• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.9-11
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• 1999

조합 최적화 문제인 Traveling Salesman problems(TSP)을 Genetic Algorithm(GA)과 Local Search Heuristic인 Lin-Kernighan(LK) Heuristic[2]을 이용하여 접근하는 것은 최적해를 구하기 위해 널리 알려진 방법이다. 이 논문에서는 LK를 이용하여 주어진 TSP 문제에서 Local Optima를 찾고, GA를 이용하여 Local Optimal를 바탕으로 Global Optima를 찾는데 이용하게 된다. 여기서 이런 GA와 LK를 이용하여 TSP 문제를 풀 경우 해가 점점 수렴해가면서 중복된 유전자가 많이 생성된다. 이런 중복된 유전자를 제거함으로써 탐색의 범위를 보다 넓고 다양하게 검색하고, 더욱 효율적으로 최적화를 찾아내는 방법에 대해서 논하겠다. 이런 방법을 이용하여 rat195, gil262, lin318의 TSP문제에서 효율적으로 수행된다.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.2
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• pp.120-129
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• 2004

This paper provides a simple but effective method, cost relaxation to escape from a local optimum of the traveling salesman problem. We would find a better solution if we repeat a local search heuristic at a different initial solution. To find a different initial solution, we use the cost relaxation method relaxing the cost of arcs. We used the Lin-Kernighan algorithm as a local search heuristic. In experimental result, we tested large instances, 30 random instances and 34 real world instances. In real-world instances, we found average 0.17% better above the optimum solution than the Concorde known as the chained Lin-Kernighan. In clustered random instances, we found average 0.9% better above the optimum solution than the Concorde.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.321-324
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• 2000

조합 최적화 문제인 Traveling Salesman problems(TSP)을 Genetic Algorithm(GA)[3]과 Local Search Heuristic Algorithm[8]을 이용하여 접근하는 것은 최적해를 구하기 위해 널리 알려진 방법이다. 본 논문에서는 TSP문제를 해결하기 위한 또 다른 접근법으로, 다수의 Ant들이 Tour들을 찾는 ACS(Ant Colony System) Algorithms[4][6][7]을 소개하고, ACS에서 Global Optima를 찾는 과정에서, 이미 이루어져 있는 Ant들의 Tour결과들을 서로 비교한다. Global Updating Rule에 의해 Global Best Tour 에 속해 있는 각 Ant Tour의 edge들을 update하는 ACS Algorithm에, 각 루프마다 Ant Tour들을 우성과 열성 인자들로 구분하고, 각각의 우성과 열성 인자들에 대해서 Global Updating Rule에 기반한 가중치를 적용(Weight Updating Rule)하므로서 기존의 ACS Algorithm보다 효율적으로 최적 해를 찾아내는 방법에 대해서 논하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.622-627
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• 2006

TSP(Traveling Salesman Problem) is a problem finding out the shortest distance out of many courses where given cities of the number of N, one starts a certain city and turns back to a starting city, visiting every city only once. As the number of cities having visited increases, the calculation rate increases geometrically. This problem makes TSP classified in NP-Hard Problem and genetic algorithm is used representatively. To obtain a better result in TSP, various operators have been developed and studied. This paper suggests new method of population initialization and of sequential transformation, and then proves the improvement of capability by comparing them with existing methods.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.744-746
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• 1998

외판원(Traveling Salesman Problem)는 계산 복잡도가 매우 높으므로 이를 해결하려는 다양한 방법들이 제시되어 왔다. 최근에는 특히 휴리스틱(Heuristic) 에 기반한 유전 알고리즘(Genetic Algorithm)에 위한 방법이 관심을 집중시키고 있고, 이를 위한 다양한 교잡(Crossiver)연산자와 돌연변이(Mutation) 연산자들이 발표되고 있다. 돌연변이연산자는 지역해에 빠지는 것을 방지하며, 유용한 유전 특성을 잃어버릴 위험이 있는 교잡 연산자의 단점을 보완할 수 있다. 본 논문에서는 새로운 돌연변이 연산자를 개발하여 적용한 유전 알고리즘으로 외판원 문제를 해결한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.652-656
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• 2002

본 연구에서는 각 수요지간의 시간이 확률적으로 주어지는 경우의 TSP(Traveling Salesman Problem)를 다루고자 한다. 현실적으로, 도심의 교통 체증 등으로 인해서 각 지점간의 걸리는 시간은 시간대별로 요일별로 심한 변화를 일으키기 마련이다. 그러나, 현재까지의 연구 결과는 수요지간의 경과시간이 확정적으로 주어지는 경우가 대부분으로, 도심물류 등에서 나타나는 현실적인 문제를 해결하는데는 많은 한계가 있다 본 연구에서는 문제의 해법으로 강화학습기법의 하나인 Q학습(Q-Learning)과 Neural Network를 활용한 효율적인 알고리즘을 제시한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.111-118
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• 1995

This paper suggests a hybrid genetic algorithm for asymmetric traveling salesman problem(TSP). The TSP was proved to be NP-complete, so it is difficult to find optimal solution in reasonable time. Therefore it is important to develope an algorithm satisfying robustness. The algorithm applies dynamic programming to find initial solution. The genetic operator is uniform order crossover and scramble sublist mutation. And experiment of parameterization has been performed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.75-79
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• 1998

It is well known that traveling salesman problem (for short, TSP) is one of mot important problems for optimization, and almost all optimization problems result in TSP. This paper describes on an effective solution of TSP using genetic algorithm. The features of our method are summarized as follows : (1) By using division and unification method, a large problem is replaced with some small ones. (2) Smoothing method proposed in this paper enables us to obtain a fine approximate solution globally. Accordingly, demerits caused by division and unification method are decreased. (3) Parallel operation is available because all divided problems are independent of each other.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.1-7
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• 2019

It is one of the known methods to obtain the optimal solution using the Ant Colony Optimization Algorithm for the Traveling Salesman Problem (TSP), which is a combination optimization problem. In this paper, we solve the TSP problem by proposing an improved new ant colony optimization algorithm that combines genetic algorithm mutations in existing ant colony optimization algorithms to solve TSP problems in many cities. The new ant colony optimization algorithm provides the opportunity to move easily fall on the issue of developing local optimum values of the existing ant colony optimization algorithm to global optimum value through a new path through mutation. The new path will update the pheromone through an ant colony optimization algorithm. The renewed new pheromone serves to derive the global optimal value from what could have fallen to the local optimal value. Experimental results show that the existing algorithms and the new algorithms are superior to those of existing algorithms in the search for optimum values of newly improved algorithms.

• Spatial Information Research
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• v.11 no.3
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• pp.275-289
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• 2003

This study focused on offering a helpful information for the touring. To develop a tour route guidance tool, it is needed to use a GIS function, which can suggest a possible shortest path based on time and distance. A Traveling salesman problem algorithm, which was developed to solve multi destination problem in network analysis, was used to implement a tour guidance system. This system was developed using ArcObjects programming components within ArcGIS 8.3. Jeju city was selected for the case study to apply the tool and to test the effectiveness of it. This study demonstrated that this tool was considerably effective for finding a probable optimum tour path in the following aspects. First, it can help tourists to select several attractions from numerous tour sites in an area given limited time. Second, tourist can manage time efficiently by organizing their tour courses on the basis of the tool. Third, tourists can reduce the uncertainties that may happen under unexpected situations in unfamiliar places by obtaining specific spatial information using the tool. Fourth, the Graphic User Interface of the tool can be easily used to obtain visual information of spatial data.