• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.13-27
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• 2012

Integer programming-based local search(IPbLS) is a kind of local search based on simple hill-climbing search and adopts integer programming for neighbor generation unlike general local search. According to an existing research [1], IPbLS is known as an effective method for the multidimensional knapsack problem(MKP) which has received wide attention in operations research and artificial intelligence area. However, the existing research has a shortcoming that it verified the superiority of IPbLS targeting only largest-scale problems among MKP test problems in the OR-Library. In this paper, I verify the superiority of IPbLS more objectively by applying it to other problems. In addition, unlike the existing IPbLS that combines simple hill-climbing search and integer programming, I propose methods combining other local search algorithms like hill-climbing search, tabu search, simulated annealing with integer programming. Through the experimental results, I confirmed that IPbLS shows comparable or better performance than the best known heuristic search also for mid or small-scale MKP test problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.650-656
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• 1999

In this paper, we study on the convergence of the evolution strategies by introducing the Lamarckian evolution and the Baldwin effect, and propose a random local searching and a reinforcement local searching methods. In the random local searching method some neighbors generated randomly from each individual are med without any other information, but in the reinforcement local searching method the previous results of the local search are reflected on the current local search. From the viewpoint of the purpose of the local search it is suitable that we try all the neighbors of the best individual and then search the neighbors of the best one of them repeatedly. Since the reinforcement local searching method based on the Lamarckian evolution and Baldwin effect does not search neighbors randomly, but searches the neighbors in the direction of the better fitness, it has advantages of fast convergence and an improvement on the global searching capability. In other words the performance of the evolution strategies is improved by introducing the learning, reinforcement local search, into the evolution. We study on the learning effect on evolution strategies by applying the proposed method to various function optimization problems.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.886-888
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• 2004

본 논문에서는 현재 프레임 블록의 인접 블록으로부터 예측된 움직임 정보를 구하여, 이를 탐색 원점으로 하여 한 방향으로 진행하는 수정된 다이아몬드 지역 레이더 패턴으로 블록정합을 수행하는 수정된 다이아몬드 지역탐색 알고리즘을 제안한다. 실험 결과 제안된 방식은 기존의 방식들에 비해 PSNR 간에 있어서 평균적으로 0.02~0.37(㏈) 개선되고 속도면에서 우수한 결과를 나타내었다. 또한 기존의 다이아몬드 지역탐색 알고리즘보다 탐색 속도면에서 14~24%의 성능향상을 보이면서 화질면에서는 거의 근접하게 나타났다. 제안된 방식은 정량적인 결과뿐만 아니라 부호화후 복호화한 영상의 화질에 있어서도 다른 고속 탐색 알고리즘보다 월등히 우수한 화질을 제공한다.

• Journal of Agricultural Extension & Community Development
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• v.5 no.1
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• pp.71-77
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• 1998

탐색회의는 조직이나 지역사회의 사람들로 하여금 그들의 가장 바람직한 미래를 규정하고 참여에의 책임감을 강조하는 과업수행을 위한 전략을 만들어낼 수 있도록 하는 참여적 계획 방법이다. 참여자들은 가능한 한 다양한 배경을 지닌 사람들이며, 그리하여 그들이 지니고 있는 지역사회에 대한 자신들의 지식과 과정을 변화시킬 수 있는 기술을 가지고 지역사회 개발에 기여할 수 있다. 탐색회의는 이루어져야만 하는 특별한 활동들을 규정하고 필요한 변화를 만들어낼 책임을 지니는 사람들에게 힘을 부여함으로써 표준적인 전략 계획보다 훨씬 더욱 유용한 결과를 산출한다. 계획과정에의 전체 지역사회의 참여는 바람직한 결과를 지역사회 전체가 소유할 개연성을 증가시키는 데, 이는 과업을 수행하는데 필수불가결하고 지역사회가 희망하는 목표를 성취하는데 중심적인 조건이다. 효과적인 지역사회 개발 계획을 위한 탐색회의의 성공적인 수행을 위해서는, 중앙정부가 지방정부의 탐색회의와 관련된 교육프로그램을 제공해야 하는데, 이는 탐색회의가 참여자들을 조직하고 관리하는 따위와 같은 얼마간의 기술을 필요로 하기 때문이다.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.39-47
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• 2010

Constraint satisfaction optimization problem is a kind of optimization problem involving cost minimization as well as complex constraints. Local search and constraint programming respectively have been used for solving such problems. In this paper, I propose a method to integrate local search and constraint programming to improve search performance. Basically, local search is used to solve the given problem. However, it is very difficult to find a feasible neighbor satisfying all the constraints when we use only local search. Therefore, I introduced constraint programming as a tool for neighbor generation. Through the experimental results using weighted N-Queens problems, I confirmed that the proposed method can significantly improve search performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.171-179
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• 2013

The primary function of navigation system is to provide route search and road guidance for safe driving for drivers. However, the existing route search system provides a simple service that looks up the shortest route using a safe driving DB without considering different road characteristics for the safety of the drivers. In order to maintain the safe driving, rather than searching the shortest path, a navigation system, in which the danger areas and/or the dangerous time zones have been considered, is required. Therefore, in this paper we propose a strategy of searching a navigation path to avoid danger areas for safe driving by using the A* algorithm. In the strategy, when evaluating the path-specific fitness of the navigation nodes, different heuristic weights were assigned to different types of risk areas. In particular, we considered three kinds of danger areas, such as accident-prone sections where accidents occur frequently, school zones, and intersection regions, as well as the time slots when the probability of danger is high. From computer simulation, the results demonstrate that the proposed scheme can provide the way to avoid danger areas on the route searching and confirm the possibility of providing the actual service.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.13-21
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• 2014

The set covering problem (SCP) is one of representative combinatorial optimization problems, which is defined as the problem of covering the m-rows by a subset of the n-columns at minimal cost. This paper proposes a method utilizing Integer Programming-based Local Search (IPbLS) to solve the set covering problem. IPbLS is a kind of local search technique in which the current solution is improved by searching neighborhood solutions. Integer programming is used to generate neighborhood solution in IPbLS. The effectiveness of the proposed algorithm has been tested on OR-Library test instances. The experimental results showed that IPbLS could search for the best known solutions in all the test instances. Especially, I confirmed that IPbLS could search for better solutions than the best known solutions in four test instances.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.11-22
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• 1997

본 연구에서는 비선형 최적화 문제를 효율적으로 해결하기 위한 혼합유전알고리즘(Hybrid Genetic Algorthm : HGA)을 개발하였다. HGA는 기존 유전알고리즘의 적용에 있어 문제점으로 지적된 정밀도의 적용문제와 벌금함수의 사용을 배제하였으며 지역적최적점으로 빠르게 수렴하는 기존의 지역적 탐색법과 유전알고리즘 적용이후 수렴된 해 주변에 대한 정밀탐색법을 함께 고려하여 설계하였으며 이를 세가지의 비선형 최적화 문제 적용하여 본 논문에서 개발한 HGA의 유효성을 보였다.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1063-1068
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• 2006

본 논문에서는 PAN에서의 빠르고 안정성 있는 서비스 탐색방법으로 그룹관리와 지역캐시를 이용한 논리적 메쉬구조 기법을 제안하고자 한다. 이 기법은 PAN 내부의 노드들을 하나의 그룹으로 관리하는데, 각 노드들은 그룹에 속한 모든 노드들의 정보를 지역캐시로 저장하고 있어 논리적인 메쉬구조를 가지게 된다. 이 지역캐시 정보는 가입과 탈퇴 방법을 통해 각 노드에게 저장되므로 탐색시 발생하는 브로드캐스팅 트래픽을 감소시킬 수 있다. 이때 논리적 메쉬구조를 사용하여 전송하므로 직접전송을 통한 빠른 전송이 가능하다. 또한 이런 논리적 메쉬구조는 특정 서비스의 탐색을 위해 중간에 다른 노드나 레지스트리 서버를 거치지 않으므로 레지스트리 의존 구조에 비해 상대적으로 안정성이 있다. 이 서비스 탐색 방법은 향후 상황정보 공유를 위한 프레임워크에 상황정보 탐색 모듈로서 반영 될 수 있을 것이며 웨어러블 컴퓨팅에도 적용 가능하다.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle's movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle's movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.