• Proceedings of the Korean Society of Disaster Information Conference
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• 2015.11a
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• pp.70-71
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• 2015

2011년3월에 발생한 동일본대지진에서는 일부지역에서 대규모 쓰나미 경보 사이렌의 고장 및 긴급시 사용하는 방조제 개폐장치의 고장 등, 상상외의 여러가지 일들이 발생하면서 그 피해가 더 커졌다. 이러한 사태를 바탕으로 대규모 지역에서의 피난 계획 및 시뮬레이션의 필요성이 최근에 대두되고 있다. 본 연구에서는 이러한 광역 피난계획을 풀기 위한 동적 네트워크 흐름 문제(dynamic network flow problem)를 적용한 방법론을 소개하고, 동적 네트워크 흐름 문제를 풀기 위한 일반적인 방법론 중 시간 확대 네트워크 문제 및 시간 확대 네트워크의 문제점인 계산시간을 해결하기 위한 고속연산 휴리스틱 알고리즘을 제시하고자 한다.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.472-482
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• 2013

This paper proposes a wireless LAN topology configuration method for enhancing indoor location-awareness and improving network performance simultaneously. We first develop four objective functions that yield objective goals significant to the optimal design of a wireless LAN topology in terms of location-awareness accuracy and network performance factors. Then, we develop metaheuristic algorithms such as simulated annealing, tabu search, and genetic algorithm that examine the proposed objective functions and generate a near-optimal solution for a given objective function. Finally, four objective functions and metaheuristic algorithms developed in this paper are exploited to evaluate and measure the performance of the proposed wireless LAN topology configuration method.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.229-237
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• 2010

Recently, forest management objectives become more complex and complicated, and spatial constraints were necessarily considered for ecological stability. Now forest planning is required to provide an optimized solution that is able to achieve a number of management objectives and constraints. In this study, we developed a decision supporting system based on the one of dynamic planning techniques, Tabu Search (TS) heuristic algorithm, which enable one to generate an optimized solution for given objectives and constraints. For this purpose, we analyzed the logical flow of the algorithm and designed the subsequence of processes. To develop a high-performance computing system, we examined a number of strategy to minimize execution time and workloads in each process and to maximize efficiency of using system resources. We examined two model based on the original TS algorithm and revised version of TS algorithm and compared their performance in optimization process. The results showed high performance of the developed system in providing feasible solutions for several management objectives and constraints. Moreover, the revised version of TS algorithm was appeared to be more stable for providing results with minimum variation. The developed system is expected to use for developing forest management plans in Korea.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.80-90
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• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.35-44
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• 2005

This paper proposes an efficient heuristic load balancing algorithm for multiprocessor systems. The algorithm minimizes the number of idle links to distribute load traffic and reduces its communication cost. Each processor iteratively tries to transfer unit load to/from all neighbor processors. However, real load transfer is collectively done after all load traffic is calculated. This prevents useless traffic and thus reduces the overall load traffic. The proposed algorithm can be employed in various interconnection topologies with slight modifications. In this paper, it is applied to hypercube, mesh, k-ary n-cube and general graph environments. For performance evaluation, simulation studies are performed. The proposed algorithm and the well-known existing algorithms are implemented and compared. The results show that the proposed algorithm always balances the loads perfectly. furthermore, in comparison with the existing algorithms, it reduces the communication costs by 77%, 74% and 73% in the hypercube, the mesh, and k-ary n-cube, respectively.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1333-1341
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• 2009

Tactical path-finding in computer games is path-finding where a path is selected by considering not only basic elements such as the shortest distance or the minimum time spend but also tactical information of surroundings when deciding character's moving trajectory. One way to include tactical information in path-finding is to represent a heuristic function as a sum of tactical quality multiplied by a weighting factor which is.. determined based on the degree of its importance. The choice of weighting factors for tactics is very important because it controls search performance and the characteristic of paths found. In this paper. we propose a method for improving a heuristic function by adjusting weights based on the difference between paths on examples given by a level designer and paths found during the search process based on the CUITent weighting factors. The proposed method includes the search algorithm modified to detect search errors and learn heuristics and the perceptron-like weight updating formular. Through simulations it is demonstrated how different paths found by tactical path-finding are from those by traditional path-finding. We analyze the factors that affect the performance of learning and show the example applied to the real game environments.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1196-1204
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• 2022

In this paper, we propose an optimization algorithm that maximizes the network lifetime in wireless ad-hoc networks using the broadcast transmission method. The optimization algorithm proposed in this paper applies tabu search algorithm, a metaheuristic method that improves the local search method using the memory structure. The proposed tabu search algorithm proposes efficient encoding and neighborhood search method to the network lifetime maximization problem. By applying the proposed method to design efficient broadcast routing, we maximize the lifetime of the entire network. The proposed tabu search algorithm was evaluated in terms of the energy consumption of all nodes in the broadcast transmission occurring in the network, the time of the first lost node, and the algorithm execution time. From the performance evaluation results under various conditions, it was confirmed that the proposed tabu search algorithm was superior to the previously proposed metaheuristic algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.778-785
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• 2020

Wireless mesh networks consist of mesh clients, mesh routers and mesh access points. The mesh router connects wireless network services to the mesh client, and the mesh access point connects to the backbone network using a wired link and provides Internet access to the mesh client. In this paper, a limited number of mesh routers and mesh access points are used to propose optimization algorithms for network design for wireless mesh networks. The optimization algorithm in this paper has been applied with a sub-subscription algorithm, which is one of the meta-heuristic methods, and is designed to minimize the transmission delay for the placement of mesh routers and mesh access points, and produce optimal results within a reasonable time. The proposed algorithm was evaluated in terms of transmission delay and time to perform the algorithm for the placement of mesh routers and mesh access points, and the performance evaluation results showed superior performance compared to the previous meta-heuristic methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.7 no.2
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• pp.73-92
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• 1997

A modular exponentiation( E$M^{$=varepsilon$}$mod N) is one of the most important operations in Public-key cryptography. However, it takes much time because the modular exponentiation deals with very large operands as 512-bit integers. Modular exponentiation is composed of repetition of modular multiplications, and the number of repetition is the same as the length of the addition-chain of the exponent(E). Therefore, we can reduce the execution time of modular exponentiation by finding shorter addition-chain(i.e. reducing the number of repetitions) or by reducing the execution time of each modular multiplication. In this paper, we propose an addition-chain heuristics and two fast modular multiplication algorithms. Of two modular multiplication algorithms, one is for modular multiplication between different integers, and the other is for modular squaring. The proposed addition-chain heuristics finds the shortest addition-chain among exisiting algorithms. Two proposed modular multiplication algorithms require single-precision multiplications fewer than 1/2 times of those required for previous algorithms. Implementing on PC, proposed algorithms reduce execution times by 30-50% compared with the Montgomery algorithm, which is the best among previous algorithms.