• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.1011-1012
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• 2008

본 논문은 차량 텔레매틱스 시스템에서 중요한 응용중의 하나인 다중 목적지 방문을 위한 경로 결정방식을 구현하기 위하여 Lin-Kernighan 휴리스틱을 텔레매틱스 시스템에 결합하는 방법에 대해 기술하고 다중 목적지 결정 서버를 구현한다. 서버는 클라이언트는 로드 네트워크에 대한 자료구조를 공유하고 있으며 클라이언트가 목적지 리스트를 요청하면 1) 서버가 $A^*$ 기법에 의해 각 목적지간의 비용을 계산하고 2) Lin-Kernighan 프로그램의 인자로 변환하여 3) 경로 결정 모듈을 수행시킨다. 이 경로의 순서는 클라이언트에게 정해진 메시지 포맷에 의해 전달되며 클라이언트는 각 인접한 목적지간에 $A^*$ 기법에 의해 실제 도로 네트워크 상에서의 경로를 결정하여 사용자에게 제공한다. 성능측정 결과 방문지 수가 많더라도 수초 이내에 경로를 결정할 수 있으며 그 정확성도 거의 100%에 근접한다.

• 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.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.97-102
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• 2001

In computer-aided design, partitioning is task of clustering objects into groups to that a given objection function is optimized It is used at the layout level to fin strongly connected components that can be placed together in order to minimize the layout area and propagation delay. Partitioning can also be used to cluster variables and operation into groups for scheduling and unit selection in high-level synthesis. The most popular algorithms partitioning include the Kernighan-Lin algorithm Fiduccia-Mattheyses heuristic and simulated annealing In this paper we propose a genetic algorithm searching solution space for the circuit partitioning problem. and then compare it with simulated annealing by analyzing the results of implementation.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.164-170
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• 2021

In VLSI design, partitioning is a task of clustering objects into groups so that a given objective circuit is optimized. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for partitioning include the Kernighan-Lin algorithm, Fiduccia-Mattheyses heuristic and simulated annealing. In this paper, we propose a adapted genetic algorithm searching solution space for the circuit partitioning problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of implementation. As a result, it was found that an adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.219-233
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• 1994

A fixed k-coloring problem is introduced and dealt with by efficient heuristic algorithms. It is shown that the problem can be transformed into the graph partitioning problem. Initial coloring and improving methods are proposed for problems with and with and without the size restriction. Algorithm Move, LEE and OEE are developed by modifying the Kernighan-Lin's two way uniform partitioning procedure. The use of global information in the selection of the node and the color set made the proposed algorithms superior to the existing method. The computational result also shows that the superiority does not sacrifice the time demand of the proposed algorithms.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.709-730
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• 2017

To address the challenge of unprecedented growth in mobile data traffic, ultra-dense network deployment is a cost efficient solution to offload the traffic over some small cells. The overlapped coverage areas of small cells create more than one candidate access points for one mobile user. Signal strength based user association in IEEE 802.11 results in a significantly unbalanced load distribution among access points. However, the effective bandwidth demand of each user actually differs vastly due to their different preferences for mobile applications. In this paper, we formulate a set of non-linear integer programming models for joint user association control and user demand guarantee problem. In this model, we are trying to maximize the system capacity and guarantee the effective bandwidth demand for each user by soft-association control with a software defined network controller. With the fact of NP-hard complexity of non-linear integer programming solver, we propose a Kernighan Lin Algorithm based graph-partitioning method for a large-scale network. Finally, we evaluated the performance of the proposed algorithm for the edge users with heterogeneous bandwidth demands and mobility scenarios. Simulation results show that the proposed adaptive soft-association control can achieve a better performance than the other two and improves the individual quality of user experience with a little price on system throughput.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1183-1194
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• 2004

Network partitioning problem is to partition a network into multiple blocks such that the size of cutset is minimized while keeping the block sizes balanced. Among these, iterative algorithms are regarded as simple and efficient which are based on cell move of Fiduccia and Mattheyses algorithm, Sanchis algorithm, or Kernighan and Lin algorithm. All these algorithms stipulate balanced block size as a constraint that should be satisfied, which makes a cell movement be inefficient. Park and Park introduced a balancing coefficient R by which the block size balance is considered as a part of partitioning cost, not as a constraint. However, Park and Park's algorithm has a square time complexity with respect to the number of cells. In this paper, we proposed Bucket algorithm that has a linear time complexity with respect to the number of cells, while taking advantage of the balancing coefficient. Reducing time complexity is made possible by a simple observation that balancing cost does not vary so much when a cell moves. Bucket data structure is used to maintain partitioning cost efficiently. Experimental results for MCNC test sets show that cutset size of proposed algorithm is 63.33% 92.38% of that of Sanchis algorithm while our algorithm satisfies predefined balancing constraints and acceptable execution time.