• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.129-138
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• 2006

Various techniques have been applied to solve the maximal covering problem. Tabu search is also one of them. But, existing researches were lacking of the synthetic analysis and the effort for performance improvement about neighborhood search techniques such as hill-climbing search and simulated annealing including tabu search. In this paper, I introduce the way to improve performance of neighborhood search techniques through various experiments and analyses. Basically, all neighborhood search algorithms use the k-exchange neighborhood generation method. And I analyzed how the performance of each algorithm changes according to various parameter settings. Experimental results have shown that simple hill-climbing search and simulated annealing can produce better results than any other techniques. And I confirmed that simple hill-climbing search can produce similar results as simulated annealing unlike general case.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.164-172
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• 2004

For a local search algorithm to find a bettor quality solution it is required to generate and evaluate a sufficiently large number of candidate solutions as neighbors at each iteration, demanding quite an amount of CPU time. This paper presents a method of selectively generating only good-looking candidate neighbors, so that the number of neighbors can be kept low to improve the efficiency of search. In our method, a newly generated candidate solution is probabilistically selected to become a neighbor based on the quality estimation determined heuristically by a very simple evaluation of the generated candidate. Experimental results on the problem of load balancing for production scheduling have shown that our candidate selection method outperforms other random or greedy selection methods in terms of solution quality given the same amount of CPU time.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.246-254
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• 2007

Local search algorithms start from a certain candidate solution and probe its neighborhood to find ones with improved quality. This paper proposes a method of probabilistically filtering out bad-looking neighbors based on a simple low-cost preliminary evaluation heuristics. The probabilistic filtering enables us to save time wasted on fully evaluating those solutions that will eventually be trashed, and thus improves the search efficiency by allowing us to spend more time on examining better looking solutions. Experiments with two large-scaled real-world problems, which are a traffic signal control problem in traffic network and a load balancing problem in production scheduling, have shown that the proposed method finds better quality solutions, given the same amount of CPU time.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.1-8
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• 2008

With growing interest in location-based service (LBS), there is increasing necessity for nearest neighbor (NN) search through query while the user is moving. NN search in such a dynamic environment has been performed through the repeated applicaton of the NN method to the search segment, but this increases search cost because of unnecessary redundant calculation. We propose slabbed continuous nearest neighbor (Slabbed_CNN) search, which is a new method that searches CNN in the search segment while moving, Slabbed_CNN reduces calculation costs and provides faster services than existing CNN by reducing the search area and calculation cost of the existing CNN method through reducing the search segment using slabs.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.829-839
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• 2004

Methods based on integer programming have been shown to be very effective in solving various crew pairing optimization problems. However, their applicability is limited to problems with linear constraints and objective functions. Also, those methods often require an unacceptable amount of time and/or memory resources given problems of larger scale. Heuristic methods such as neighborhood search, on the other hand, can handle large-scaled problems without too much difficulty and can be applied to problems having any form of objective functions and constraints. However, neighborhood search often gets stuck at local optima when faced with complex search spaces. This paper presents ,i hybrid algorithm of neighborhood search and integer programming, which nicely combines the advantages of both methods. The hybrid algorithm has been successfully tested on a large-scaled crew pairing optimization problem for a real subway line.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.27-35
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• 2009

Integer programming is a very effective technique for searching optimal solution of combinatorial optimization problems. However, its applicability is limited to linear models. In this paper, I propose an effective method for solving a nonlinear optimization problem by integrating the powerful search performance of integer programming and the flexibility of neighborhood search algorithms. In the first phase, integer programming is executed with subproblem which can be represented as a linear form from the given problem. In the second phase, a neighborhood search algorithm is executed with the whole problem by taking the result of the first phase as the initial solution. Through the experimental results using a nonlinear maximal covering problem, I confirmed that such a simple integration method can produce far better solutions than a neighborhood search algorithm alone. It is estimated that the success is primarily due to the powerful performance of integer programming.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.9
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• pp.561-569
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• 2014

In wireless ad hoc networks, neighbor discovery is essential in the network initialization and the design of routing, topology control, and medium access control algorithms. Therefore, efficient neighbor discovery algorithms should be devised for self-organization in wireless ad hoc networks. In this paper, we propose a probabilistic neighbor discovery (PND) algorithm, which aims at reducing the neighbor discovery time by adjusting the transmission probability of advertisement messages through the multiplicative-increase/multiplicative-decrease (MIMD) policy. To further improve PND, we consider the collision detection (CD) capability in which a device can distinguish between successful reception and collision of advertisement messages. Simulation results show that the transmission probabilities of PND and PND with CD converge on the optimal value quickly although the number of devices is unknown. As a result, PND and PND with CD can reduce the neighbor discovery time by 15.6% to 57.0% compared with the ALOHA-like neighbor discovery algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.420-423
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• 2020

스카이라인 질의는 객체의 속성을 기준으로 사용자의 선호에 적합한 대상을 탐색하는 기법이다. 기존 스카이라인 질의는 일괄처리 방식으로 탐색 결과를 반환하지만 대화형 앱이나 모바일 환경과 같이 잦은 위치이동 발생 시 일괄처리 방식으로 스카이라인 질의 결과를 신속하게 받기 어렵다. 최근접 이웃(Nearest Neighbor) 알고리즘은 사용자와 상호 작용이 필요한 대화형 앱에서 실시간으로 선호 객체를 탐색하여 사용자에게 전달함으로써 객체의 반환 속도를 향상시켰다. 그러나 최근접 이웃 알고리즘은 객체 탐색 과정에서 반복적인 비교 연산을 수행하여 불필요한 탐색 시간이 소요된다. 본 논문은 대화형 앱에서 신속한 스카이라인 결과를 산출하고자 연산 대상 객체의 범위를 축소함으로써 최근접 이웃 스카이라인 질의 알고리즘의 성능을 향상시킨 전처리 기법을 제안한다. 데이터 객체는 최대 40,000 개의 실험에서 제안 기법은 최근접 이웃 알고리즘보다 50% 빠른 성능을 나타내어 본 연구의 가용성이 증명되었다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.27-35
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• 2021

Local search has been used to solve various combinatorial optimization problems. One of the most important factors in local search is the method of generating a neighbor solution. In this paper, we propose neighbor generation strategies of local search for permutation-based combinatorial optimization, and compare the performance of each strategies targeting the traveling salesman problem. In this paper, we propose a total of 10 neighbor generation strategies. Basically, we propose 4 new strategies such as Rotation in addition to the 4 strategies such as Swap which have been widely used in the past. In addition, there are Combined1 and Combined2, which are made by combining basic neighbor generation strategies. The experiment was performed by applying the basic local search, but changing only the neighbor generation strategy. As a result of the experiment, it was confirmed that the performance difference is large according to the neighbor generation strategy, and also confirmed that the performance of Combined2 is the best. In addition, it was confirmed that Combined2 shows better performance than the existing local search methods.

• Journal of KIISE:Information Networking
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• v.37 no.1
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• pp.1-7
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• 2010

Network Centric Warfare(NCW) is becoming a prominent concept in the current trend of warfare. To support high quality communication between strategic/tactical units in the concept of NCW, Tactical Airborne Networks are likely to be constructed in the near future to take part in the NCW. In these Tactical Airborne Networks with dynamic topology variations due to very high mobility of participants nodes, more efficient and reliable neighbor discovery protocols are needed. This paper presents the adaptive HELLO message scheduling algorithm for Tactical Airborne Network using directional antennas. The purposed algorithm can reduce the overhead of periodic HELLO message transfer, while guaranteeing successful data transmission. We concluded a mathematical analysis and simulation studies using Qualnet 4.5 for evaluation the performance and efficiency of the proposed scheme.