, interests, waiting time because it uses only one attribute - distance between nodes. This paper proposed Top-n Skyline-Multi Dimension TSP to resolve formerly adverted problems. The proposed algorithm finds the shortest route faster than the existing method by decreasing the number of operations, selecting multi-dimensional nodes according to the dominance of skyline. In the simulation, we compared computation time of dynamic programming algorithm to the proposed a TS-MDT algorithm, and it showed that TS-MDT was faster than dynamic programming algorithm.

• Journal of the Korea Computer Industry Society
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• v.2 no.10
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• pp.1349-1354
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• 2001

In the Traveling Salesman Problem(TSP), a set of N cities is given and the problem is to find the shortest route connecting them all, with no city visited twice and return to the city at which it started. Since TSP is a well-known combinatorial optimization problem and belongs to the class of NP-complete problems, various techniques are required for finding optimum or near optimum solution to the TSP. Especially DNA computing, which uses real bio-molecules to perform computations supported by molecular biology, has been studied by many researchers to solve NP-complete problem using massive parallelism of DNA computing. Though very promising, DNA computing technology of today is inefficiency because the effective computing models and operations reflected the characteristics of bio-molecules have not been developed yet. In this paper, I design new Polymerase Chain Reaction(PCR) operations of DNA computing to solve TSP.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.81-85
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• 1999

본 연구에서는 한 유연생산기계에서 생산해야 되는 제품들의 공구교환횟수의 합을 최소로 하는 생산순서를 결정하는 문제를 다룬다. 이 문제를 풀기 위한 방법으로 외판원문제와 관련된 발견적 기법을 적용할 수 있다. 이 때 연속으로 생산해야 될 두 제품사이의 공구교환횟수는 외판원문제에서의 방문해야 될 두 지점사이의 거리에 해당된다. 만약 각 제품이 필요로 하는 공구의 갯수가 공구장착장치의 용량과 같다면 두 제품사이의 공구교환횟수를 정확히 계산할 수 있지만 그렇지 않다면 각 제품이 필요로 하는 공구의 수와 종류가 다르고 제품을 생산하기 전에 공구장착장치에 장착되어 있는 공구의 종류에 따라서 두 제품사이의 공구교환횟수가 달라지므로 정확하게 추정하기는 힘들다. 이러한 공구교환횟수를 추정하는 방법으로 기존의 방법들은 단지 두 제품사이의 생산에 필요한 공구만을 고려하였으나 본 연구에서는 제품생산순서의 전체적인 관점에서 두 제품사이의 공구교환횟수의 상한값을 기초로 추정하는 새로운 방법을 제시한다. 이 새로운 방법의 우수성을 많은 예제를 통하여 기존에 제시된 다른 방법들과 비교하여 보여준다.

• Tunnel and Underground Space
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• v.24 no.1
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• pp.11-20
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• 2014

This study analyzed the optimal routes of auxiliary vehicles in an open-pit mine that need to traverse the entire mine through many working points. Unlike previous studies which usually used the Dijkstra's algorithm, this study utilized a heuristic algorithm for the Traveling Salesman Problem(TSP). Thus, the optimal routes of auxiliary vehicles could be determined by considering the visiting order of multiple working points. A case study at the Pasir open-pit coal mine, Indonesia was conducted to analyze the travel route of an auxiliary vehicle that monitors the working condition by traversing the entire mine without stopping. As a result, we could know that the heuristic TSP algorithm is more efficient than intuitive judgment in determining the optimal travel route; 20 minutes can be shortened when the auxiliary vehicle traverses the entire mine through 25 working points according to the route determined by the heuristic TSP algorithm. It is expected that the results of this study can be utilized as a basis to set the direction of future research for the system optimization of auxiliary vehicles in open-pit mines.